code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
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"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
A = logging.get_logger(__name__)
A = Dict[str, Any]
A = List[Prediction]
@add_end_docstrings(__magic_name__ )
class a__ ( __magic_name__ ):
def __init__( self : Optional[int] , *UpperCamelCase_ : Optional[int] , **UpperCamelCase_ : Any):
"""simple docstring"""
super().__init__(*UpperCamelCase_ , **UpperCamelCase_)
if self.framework == "tf":
raise ValueError(F"The {self.__class__} is only available in PyTorch.")
requires_backends(self , "vision")
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))
def a_ ( self : Dict , **UpperCamelCase_ : Tuple):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = {}
if "threshold" in kwargs:
__UpperCAmelCase : Dict = kwargs["threshold"]
return {}, {}, postprocess_kwargs
def __call__( self : str , *UpperCamelCase_ : Optional[int] , **UpperCamelCase_ : Tuple):
"""simple docstring"""
return super().__call__(*UpperCamelCase_ , **UpperCamelCase_)
def a_ ( self : int , UpperCamelCase_ : Dict):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = load_image(UpperCamelCase_)
__UpperCAmelCase : Optional[int] = torch.IntTensor([[image.height, image.width]])
__UpperCAmelCase : str = self.image_processor(images=[image] , return_tensors="pt")
if self.tokenizer is not None:
__UpperCAmelCase : Any = self.tokenizer(text=inputs["words"] , boxes=inputs["boxes"] , return_tensors="pt")
__UpperCAmelCase : List[Any] = target_size
return inputs
def a_ ( self : Tuple , UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Dict = model_inputs.pop("target_size")
__UpperCAmelCase : Tuple = self.model(**UpperCamelCase_)
__UpperCAmelCase : int = outputs.__class__({"target_size": target_size, **outputs})
if self.tokenizer is not None:
__UpperCAmelCase : str = model_inputs["bbox"]
return model_outputs
def a_ ( self : int , UpperCamelCase_ : Tuple , UpperCamelCase_ : Tuple=0.9):
"""simple docstring"""
__UpperCAmelCase : Dict = model_outputs["target_size"]
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
__UpperCAmelCase , __UpperCAmelCase : str = target_size[0].tolist()
def unnormalize(UpperCamelCase_ : List[str]):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1000),
(height * bbox[1] / 1000),
(width * bbox[2] / 1000),
(height * bbox[3] / 1000),
]))
__UpperCAmelCase , __UpperCAmelCase : int = model_outputs["logits"].squeeze(0).softmax(dim=-1).max(dim=-1)
__UpperCAmelCase : List[str] = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
__UpperCAmelCase : Optional[Any] = [unnormalize(UpperCamelCase_) for bbox in model_outputs["bbox"].squeeze(0)]
__UpperCAmelCase : Union[str, Any] = ["score", "label", "box"]
__UpperCAmelCase : Dict = [dict(zip(UpperCamelCase_ , UpperCamelCase_)) for vals in zip(scores.tolist() , UpperCamelCase_ , UpperCamelCase_) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
__UpperCAmelCase : List[str] = self.image_processor.post_process_object_detection(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Optional[int] = raw_annotations[0]
__UpperCAmelCase : Optional[int] = raw_annotation["scores"]
__UpperCAmelCase : Dict = raw_annotation["labels"]
__UpperCAmelCase : Tuple = raw_annotation["boxes"]
__UpperCAmelCase : List[Any] = scores.tolist()
__UpperCAmelCase : Any = [self.model.config.idalabel[label.item()] for label in labels]
__UpperCAmelCase : Tuple = [self._get_bounding_box(UpperCamelCase_) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
__UpperCAmelCase : Union[str, Any] = ["score", "label", "box"]
__UpperCAmelCase : Optional[int] = [
dict(zip(UpperCamelCase_ , UpperCamelCase_))
for vals in zip(raw_annotation["scores"] , raw_annotation["labels"] , raw_annotation["boxes"])
]
return annotation
def a_ ( self : int , UpperCamelCase_ : "torch.Tensor"):
"""simple docstring"""
if self.framework != "pt":
raise ValueError("The ObjectDetectionPipeline is only available in PyTorch.")
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : List[str] = box.int().tolist()
__UpperCAmelCase : Dict = {
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| 77 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self : Optional[Any] , UpperCamelCase_ : int , UpperCamelCase_ : Any=13 , UpperCamelCase_ : Optional[int]=3 , UpperCamelCase_ : int=224 , UpperCamelCase_ : int=30 , UpperCamelCase_ : str=400 , UpperCamelCase_ : List[Any]=True , UpperCamelCase_ : Optional[Any]=None , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Optional[int]=[0.5, 0.5, 0.5] , UpperCamelCase_ : Optional[Any]=[0.5, 0.5, 0.5] , ):
"""simple docstring"""
__UpperCAmelCase : Tuple = size if size is not None else {"height": 18, "width": 18}
__UpperCAmelCase : List[Any] = parent
__UpperCAmelCase : Tuple = batch_size
__UpperCAmelCase : Tuple = num_channels
__UpperCAmelCase : List[Any] = image_size
__UpperCAmelCase : str = min_resolution
__UpperCAmelCase : Tuple = max_resolution
__UpperCAmelCase : Optional[Any] = do_resize
__UpperCAmelCase : Any = size
__UpperCAmelCase : Any = do_normalize
__UpperCAmelCase : Any = image_mean
__UpperCAmelCase : Optional[Any] = image_std
def a_ ( self : str):
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = ViTImageProcessor if is_vision_available() else None
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = EfficientFormerImageProcessorTester(self)
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
return self.image_proc_tester.prepare_image_processor_dict()
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(UpperCamelCase_ , "image_mean"))
self.assertTrue(hasattr(UpperCamelCase_ , "image_std"))
self.assertTrue(hasattr(UpperCamelCase_ , "do_normalize"))
self.assertTrue(hasattr(UpperCamelCase_ , "do_resize"))
self.assertTrue(hasattr(UpperCamelCase_ , "size"))
def a_ ( self : Dict):
"""simple docstring"""
pass
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
__UpperCAmelCase : str = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , Image.Image)
# Test not batched input
__UpperCAmelCase : Optional[int] = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Optional[int] = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : int = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
__UpperCAmelCase : Union[str, Any] = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_ , numpify=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , np.ndarray)
# Test not batched input
__UpperCAmelCase : Tuple = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Any = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
__UpperCAmelCase : Any = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_ , torchify=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , torch.Tensor)
# Test not batched input
__UpperCAmelCase : Optional[Any] = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Optional[int] = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
| 77 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
A = logging.get_logger(__name__)
A = [
("""bert.bert""", """visual_bert"""),
("""bert.cls""", """cls"""),
("""bert.classifier""", """cls"""),
("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
("""position_embeddings_visual""", """visual_position_embeddings"""),
("""projection""", """visual_projection"""),
]
A = [
"""nlvr2_coco_pre_trained.th""",
"""nlvr2_fine_tuned.th""",
"""nlvr2_pre_trained.th""",
"""vcr_coco_pre_train.th""",
"""vcr_fine_tune.th""",
"""vcr_pre_train.th""",
"""vqa_coco_pre_trained.th""",
"""vqa_fine_tuned.th""",
"""vqa_pre_trained.th""",
]
def _UpperCamelCase ( UpperCamelCase ) -> List[Any]:
"""simple docstring"""
__UpperCAmelCase : str = torch.load(UpperCamelCase , map_location="cpu" )
return sd
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase=rename_keys_prefix ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = OrderedDict()
__UpperCAmelCase : Union[str, Any] = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
__UpperCAmelCase : Optional[int] = key
for name_pair in rename_keys_prefix:
__UpperCAmelCase : List[Any] = new_key.replace(name_pair[0] , name_pair[1] )
__UpperCAmelCase : Dict = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
__UpperCAmelCase : Optional[Any] = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), f"The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."
# Get Config
if "pre" in checkpoint_path:
__UpperCAmelCase : str = "pretraining"
if "vcr" in checkpoint_path:
__UpperCAmelCase : Optional[Any] = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
__UpperCAmelCase : Union[str, Any] = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
__UpperCAmelCase : Union[str, Any] = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
__UpperCAmelCase : List[Any] = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(f"No implementation found for `{checkpoint_path}`." )
else:
if "vcr" in checkpoint_path:
__UpperCAmelCase : Union[str, Any] = {"visual_embedding_dim": 512}
__UpperCAmelCase : Union[str, Any] = "multichoice"
elif "vqa_advanced" in checkpoint_path:
__UpperCAmelCase : Union[str, Any] = {"visual_embedding_dim": 2048}
__UpperCAmelCase : Any = "vqa_advanced"
elif "vqa" in checkpoint_path:
__UpperCAmelCase : str = {"visual_embedding_dim": 2048, "num_labels": 3129}
__UpperCAmelCase : List[str] = "vqa"
elif "nlvr" in checkpoint_path:
__UpperCAmelCase : Any = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
__UpperCAmelCase : List[Any] = "nlvr"
__UpperCAmelCase : Optional[int] = VisualBertConfig(**UpperCamelCase )
# Load State Dict
__UpperCAmelCase : Dict = load_state_dict(UpperCamelCase )
__UpperCAmelCase : List[Any] = get_new_dict(UpperCamelCase , UpperCamelCase )
if model_type == "pretraining":
__UpperCAmelCase : Optional[int] = VisualBertForPreTraining(UpperCamelCase )
elif model_type == "vqa":
__UpperCAmelCase : str = VisualBertForQuestionAnswering(UpperCamelCase )
elif model_type == "nlvr":
__UpperCAmelCase : List[Any] = VisualBertForVisualReasoning(UpperCamelCase )
elif model_type == "multichoice":
__UpperCAmelCase : Tuple = VisualBertForMultipleChoice(UpperCamelCase )
model.load_state_dict(UpperCamelCase )
# Save Checkpoints
Path(UpperCamelCase ).mkdir(exist_ok=UpperCamelCase )
model.save_pretrained(UpperCamelCase )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
A = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 77 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": from_to(0.76455, 1.30795),
"""cubicfoot""": from_to(0.028, 35.3147),
"""cup""": from_to(0.000236588, 4226.75),
}
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> float:
"""simple docstring"""
if from_type not in METRIC_CONVERSION:
raise ValueError(
f"Invalid 'from_type' value: {from_type!r} Supported values are:\n"
+ ", ".join(UpperCamelCase ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
f"Invalid 'to_type' value: {to_type!r}. Supported values are:\n"
+ ", ".join(UpperCamelCase ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 77 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class a__ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase_ = StableDiffusionPanoramaPipeline
lowercase_ = TEXT_TO_IMAGE_PARAMS
lowercase_ = TEXT_TO_IMAGE_BATCH_PARAMS
lowercase_ = TEXT_TO_IMAGE_IMAGE_PARAMS
lowercase_ = TEXT_TO_IMAGE_IMAGE_PARAMS
def a_ ( self : int):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
__UpperCAmelCase : List[str] = DDIMScheduler()
torch.manual_seed(0)
__UpperCAmelCase : Dict = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0)
__UpperCAmelCase : Any = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__UpperCAmelCase : Optional[int] = CLIPTextModel(UpperCamelCase_)
__UpperCAmelCase : List[str] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
__UpperCAmelCase : List[str] = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def a_ ( self : List[str] , UpperCamelCase_ : str , UpperCamelCase_ : List[Any]=0):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = torch.manual_seed(UpperCamelCase_)
__UpperCAmelCase : Dict = {
"prompt": "a photo of the dolomites",
"generator": generator,
# Setting height and width to None to prevent OOMs on CPU.
"height": None,
"width": None,
"num_inference_steps": 1,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = "cpu" # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase : Tuple = self.get_dummy_components()
__UpperCAmelCase : Union[str, Any] = StableDiffusionPanoramaPipeline(**UpperCamelCase_)
__UpperCAmelCase : Optional[int] = sd_pipe.to(UpperCamelCase_)
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Tuple = self.get_dummy_inputs(UpperCamelCase_)
__UpperCAmelCase : Optional[int] = sd_pipe(**UpperCamelCase_).images
__UpperCAmelCase : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__UpperCAmelCase : Any = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def a_ ( self : List[Any]):
"""simple docstring"""
super().test_inference_batch_consistent(batch_sizes=[1, 2])
def a_ ( self : Optional[int]):
"""simple docstring"""
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.25e-3)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase : Optional[Any] = self.get_dummy_components()
__UpperCAmelCase : Any = StableDiffusionPanoramaPipeline(**UpperCamelCase_)
__UpperCAmelCase : Dict = sd_pipe.to(UpperCamelCase_)
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Any = self.get_dummy_inputs(UpperCamelCase_)
__UpperCAmelCase : List[str] = "french fries"
__UpperCAmelCase : Any = sd_pipe(**UpperCamelCase_ , negative_prompt=UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = output.images
__UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__UpperCAmelCase : str = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Tuple = "cpu" # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase : Optional[Any] = self.get_dummy_components()
__UpperCAmelCase : Dict = StableDiffusionPanoramaPipeline(**UpperCamelCase_)
__UpperCAmelCase : List[Any] = sd_pipe.to(UpperCamelCase_)
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = self.get_dummy_inputs(UpperCamelCase_)
__UpperCAmelCase : int = sd_pipe(**UpperCamelCase_ , view_batch_size=2)
__UpperCAmelCase : Any = output.images
__UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__UpperCAmelCase : Optional[Any] = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Any = "cpu" # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase : Union[str, Any] = self.get_dummy_components()
__UpperCAmelCase : Optional[Any] = EulerAncestralDiscreteScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear")
__UpperCAmelCase : Union[str, Any] = StableDiffusionPanoramaPipeline(**UpperCamelCase_)
__UpperCAmelCase : List[str] = sd_pipe.to(UpperCamelCase_)
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = self.get_dummy_inputs(UpperCamelCase_)
__UpperCAmelCase : str = sd_pipe(**UpperCamelCase_).images
__UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__UpperCAmelCase : int = np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : List[Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase : int = self.get_dummy_components()
__UpperCAmelCase : List[Any] = PNDMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , skip_prk_steps=UpperCamelCase_)
__UpperCAmelCase : Tuple = StableDiffusionPanoramaPipeline(**UpperCamelCase_)
__UpperCAmelCase : str = sd_pipe.to(UpperCamelCase_)
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = self.get_dummy_inputs(UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = sd_pipe(**UpperCamelCase_).images
__UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__UpperCAmelCase : Any = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def a_ ( self : List[Any]):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self : Optional[Any] , UpperCamelCase_ : Optional[int]=0):
"""simple docstring"""
__UpperCAmelCase : List[str] = torch.manual_seed(UpperCamelCase_)
__UpperCAmelCase : List[Any] = {
"prompt": "a photo of the dolomites",
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = "stabilityai/stable-diffusion-2-base"
__UpperCAmelCase : Dict = DDIMScheduler.from_pretrained(UpperCamelCase_ , subfolder="scheduler")
__UpperCAmelCase : List[Any] = StableDiffusionPanoramaPipeline.from_pretrained(UpperCamelCase_ , scheduler=UpperCamelCase_ , safety_checker=UpperCamelCase_)
pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
pipe.enable_attention_slicing()
__UpperCAmelCase : Optional[Any] = self.get_inputs()
__UpperCAmelCase : Optional[int] = pipe(**UpperCamelCase_).images
__UpperCAmelCase : int = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
__UpperCAmelCase : Tuple = np.array(
[
0.36968392,
0.27025372,
0.32446766,
0.28379387,
0.36363274,
0.30733347,
0.27100027,
0.27054125,
0.25536096,
])
assert np.abs(expected_slice - image_slice).max() < 1e-2
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Dict = StableDiffusionPanoramaPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base" , safety_checker=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
pipe.enable_attention_slicing()
__UpperCAmelCase : Dict = self.get_inputs()
__UpperCAmelCase : Optional[int] = pipe(**UpperCamelCase_).images
__UpperCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
__UpperCAmelCase : Optional[int] = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : List[Any] = 0
def callback_fn(UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : torch.FloatTensor) -> None:
__UpperCAmelCase : Any = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
__UpperCAmelCase : List[Any] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
__UpperCAmelCase : Dict = latents[0, -3:, -3:, -1]
__UpperCAmelCase : Union[str, Any] = np.array(
[
0.18681869,
0.33907816,
0.5361276,
0.14432865,
-0.02856611,
-0.73941123,
0.23397987,
0.47322682,
-0.37823164,
])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 2:
__UpperCAmelCase : Tuple = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
__UpperCAmelCase : Any = latents[0, -3:, -3:, -1]
__UpperCAmelCase : Optional[int] = np.array(
[
0.18539645,
0.33987248,
0.5378559,
0.14437142,
-0.02455261,
-0.7338317,
0.23990755,
0.47356272,
-0.3786505,
])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
__UpperCAmelCase : Dict = False
__UpperCAmelCase : Any = "stabilityai/stable-diffusion-2-base"
__UpperCAmelCase : str = DDIMScheduler.from_pretrained(UpperCamelCase_ , subfolder="scheduler")
__UpperCAmelCase : Optional[int] = StableDiffusionPanoramaPipeline.from_pretrained(UpperCamelCase_ , scheduler=UpperCamelCase_ , safety_checker=UpperCamelCase_)
__UpperCAmelCase : Any = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
pipe.enable_attention_slicing()
__UpperCAmelCase : str = self.get_inputs()
pipe(**UpperCamelCase_ , callback=UpperCamelCase_ , callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == 3
def a_ ( self : str):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__UpperCAmelCase : Optional[int] = "stabilityai/stable-diffusion-2-base"
__UpperCAmelCase : Any = DDIMScheduler.from_pretrained(UpperCamelCase_ , subfolder="scheduler")
__UpperCAmelCase : List[str] = StableDiffusionPanoramaPipeline.from_pretrained(UpperCamelCase_ , scheduler=UpperCamelCase_ , safety_checker=UpperCamelCase_)
__UpperCAmelCase : Tuple = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
__UpperCAmelCase : int = self.get_inputs()
__UpperCAmelCase : Optional[Any] = pipe(**UpperCamelCase_)
__UpperCAmelCase : str = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 77 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = ShapEPipeline
lowercase_ = ["prompt"]
lowercase_ = ["prompt"]
lowercase_ = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
lowercase_ = False
@property
def a_ ( self : Optional[int]):
"""simple docstring"""
return 32
@property
def a_ ( self : Any):
"""simple docstring"""
return 32
@property
def a_ ( self : int):
"""simple docstring"""
return self.time_input_dim * 4
@property
def a_ ( self : List[Any]):
"""simple docstring"""
return 8
@property
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def a_ ( self : List[str]):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(UpperCamelCase_)
@property
def a_ ( self : Any):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Union[str, Any] = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
__UpperCAmelCase : Dict = PriorTransformer(**UpperCamelCase_)
return model
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Tuple = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
__UpperCAmelCase : List[Any] = ShapERenderer(**UpperCamelCase_)
return model
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.dummy_prior
__UpperCAmelCase : str = self.dummy_text_encoder
__UpperCAmelCase : int = self.dummy_tokenizer
__UpperCAmelCase : int = self.dummy_renderer
__UpperCAmelCase : Tuple = HeunDiscreteScheduler(
beta_schedule="exp" , num_train_timesteps=1024 , prediction_type="sample" , use_karras_sigmas=UpperCamelCase_ , clip_sample=UpperCamelCase_ , clip_sample_range=1.0 , )
__UpperCAmelCase : str = {
"prior": prior,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"renderer": renderer,
"scheduler": scheduler,
}
return components
def a_ ( self : Optional[int] , UpperCamelCase_ : Dict , UpperCamelCase_ : Any=0):
"""simple docstring"""
if str(UpperCamelCase_).startswith("mps"):
__UpperCAmelCase : List[Any] = torch.manual_seed(UpperCamelCase_)
else:
__UpperCAmelCase : str = torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)
__UpperCAmelCase : List[Any] = {
"prompt": "horse",
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "np",
}
return inputs
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : str = "cpu"
__UpperCAmelCase : Union[str, Any] = self.get_dummy_components()
__UpperCAmelCase : Union[str, Any] = self.pipeline_class(**UpperCamelCase_)
__UpperCAmelCase : Any = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = pipe(**self.get_dummy_inputs(UpperCamelCase_))
__UpperCAmelCase : Union[str, Any] = output.images[0]
__UpperCAmelCase : str = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__UpperCAmelCase : Union[str, Any] = np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def a_ ( self : Tuple):
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2])
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = torch_device == "cpu"
__UpperCAmelCase : Optional[Any] = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=UpperCamelCase_ , relax_max_difference=UpperCamelCase_ , )
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.get_dummy_components()
__UpperCAmelCase : List[str] = self.pipeline_class(**UpperCamelCase_)
__UpperCAmelCase : int = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = 1
__UpperCAmelCase : Any = 2
__UpperCAmelCase : Optional[Any] = self.get_dummy_inputs(UpperCamelCase_)
for key in inputs.keys():
if key in self.batch_params:
__UpperCAmelCase : List[Any] = batch_size * [inputs[key]]
__UpperCAmelCase : List[Any] = pipe(**UpperCamelCase_ , num_images_per_prompt=UpperCamelCase_)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def a_ ( self : List[str]):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Dict = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_np_out.npy")
__UpperCAmelCase : Optional[Any] = ShapEPipeline.from_pretrained("openai/shap-e")
__UpperCAmelCase : Any = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Dict = torch.Generator(device=UpperCamelCase_).manual_seed(0)
__UpperCAmelCase : int = pipe(
"a shark" , generator=UpperCamelCase_ , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type="np" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(UpperCamelCase_ , UpperCamelCase_)
| 77 | 1 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class a__ ( unittest.TestCase ):
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = get_activation("swish")
self.assertIsInstance(UpperCamelCase_ , nn.SiLU)
self.assertEqual(act(torch.tensor(-100 , dtype=torch.floataa)).item() , 0)
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa)).item() , 20)
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Tuple = get_activation("silu")
self.assertIsInstance(UpperCamelCase_ , nn.SiLU)
self.assertEqual(act(torch.tensor(-100 , dtype=torch.floataa)).item() , 0)
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa)).item() , 20)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = get_activation("mish")
self.assertIsInstance(UpperCamelCase_ , nn.Mish)
self.assertEqual(act(torch.tensor(-200 , dtype=torch.floataa)).item() , 0)
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa)).item() , 20)
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = get_activation("gelu")
self.assertIsInstance(UpperCamelCase_ , nn.GELU)
self.assertEqual(act(torch.tensor(-100 , dtype=torch.floataa)).item() , 0)
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa)).item() , 0)
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa)).item() , 20)
| 77 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_features", "is_longer"]
def __init__( self : List[str] , UpperCamelCase_ : Dict=64 , UpperCamelCase_ : Tuple=48000 , UpperCamelCase_ : List[Any]=480 , UpperCamelCase_ : List[str]=10 , UpperCamelCase_ : str=1024 , UpperCamelCase_ : List[str]=0.0 , UpperCamelCase_ : Optional[int]=False , UpperCamelCase_ : float = 0 , UpperCamelCase_ : float = 14000 , UpperCamelCase_ : int = None , UpperCamelCase_ : str = "fusion" , UpperCamelCase_ : str = "repeatpad" , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
feature_size=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , padding_value=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Union[str, Any] = top_db
__UpperCAmelCase : Optional[Any] = truncation
__UpperCAmelCase : str = padding
__UpperCAmelCase : int = fft_window_size
__UpperCAmelCase : str = (fft_window_size >> 1) + 1
__UpperCAmelCase : List[Any] = hop_length
__UpperCAmelCase : Optional[Any] = max_length_s
__UpperCAmelCase : Tuple = max_length_s * sampling_rate
__UpperCAmelCase : str = sampling_rate
__UpperCAmelCase : int = frequency_min
__UpperCAmelCase : Optional[Any] = frequency_max
__UpperCAmelCase : Any = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCamelCase_ , min_frequency=UpperCamelCase_ , max_frequency=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , norm=UpperCamelCase_ , mel_scale="htk" , )
__UpperCAmelCase : Any = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCamelCase_ , min_frequency=UpperCamelCase_ , max_frequency=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , norm="slaney" , mel_scale="slaney" , )
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Dict = copy.deepcopy(self.__dict__)
__UpperCAmelCase : str = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def a_ ( self : int , UpperCamelCase_ : np.array , UpperCamelCase_ : Optional[np.array] = None):
"""simple docstring"""
__UpperCAmelCase : List[Any] = spectrogram(
UpperCamelCase_ , window_function(self.fft_window_size , "hann") , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=UpperCamelCase_ , log_mel="dB" , )
return log_mel_spectrogram.T
def a_ ( self : List[Any] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = np.array_split(list(range(0 , total_frames - chunk_frames + 1)) , 3)
if len(ranges[1]) == 0:
# if the audio is too short, we just use the first chunk
__UpperCAmelCase : str = [0]
if len(ranges[2]) == 0:
# if the audio is too short, we just use the first chunk
__UpperCAmelCase : Dict = [0]
# randomly choose index for each part
__UpperCAmelCase : Dict = np.random.choice(ranges[0])
__UpperCAmelCase : List[str] = np.random.choice(ranges[1])
__UpperCAmelCase : List[Any] = np.random.choice(ranges[2])
__UpperCAmelCase : List[Any] = mel[idx_front : idx_front + chunk_frames, :]
__UpperCAmelCase : List[str] = mel[idx_middle : idx_middle + chunk_frames, :]
__UpperCAmelCase : List[str] = mel[idx_back : idx_back + chunk_frames, :]
__UpperCAmelCase : Tuple = torch.tensor(mel[None, None, :])
__UpperCAmelCase : Union[str, Any] = torch.nn.functional.interpolate(
UpperCamelCase_ , size=[chunk_frames, 64] , mode="bilinear" , align_corners=UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = mel_shrink[0][0].numpy()
__UpperCAmelCase : Optional[int] = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0)
return mel_fusion
def a_ ( self : Optional[Any] , UpperCamelCase_ : np.array , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
__UpperCAmelCase : List[str] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
__UpperCAmelCase : List[Any] = len(UpperCamelCase_) - max_length
__UpperCAmelCase : int = np.random.randint(0 , overflow + 1)
__UpperCAmelCase : Union[str, Any] = waveform[idx : idx + max_length]
__UpperCAmelCase : Union[str, Any] = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters_slaney)[None, :]
elif truncation == "fusion":
__UpperCAmelCase : Any = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters)
__UpperCAmelCase : Dict = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
__UpperCAmelCase : Tuple = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
__UpperCAmelCase : List[str] = np.stack([mel, mel, mel, mel] , axis=0)
__UpperCAmelCase : Any = False
else:
__UpperCAmelCase : List[str] = self._random_mel_fusion(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = True
else:
raise NotImplementedError(F"data_truncating {truncation} not implemented")
else:
__UpperCAmelCase : Optional[Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
__UpperCAmelCase : Tuple = int(max_length / len(UpperCamelCase_))
__UpperCAmelCase : List[str] = np.stack(np.tile(UpperCamelCase_ , n_repeat + 1))[:max_length]
if padding == "repeatpad":
__UpperCAmelCase : Union[str, Any] = int(max_length / len(UpperCamelCase_))
__UpperCAmelCase : Optional[Any] = np.stack(np.tile(UpperCamelCase_ , UpperCamelCase_))
__UpperCAmelCase : int = np.pad(UpperCamelCase_ , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0)
if truncation == "fusion":
__UpperCAmelCase : Any = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters)
__UpperCAmelCase : List[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0)
else:
__UpperCAmelCase : Optional[int] = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters_slaney)[None, :]
return input_mel, longer
def __call__( self : Dict , UpperCamelCase_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , UpperCamelCase_ : str = None , UpperCamelCase_ : Optional[str] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[Union[str, TensorType]] = None , **UpperCamelCase_ : Any , ):
"""simple docstring"""
__UpperCAmelCase : int = truncation if truncation is not None else self.truncation
__UpperCAmelCase : Optional[Any] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
F" was sampled with {self.sampling_rate} and not {sampling_rate}.")
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug.")
__UpperCAmelCase : List[str] = isinstance(UpperCamelCase_ , np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(F"Only mono-channel audio is supported for input to {self}")
__UpperCAmelCase : str = is_batched_numpy or (
isinstance(UpperCamelCase_ , (list, tuple)) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)))
)
if is_batched:
__UpperCAmelCase : Dict = [np.asarray(UpperCamelCase_ , dtype=np.floataa) for speech in raw_speech]
elif not is_batched and not isinstance(UpperCamelCase_ , np.ndarray):
__UpperCAmelCase : Tuple = np.asarray(UpperCamelCase_ , dtype=np.floataa)
elif isinstance(UpperCamelCase_ , np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
__UpperCAmelCase : Optional[int] = raw_speech.astype(np.floataa)
# always return batch
if not is_batched:
__UpperCAmelCase : int = [np.asarray(UpperCamelCase_)]
# convert to mel spectrogram, truncate and pad if needed.
__UpperCAmelCase : Optional[int] = [
self._get_input_mel(UpperCamelCase_ , max_length if max_length else self.nb_max_samples , UpperCamelCase_ , UpperCamelCase_)
for waveform in raw_speech
]
__UpperCAmelCase : Tuple = []
__UpperCAmelCase : List[Any] = []
for mel, longer in padded_inputs:
input_mel.append(UpperCamelCase_)
is_longer.append(UpperCamelCase_)
if truncation == "fusion" and sum(UpperCamelCase_) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
__UpperCAmelCase : Any = np.random.randint(0 , len(UpperCamelCase_))
__UpperCAmelCase : Optional[int] = True
if isinstance(input_mel[0] , UpperCamelCase_):
__UpperCAmelCase : Tuple = [np.asarray(UpperCamelCase_ , dtype=np.floataa) for feature in input_mel]
# is_longer is a list of bool
__UpperCAmelCase : List[str] = [[longer] for longer in is_longer]
__UpperCAmelCase : Optional[int] = {"input_features": input_mel, "is_longer": is_longer}
__UpperCAmelCase : Optional[int] = BatchFeature(UpperCamelCase_)
if return_tensors is not None:
__UpperCAmelCase : Any = input_features.convert_to_tensors(UpperCamelCase_)
return input_features
| 77 | 1 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in range(UpperCamelCase , UpperCamelCase ):
__UpperCAmelCase : List[Any] = i
__UpperCAmelCase : Any = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
__UpperCAmelCase : Dict = array[temp_index - 1]
temp_index -= 1
__UpperCAmelCase : str = temp_index_value
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> None: # Max Heap
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = index
__UpperCAmelCase : List[str] = 2 * index + 1 # Left Node
__UpperCAmelCase : Union[str, Any] = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
__UpperCAmelCase : Tuple = left_index
if right_index < heap_size and array[largest] < array[right_index]:
__UpperCAmelCase : int = right_index
if largest != index:
__UpperCAmelCase , __UpperCAmelCase : List[str] = array[largest], array[index]
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
__UpperCAmelCase : List[Any] = len(UpperCamelCase )
for i in range(n // 2 , -1 , -1 ):
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
for i in range(n - 1 , 0 , -1 ):
__UpperCAmelCase , __UpperCAmelCase : int = array[0], array[i]
heapify(UpperCamelCase , 0 , UpperCamelCase )
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = low
__UpperCAmelCase : List[str] = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = array[j], array[i]
i += 1
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
if len(UpperCamelCase ) == 0:
return array
__UpperCAmelCase : Optional[int] = 2 * math.ceil(math.loga(len(UpperCamelCase ) ) )
__UpperCAmelCase : List[Any] = 16
return intro_sort(UpperCamelCase , 0 , len(UpperCamelCase ) , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> list:
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(UpperCamelCase )
max_depth -= 1
__UpperCAmelCase : List[Any] = median_of_a(UpperCamelCase , UpperCamelCase , start + ((end - start) // 2) + 1 , end - 1 )
__UpperCAmelCase : Union[str, Any] = partition(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
intro_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Optional[Any] = p
return insertion_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
A = input("""Enter numbers separated by a comma : """).strip()
A = [float(item) for item in user_input.split(""",""")]
print(sort(unsorted))
| 77 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : Optional[Any] , UpperCamelCase_ : List[Any]="</s>" , UpperCamelCase_ : Tuple="<unk>" , UpperCamelCase_ : List[str]="<pad>" , UpperCamelCase_ : Union[str, Any]=125 , UpperCamelCase_ : Dict=None , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
if extra_ids > 0 and additional_special_tokens is None:
__UpperCAmelCase : int = [F"<extra_id_{i}>" for i in range(UpperCamelCase_)]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__UpperCAmelCase : Dict = len(set(filter(lambda UpperCamelCase_: bool("extra_id" in str(UpperCamelCase_)) , UpperCamelCase_)))
if extra_tokens != extra_ids:
raise ValueError(
F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
" extra_ids tokens")
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else pad_token
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else eos_token
__UpperCAmelCase : Optional[int] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else unk_token
super().__init__(
eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , extra_ids=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : List[str] = extra_ids
__UpperCAmelCase : int = 2**8 # utf is 8 bits
# define special tokens dict
__UpperCAmelCase : Dict[int, str] = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
__UpperCAmelCase : Any = len(self.special_tokens_encoder)
__UpperCAmelCase : List[Any] = len(UpperCamelCase_)
for i, token in enumerate(UpperCamelCase_):
__UpperCAmelCase : Union[str, Any] = self.vocab_size + i - n
__UpperCAmelCase : Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def a_ ( self : List[Any]):
"""simple docstring"""
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def a_ ( self : List[str] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase_ , token_ids_a=UpperCamelCase_ , already_has_special_tokens=UpperCamelCase_)
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(UpperCamelCase_)) + [1]
return ([0] * len(UpperCamelCase_)) + [1] + ([0] * len(UpperCamelCase_)) + [1]
def a_ ( self : Optional[Any] , UpperCamelCase_ : List[int]):
"""simple docstring"""
if len(UpperCamelCase_) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
" eos tokens being added.")
return token_ids
else:
return token_ids + [self.eos_token_id]
def a_ ( self : Dict , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Dict = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos) * [0]
return len(token_ids_a + eos + token_ids_a + eos) * [0]
def a_ ( self : Optional[int] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self._add_eos_if_not_present(UpperCamelCase_)
if token_ids_a is None:
return token_ids_a
else:
__UpperCAmelCase : List[Any] = self._add_eos_if_not_present(UpperCamelCase_)
return token_ids_a + token_ids_a
def a_ ( self : List[str] , UpperCamelCase_ : str):
"""simple docstring"""
__UpperCAmelCase : Any = [chr(UpperCamelCase_) for i in text.encode("utf-8")]
return tokens
def a_ ( self : Tuple , UpperCamelCase_ : List[Any]):
"""simple docstring"""
if token in self.special_tokens_encoder:
__UpperCAmelCase : Any = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
__UpperCAmelCase : int = self.added_tokens_encoder[token]
elif len(UpperCamelCase_) != 1:
__UpperCAmelCase : Optional[Any] = self.unk_token_id
else:
__UpperCAmelCase : Any = ord(UpperCamelCase_) + self._num_special_tokens
return token_id
def a_ ( self : Any , UpperCamelCase_ : List[str]):
"""simple docstring"""
if index in self.special_tokens_decoder:
__UpperCAmelCase : Any = self.special_tokens_decoder[index]
else:
__UpperCAmelCase : List[str] = chr(index - self._num_special_tokens)
return token
def a_ ( self : Dict , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : str = b""
for token in tokens:
if token in self.special_tokens_decoder:
__UpperCAmelCase : Tuple = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.added_tokens_decoder:
__UpperCAmelCase : Any = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.special_tokens_encoder:
__UpperCAmelCase : Optional[int] = token.encode("utf-8")
elif token in self.added_tokens_encoder:
__UpperCAmelCase : Optional[Any] = token.encode("utf-8")
else:
__UpperCAmelCase : Any = bytes([ord(UpperCamelCase_)])
bstring += tok_string
__UpperCAmelCase : List[Any] = bstring.decode("utf-8" , errors="ignore")
return string
def a_ ( self : Optional[int] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
return ()
| 77 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
A = logging.get_logger(__name__)
A = {
"""EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class a__ ( __magic_name__ ):
lowercase_ = "gpt_neo"
lowercase_ = ["past_key_values"]
lowercase_ = {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self : str , UpperCamelCase_ : str=50257 , UpperCamelCase_ : Optional[int]=2048 , UpperCamelCase_ : Dict=2048 , UpperCamelCase_ : int=24 , UpperCamelCase_ : int=[[["global", "local"], 12]] , UpperCamelCase_ : Dict=16 , UpperCamelCase_ : str=None , UpperCamelCase_ : int=256 , UpperCamelCase_ : str="gelu_new" , UpperCamelCase_ : str=0.0 , UpperCamelCase_ : Dict=0.0 , UpperCamelCase_ : Tuple=0.0 , UpperCamelCase_ : List[str]=0.1 , UpperCamelCase_ : Dict=1e-5 , UpperCamelCase_ : Tuple=0.02 , UpperCamelCase_ : Dict=True , UpperCamelCase_ : List[Any]=50256 , UpperCamelCase_ : Optional[Any]=50256 , **UpperCamelCase_ : str , ):
"""simple docstring"""
__UpperCAmelCase : Tuple = vocab_size
__UpperCAmelCase : str = max_position_embeddings
__UpperCAmelCase : Any = hidden_size
__UpperCAmelCase : Any = num_layers
__UpperCAmelCase : int = num_heads
__UpperCAmelCase : List[str] = intermediate_size
__UpperCAmelCase : Union[str, Any] = window_size
__UpperCAmelCase : Tuple = activation_function
__UpperCAmelCase : List[str] = resid_dropout
__UpperCAmelCase : Optional[int] = embed_dropout
__UpperCAmelCase : Tuple = attention_dropout
__UpperCAmelCase : Optional[Any] = classifier_dropout
__UpperCAmelCase : int = layer_norm_epsilon
__UpperCAmelCase : Any = initializer_range
__UpperCAmelCase : Union[str, Any] = use_cache
__UpperCAmelCase : Union[str, Any] = bos_token_id
__UpperCAmelCase : List[Any] = eos_token_id
__UpperCAmelCase : Union[str, Any] = attention_types
__UpperCAmelCase : Dict = self.expand_attention_types_params(UpperCamelCase_)
if len(self.attention_layers) != self.num_layers:
raise ValueError(
"Configuration for convolutional module is incorrect. "
"It is required that `len(config.attention_layers)` == `config.num_layers` "
F"but is `len(config.attention_layers) = {len(self.attention_layers)}`, "
F"`config.num_layers = {self.num_layers}`. "
"`config.attention_layers` is prepared using `config.attention_types`. "
"Please verify the value of `config.attention_types` argument.")
super().__init__(bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_)
@staticmethod
def a_ ( UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = []
for item in attention_types:
for _ in range(item[1]):
attentions.extend(item[0])
return attentions
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[Any]:
"""simple docstring"""
import torch
__UpperCAmelCase : Any = input.size()
__UpperCAmelCase : Dict = len(UpperCamelCase )
__UpperCAmelCase : Any = shape[dimension]
__UpperCAmelCase : List[Any] = torch.arange(0 , UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Optional[Any] = torch.div(sizedim - size , UpperCamelCase , rounding_mode="floor" ) + 1
__UpperCAmelCase : str = torch.arange(UpperCamelCase ) + low_indices[:min_length][:, None]
__UpperCAmelCase : List[str] = [slice(UpperCamelCase )] * rank
__UpperCAmelCase : List[Any] = indices
__UpperCAmelCase : Dict = input[s]
__UpperCAmelCase : List[str] = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
import torch
__UpperCAmelCase : str = torch.arange(1 , UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = torch.remainder(UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Optional[int] = remainders == 0
__UpperCAmelCase : Optional[Any] = candidates[divisor_indices]
__UpperCAmelCase : List[str] = torch.max(UpperCamelCase )
return largest_divisor, torch.div(UpperCamelCase , UpperCamelCase , rounding_mode="floor" )
class a__ ( __magic_name__ ):
@property
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : int = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
if self.use_past:
self.fill_with_past_key_values_(UpperCamelCase_ , direction="inputs")
__UpperCAmelCase : Dict = {0: "batch", 1: "past_sequence + sequence"}
else:
__UpperCAmelCase : List[Any] = {0: "batch", 1: "sequence"}
return common_inputs
@property
def a_ ( self : int):
"""simple docstring"""
return self._config.num_heads
def a_ ( self : Dict , UpperCamelCase_ : PreTrainedTokenizer , UpperCamelCase_ : int = -1 , UpperCamelCase_ : int = -1 , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[TensorType] = None , ):
"""simple docstring"""
__UpperCAmelCase : int = super(UpperCamelCase_ , self).generate_dummy_inputs(
UpperCamelCase_ , batch_size=UpperCamelCase_ , seq_length=UpperCamelCase_ , is_pair=UpperCamelCase_ , framework=UpperCamelCase_)
# We need to order the input in the way they appears in the forward()
__UpperCAmelCase : str = OrderedDict({"input_ids": common_inputs["input_ids"]})
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
__UpperCAmelCase , __UpperCAmelCase : str = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
__UpperCAmelCase : str = seqlen + 2
__UpperCAmelCase : int = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__UpperCAmelCase : Dict = [
(torch.zeros(UpperCamelCase_), torch.zeros(UpperCamelCase_)) for _ in range(self.num_layers)
]
__UpperCAmelCase : Dict = common_inputs["attention_mask"]
if self.use_past:
__UpperCAmelCase : str = ordered_inputs["attention_mask"].dtype
__UpperCAmelCase : List[Any] = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(UpperCamelCase_ , UpperCamelCase_ , dtype=UpperCamelCase_)] , dim=1)
return ordered_inputs
@property
def a_ ( self : Any):
"""simple docstring"""
return 13
| 77 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self : List[Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple=3 , UpperCamelCase_ : Optional[int]=32 , UpperCamelCase_ : Dict=3 , UpperCamelCase_ : List[str]=10 , UpperCamelCase_ : str=[10, 20, 30, 40] , UpperCamelCase_ : Tuple=[1, 1, 2, 1] , UpperCamelCase_ : str=True , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Dict="relu" , UpperCamelCase_ : str=3 , UpperCamelCase_ : int=None , ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = parent
__UpperCAmelCase : List[str] = batch_size
__UpperCAmelCase : List[str] = image_size
__UpperCAmelCase : Tuple = num_channels
__UpperCAmelCase : Union[str, Any] = embeddings_size
__UpperCAmelCase : Dict = hidden_sizes
__UpperCAmelCase : Dict = depths
__UpperCAmelCase : Tuple = is_training
__UpperCAmelCase : List[Any] = use_labels
__UpperCAmelCase : Optional[int] = hidden_act
__UpperCAmelCase : str = num_labels
__UpperCAmelCase : Optional[int] = scope
__UpperCAmelCase : Dict = len(UpperCamelCase_)
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__UpperCAmelCase : Dict = self.get_config()
return config, pixel_values
def a_ ( self : Dict):
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def a_ ( self : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = FlaxRegNetModel(config=UpperCamelCase_)
__UpperCAmelCase : Dict = model(UpperCamelCase_)
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def a_ ( self : Any , UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.num_labels
__UpperCAmelCase : Tuple = FlaxRegNetForImageClassification(config=UpperCamelCase_)
__UpperCAmelCase : str = model(UpperCamelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Any = self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase : Tuple = config_and_inputs
__UpperCAmelCase : Optional[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
lowercase_ = False
lowercase_ = False
lowercase_ = False
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = FlaxRegNetModelTester(self)
__UpperCAmelCase : str = ConfigTester(self , config_class=UpperCamelCase_ , has_text_modality=UpperCamelCase_)
def a_ ( self : Dict):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def a_ ( self : Tuple):
"""simple docstring"""
return
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_)
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_)
@unittest.skip(reason="RegNet does not use inputs_embeds")
def a_ ( self : Union[str, Any]):
"""simple docstring"""
pass
@unittest.skip(reason="RegNet does not support input and output embeddings")
def a_ ( self : Optional[int]):
"""simple docstring"""
pass
def a_ ( self : str):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : int = model_class(UpperCamelCase_)
__UpperCAmelCase : Optional[int] = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase : Any = [*signature.parameters.keys()]
__UpperCAmelCase : Dict = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCamelCase_)
def a_ ( self : int):
"""simple docstring"""
def check_hidden_states_output(UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Union[str, Any]):
__UpperCAmelCase : Union[str, Any] = model_class(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_))
__UpperCAmelCase : List[str] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__UpperCAmelCase : str = self.model_tester.num_stages
self.assertEqual(len(UpperCamelCase_) , expected_num_stages + 1)
__UpperCAmelCase , __UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : List[str] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase : Optional[int] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
def a_ ( self : Tuple):
"""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 : List[Any] = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Optional[int] = model_class(UpperCamelCase_)
@jax.jit
def model_jitted(UpperCamelCase_ : int , **UpperCamelCase_ : Optional[int]):
return model(pixel_values=UpperCamelCase_ , **UpperCamelCase_)
with self.subTest("JIT Enabled"):
__UpperCAmelCase : Optional[Any] = model_jitted(**UpperCamelCase_).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
__UpperCAmelCase : Dict = model_jitted(**UpperCamelCase_).to_tuple()
self.assertEqual(len(UpperCamelCase_) , len(UpperCamelCase_))
for jitted_output, output in zip(UpperCamelCase_ , UpperCamelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCamelCase ( ) -> Any:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_flax
class a__ ( unittest.TestCase ):
@cached_property
def a_ ( self : Optional[int]):
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/regnet-y-040") if is_vision_available() else None
@slow
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Any = FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")
__UpperCAmelCase : Dict = self.default_image_processor
__UpperCAmelCase : str = prepare_img()
__UpperCAmelCase : int = image_processor(images=UpperCamelCase_ , return_tensors="np")
__UpperCAmelCase : Dict = model(**UpperCamelCase_)
# verify the logits
__UpperCAmelCase : Dict = (1, 1000)
self.assertEqual(outputs.logits.shape , UpperCamelCase_)
__UpperCAmelCase : Any = jnp.array([-0.4180, -1.5051, -3.4836])
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , UpperCamelCase_ , atol=1e-4))
| 77 | 1 |
"""simple docstring"""
import requests
A = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def _UpperCamelCase ( UpperCamelCase ) -> None:
"""simple docstring"""
# fetching a list of articles in json format
__UpperCAmelCase : Union[str, Any] = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["articles"] , 1 ):
print(f"{i}.) {article['title']}" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")
| 77 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
A = """
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results['spearmanr'])
-0.7
>>> print(round(results['spearmanr_pvalue'], 2))
0.19
"""
A = r"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
def a_ ( self : Any):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("float"),
"references": datasets.Value("float"),
}) , reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"] , )
def a_ ( self : Union[str, Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : int=False):
"""simple docstring"""
__UpperCAmelCase : List[str] = spearmanr(UpperCamelCase_ , UpperCamelCase_)
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("String lengths must match!" )
__UpperCAmelCase : str = 0
for chara, chara in zip(UpperCamelCase , UpperCamelCase ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 77 |
"""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""": """spiece.model"""}
A = {
"""vocab_file""": {
"""bert_for_seq_generation""": (
"""https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
),
}
}
A = {"""bert_for_seq_generation""": 512}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = []
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : List[str] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : List[str]="<s>" , UpperCamelCase_ : Optional[Any]="</s>" , UpperCamelCase_ : Optional[int]="<unk>" , UpperCamelCase_ : int="<pad>" , UpperCamelCase_ : List[Any]="<::::>" , UpperCamelCase_ : Optional[Dict[str, Any]] = None , **UpperCamelCase_ : List[Any] , ):
"""simple docstring"""
__UpperCAmelCase : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase_ , )
__UpperCAmelCase : Dict = vocab_file
__UpperCAmelCase : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(UpperCamelCase_)
@property
def a_ ( self : List[str]):
"""simple docstring"""
return self.sp_model.get_piece_size()
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : int = {self.convert_ids_to_tokens(UpperCamelCase_): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self : int):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = self.__dict__.copy()
__UpperCAmelCase : List[Any] = None
return state
def __setstate__( self : Optional[Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs"):
__UpperCAmelCase : List[Any] = {}
__UpperCAmelCase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def a_ ( self : Any , UpperCamelCase_ : str):
"""simple docstring"""
return self.sp_model.encode(UpperCamelCase_ , out_type=UpperCamelCase_)
def a_ ( self : Optional[Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
return self.sp_model.piece_to_id(UpperCamelCase_)
def a_ ( self : Tuple , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : int = self.sp_model.IdToPiece(UpperCamelCase_)
return token
def a_ ( self : Dict , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : int = []
__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(UpperCamelCase_) + token
__UpperCAmelCase : List[Any] = []
else:
current_sub_tokens.append(UpperCamelCase_)
out_string += self.sp_model.decode(UpperCamelCase_)
return out_string.strip()
def a_ ( self : List[str] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : Tuple = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , UpperCamelCase_)
elif not os.path.isfile(self.vocab_file):
with open(UpperCamelCase_ , "wb") as fi:
__UpperCAmelCase : List[str] = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase_)
return (out_vocab_file,)
| 77 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = ProphetNetTokenizer
lowercase_ = False
def a_ ( self : List[Any]):
"""simple docstring"""
super().setUp()
__UpperCAmelCase : Optional[int] = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
__UpperCAmelCase : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file , "w" , encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
def a_ ( self : List[str] , UpperCamelCase_ : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = "UNwant\u00E9d,running"
__UpperCAmelCase : Dict = "unwanted, running"
return input_text, output_text
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.tokenizer_class(self.vocab_file)
__UpperCAmelCase : Optional[Any] = tokenizer.tokenize("UNwant\u00E9d,running")
self.assertListEqual(UpperCamelCase_ , ["un", "##want", "##ed", ",", "runn", "##ing"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_) , [9, 6, 7, 12, 10, 11])
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : str = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz") , ["ah", "\u535A", "\u63A8", "zz"])
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Dict = BasicTokenizer(do_lower_case=UpperCamelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? ") , ["hello", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["hello"])
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Any = BasicTokenizer(do_lower_case=UpperCamelCase_ , strip_accents=UpperCamelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["hällo", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["h\u00E9llo"])
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = BasicTokenizer(do_lower_case=UpperCamelCase_ , strip_accents=UpperCamelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["hallo", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["hello"])
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = BasicTokenizer(do_lower_case=UpperCamelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["hallo", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["hello"])
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = BasicTokenizer(do_lower_case=UpperCamelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? ") , ["HeLLo", "!", "how", "Are", "yoU", "?"])
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : int = BasicTokenizer(do_lower_case=UpperCamelCase_ , strip_accents=UpperCamelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["HäLLo", "!", "how", "Are", "yoU", "?"])
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = BasicTokenizer(do_lower_case=UpperCamelCase_ , strip_accents=UpperCamelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["HaLLo", "!", "how", "Are", "yoU", "?"])
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = BasicTokenizer(do_lower_case=UpperCamelCase_ , never_split=["[UNK]"])
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]") , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"])
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : List[str] = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
__UpperCAmelCase : Optional[int] = {}
for i, token in enumerate(UpperCamelCase_):
__UpperCAmelCase : Tuple = i
__UpperCAmelCase : Optional[Any] = WordpieceTokenizer(vocab=UpperCamelCase_ , unk_token="[UNK]")
self.assertListEqual(tokenizer.tokenize("") , [])
self.assertListEqual(tokenizer.tokenize("unwanted running") , ["un", "##want", "##ed", "runn", "##ing"])
self.assertListEqual(tokenizer.tokenize("unwantedX running") , ["[UNK]", "runn", "##ing"])
@require_torch
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased")
__UpperCAmelCase : Any = ["A long paragraph for summarization.", "Another paragraph for summarization."]
__UpperCAmelCase : Dict = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
__UpperCAmelCase : Union[str, Any] = tokenizer(UpperCamelCase_ , padding=UpperCamelCase_ , return_tensors="pt")
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Optional[int] = list(batch.input_ids.numpy()[0])
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_)
self.assertEqual((2, 9) , batch.input_ids.shape)
self.assertEqual((2, 9) , batch.attention_mask.shape)
def a_ ( self : Tuple):
"""simple docstring"""
self.assertTrue(_is_whitespace(" "))
self.assertTrue(_is_whitespace("\t"))
self.assertTrue(_is_whitespace("\r"))
self.assertTrue(_is_whitespace("\n"))
self.assertTrue(_is_whitespace("\u00A0"))
self.assertFalse(_is_whitespace("A"))
self.assertFalse(_is_whitespace("-"))
def a_ ( self : List[str]):
"""simple docstring"""
self.assertTrue(_is_control("\u0005"))
self.assertFalse(_is_control("A"))
self.assertFalse(_is_control(" "))
self.assertFalse(_is_control("\t"))
self.assertFalse(_is_control("\r"))
def a_ ( self : Tuple):
"""simple docstring"""
self.assertTrue(_is_punctuation("-"))
self.assertTrue(_is_punctuation("$"))
self.assertTrue(_is_punctuation("`"))
self.assertTrue(_is_punctuation("."))
self.assertFalse(_is_punctuation("A"))
self.assertFalse(_is_punctuation(" "))
@slow
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased")
__UpperCAmelCase : Tuple = tokenizer.encode("sequence builders" , add_special_tokens=UpperCamelCase_)
__UpperCAmelCase : List[str] = tokenizer.encode("multi-sequence build" , add_special_tokens=UpperCamelCase_)
__UpperCAmelCase : int = tokenizer.build_inputs_with_special_tokens(UpperCamelCase_)
__UpperCAmelCase : Any = tokenizer.build_inputs_with_special_tokens(UpperCamelCase_ , UpperCamelCase_)
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 77 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
A = """true"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=82 , UpperCamelCase=16 ) -> Tuple:
"""simple docstring"""
set_seed(42 )
__UpperCAmelCase : Dict = RegressionModel()
__UpperCAmelCase : Optional[Any] = deepcopy(UpperCamelCase )
__UpperCAmelCase : Any = RegressionDataset(length=UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = DataLoader(UpperCamelCase , batch_size=UpperCamelCase )
model.to(accelerator.device )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = accelerator.prepare(UpperCamelCase , UpperCamelCase )
return model, ddp_model, dataloader
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=False ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : List[str] = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
__UpperCAmelCase : Dict = load_dataset("glue" , "mrpc" , split="validation" )
def tokenize_function(UpperCamelCase ):
__UpperCAmelCase : Dict = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=UpperCamelCase , max_length=UpperCamelCase )
return outputs
with accelerator.main_process_first():
__UpperCAmelCase : str = dataset.map(
UpperCamelCase , batched=UpperCamelCase , remove_columns=["idx", "sentence1", "sentence2"] , )
__UpperCAmelCase : List[Any] = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(UpperCamelCase ):
if use_longest:
return tokenizer.pad(UpperCamelCase , padding="longest" , return_tensors="pt" )
return tokenizer.pad(UpperCamelCase , padding="max_length" , max_length=128 , return_tensors="pt" )
return DataLoader(UpperCamelCase , shuffle=UpperCamelCase , collate_fn=UpperCamelCase , batch_size=16 )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : List[Any] = Accelerator(dispatch_batches=UpperCamelCase , split_batches=UpperCamelCase )
__UpperCAmelCase : int = get_dataloader(UpperCamelCase , not dispatch_batches )
__UpperCAmelCase : Any = AutoModelForSequenceClassification.from_pretrained(
"hf-internal-testing/mrpc-bert-base-cased" , return_dict=UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Dict = accelerator.prepare(UpperCamelCase , UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase : Dict = []
for batch in dataloader:
__UpperCAmelCase , __UpperCAmelCase : int = batch.values()
with torch.no_grad():
__UpperCAmelCase : int = model(UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : List[str] = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = [], []
for logit, targ in logits_and_targets:
logits.append(UpperCamelCase )
targs.append(UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = torch.cat(UpperCamelCase ), torch.cat(UpperCamelCase )
return logits, targs
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=82 , UpperCamelCase=False , UpperCamelCase=False , UpperCamelCase=16 ) -> int:
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = get_basic_setup(UpperCamelCase , UpperCamelCase , UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = generate_predictions(UpperCamelCase , UpperCamelCase , UpperCamelCase )
assert (
len(UpperCamelCase ) == num_samples
), f"Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(UpperCamelCase )}"
def _UpperCamelCase ( UpperCamelCase = False , UpperCamelCase = False ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : List[str] = evaluate.load("glue" , "mrpc" )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = get_mrpc_setup(UpperCamelCase , UpperCamelCase )
# First do baseline
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Tuple = setup["no"]
model.to(UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(UpperCamelCase )
with torch.inference_mode():
__UpperCAmelCase : List[str] = model(**UpperCamelCase )
__UpperCAmelCase : str = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=UpperCamelCase , references=batch["labels"] )
__UpperCAmelCase : str = metric.compute()
# Then do distributed
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
__UpperCAmelCase : Any = model(**UpperCamelCase )
__UpperCAmelCase : str = outputs.logits.argmax(dim=-1 )
__UpperCAmelCase : Union[str, Any] = batch["labels"]
__UpperCAmelCase , __UpperCAmelCase : Any = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=UpperCamelCase , references=UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"
def _UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
__UpperCAmelCase : Dict = Accelerator(split_batches=UpperCamelCase , dispatch_batches=UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("**Testing gather_for_metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`" )
test_mrpc(UpperCamelCase , UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test torch metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__UpperCAmelCase : Union[str, Any] = Accelerator(split_batches=UpperCamelCase , dispatch_batches=UpperCamelCase )
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99" )
test_torch_metrics(UpperCamelCase , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test last batch is not dropped when perfectly divisible**" )
__UpperCAmelCase : Any = Accelerator()
test_torch_metrics(UpperCamelCase , 512 )
accelerator.state._reset_state()
def _UpperCamelCase ( UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 77 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class a__ :
def __init__( self : int , UpperCamelCase_ : Tuple , UpperCamelCase_ : Any=13 , UpperCamelCase_ : List[str]=7 , UpperCamelCase_ : Optional[Any]=True , UpperCamelCase_ : Dict=True , UpperCamelCase_ : str=True , UpperCamelCase_ : Any=99 , UpperCamelCase_ : List[Any]=32 , UpperCamelCase_ : Union[str, Any]=5 , UpperCamelCase_ : str=4 , UpperCamelCase_ : Union[str, Any]=37 , UpperCamelCase_ : Any="gelu" , UpperCamelCase_ : List[Any]=0.1 , UpperCamelCase_ : Tuple=0.1 , UpperCamelCase_ : Any=512 , UpperCamelCase_ : Any=16 , UpperCamelCase_ : Optional[Any]=2 , UpperCamelCase_ : Optional[int]=0.02 , UpperCamelCase_ : Tuple=3 , UpperCamelCase_ : Optional[int]=4 , UpperCamelCase_ : Any=None , ):
"""simple docstring"""
__UpperCAmelCase : Dict = parent
__UpperCAmelCase : List[str] = batch_size
__UpperCAmelCase : Union[str, Any] = seq_length
__UpperCAmelCase : Optional[Any] = is_training
__UpperCAmelCase : Tuple = use_token_type_ids
__UpperCAmelCase : List[Any] = use_labels
__UpperCAmelCase : Optional[Any] = vocab_size
__UpperCAmelCase : int = hidden_size
__UpperCAmelCase : Dict = num_hidden_layers
__UpperCAmelCase : List[Any] = num_attention_heads
__UpperCAmelCase : Optional[Any] = intermediate_size
__UpperCAmelCase : Optional[Any] = hidden_act
__UpperCAmelCase : int = hidden_dropout_prob
__UpperCAmelCase : int = attention_probs_dropout_prob
__UpperCAmelCase : Dict = max_position_embeddings
__UpperCAmelCase : Tuple = type_vocab_size
__UpperCAmelCase : Optional[int] = type_sequence_label_size
__UpperCAmelCase : Union[str, Any] = initializer_range
__UpperCAmelCase : Dict = num_labels
__UpperCAmelCase : Dict = num_choices
__UpperCAmelCase : Dict = scope
__UpperCAmelCase : List[str] = self.vocab_size - 1
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__UpperCAmelCase : Union[str, Any] = None
if self.use_token_type_ids:
__UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
__UpperCAmelCase : Optional[Any] = None
__UpperCAmelCase : Dict = None
__UpperCAmelCase : Optional[Any] = None
if self.use_labels:
__UpperCAmelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
__UpperCAmelCase : Any = ids_tensor([self.batch_size] , self.num_choices)
__UpperCAmelCase : Dict = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
__UpperCAmelCase : Any = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2)
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def a_ ( self : Dict , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : int , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Tuple , *UpperCamelCase_ : List[str]):
"""simple docstring"""
__UpperCAmelCase : int = OpenAIGPTModel(config=UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
__UpperCAmelCase : Optional[int] = model(UpperCamelCase_ , token_type_ids=UpperCamelCase_ , head_mask=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = model(UpperCamelCase_ , token_type_ids=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = model(UpperCamelCase_)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def a_ ( self : str , UpperCamelCase_ : Dict , UpperCamelCase_ : List[Any] , UpperCamelCase_ : int , UpperCamelCase_ : Tuple , *UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Any = OpenAIGPTLMHeadModel(UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
__UpperCAmelCase : Optional[Any] = model(UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_)
self.parent.assertEqual(result.loss.shape , ())
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def a_ ( self : str , UpperCamelCase_ : Dict , UpperCamelCase_ : Dict , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Tuple , *UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : Any = OpenAIGPTDoubleHeadsModel(UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
__UpperCAmelCase : Optional[Any] = model(UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_)
self.parent.assertEqual(result.loss.shape , ())
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def a_ ( self : Tuple , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Optional[Any] , *UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.num_labels
__UpperCAmelCase : List[str] = OpenAIGPTForSequenceClassification(UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
__UpperCAmelCase : int = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__UpperCAmelCase : List[Any] = model(UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) : str = config_and_inputs
__UpperCAmelCase : Any = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_torch
class a__ ( __magic_name__ , __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase_ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
lowercase_ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
lowercase_ = (
{
"feature-extraction": OpenAIGPTModel,
"text-classification": OpenAIGPTForSequenceClassification,
"text-generation": OpenAIGPTLMHeadModel,
"zero-shot": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def a_ ( self : Any , UpperCamelCase_ : Any , UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : int , UpperCamelCase_ : str):
"""simple docstring"""
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def a_ ( self : Optional[Any] , UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Optional[Any]=False):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = super()._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ , return_labels=UpperCamelCase_)
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
__UpperCAmelCase : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=UpperCamelCase_ , )
__UpperCAmelCase : Optional[int] = inputs_dict["labels"]
__UpperCAmelCase : Any = inputs_dict["labels"]
__UpperCAmelCase : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=UpperCamelCase_ , )
__UpperCAmelCase : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCamelCase_)
return inputs_dict
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : str = OpenAIGPTModelTester(self)
__UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=UpperCamelCase_ , n_embd=37)
def a_ ( self : Dict):
"""simple docstring"""
self.config_tester.run_common_tests()
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*UpperCamelCase_)
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*UpperCamelCase_)
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*UpperCamelCase_)
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*UpperCamelCase_)
@slow
def a_ ( self : Tuple):
"""simple docstring"""
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase : List[str] = OpenAIGPTModel.from_pretrained(UpperCamelCase_)
self.assertIsNotNone(UpperCamelCase_)
@require_torch
class a__ ( unittest.TestCase ):
@slow
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = OpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
model.to(UpperCamelCase_)
__UpperCAmelCase : List[Any] = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=UpperCamelCase_) # the president is
__UpperCAmelCase : List[str] = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
__UpperCAmelCase : str = model.generate(UpperCamelCase_ , do_sample=UpperCamelCase_)
self.assertListEqual(output_ids[0].tolist() , UpperCamelCase_)
| 77 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in range(UpperCamelCase , UpperCamelCase ):
__UpperCAmelCase : List[Any] = i
__UpperCAmelCase : Any = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
__UpperCAmelCase : Dict = array[temp_index - 1]
temp_index -= 1
__UpperCAmelCase : str = temp_index_value
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> None: # Max Heap
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = index
__UpperCAmelCase : List[str] = 2 * index + 1 # Left Node
__UpperCAmelCase : Union[str, Any] = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
__UpperCAmelCase : Tuple = left_index
if right_index < heap_size and array[largest] < array[right_index]:
__UpperCAmelCase : int = right_index
if largest != index:
__UpperCAmelCase , __UpperCAmelCase : List[str] = array[largest], array[index]
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
__UpperCAmelCase : List[Any] = len(UpperCamelCase )
for i in range(n // 2 , -1 , -1 ):
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
for i in range(n - 1 , 0 , -1 ):
__UpperCAmelCase , __UpperCAmelCase : int = array[0], array[i]
heapify(UpperCamelCase , 0 , UpperCamelCase )
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = low
__UpperCAmelCase : List[str] = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = array[j], array[i]
i += 1
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
if len(UpperCamelCase ) == 0:
return array
__UpperCAmelCase : Optional[int] = 2 * math.ceil(math.loga(len(UpperCamelCase ) ) )
__UpperCAmelCase : List[Any] = 16
return intro_sort(UpperCamelCase , 0 , len(UpperCamelCase ) , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> list:
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(UpperCamelCase )
max_depth -= 1
__UpperCAmelCase : List[Any] = median_of_a(UpperCamelCase , UpperCamelCase , start + ((end - start) // 2) + 1 , end - 1 )
__UpperCAmelCase : Union[str, Any] = partition(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
intro_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Optional[Any] = p
return insertion_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
A = input("""Enter numbers separated by a comma : """).strip()
A = [float(item) for item in user_input.split(""",""")]
print(sort(unsorted))
| 77 | 1 |
"""simple docstring"""
from __future__ import annotations
A = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
A = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def _UpperCamelCase ( UpperCamelCase ) -> list[float]:
"""simple docstring"""
__UpperCAmelCase : List[Any] = []
__UpperCAmelCase : int = len(UpperCamelCase )
for i in range(UpperCamelCase ):
__UpperCAmelCase : float = -1
for j in range(i + 1 , UpperCamelCase ):
if arr[i] < arr[j]:
__UpperCAmelCase : int = arr[j]
break
result.append(UpperCamelCase )
return result
def _UpperCamelCase ( UpperCamelCase ) -> list[float]:
"""simple docstring"""
__UpperCAmelCase : int = []
for i, outer in enumerate(UpperCamelCase ):
__UpperCAmelCase : float = -1
for inner in arr[i + 1 :]:
if outer < inner:
__UpperCAmelCase : Optional[int] = inner
break
result.append(UpperCamelCase )
return result
def _UpperCamelCase ( UpperCamelCase ) -> list[float]:
"""simple docstring"""
__UpperCAmelCase : Optional[int] = len(UpperCamelCase )
__UpperCAmelCase : list[float] = []
__UpperCAmelCase : list[float] = [-1] * arr_size
for index in reversed(range(UpperCamelCase ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
__UpperCAmelCase : Optional[int] = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
A = (
"""from __main__ import arr, next_greatest_element_slow, """
"""next_greatest_element_fast, next_greatest_element"""
)
print(
"""next_greatest_element_slow():""",
timeit("""next_greatest_element_slow(arr)""", setup=setup),
)
print(
"""next_greatest_element_fast():""",
timeit("""next_greatest_element_fast(arr)""", setup=setup),
)
print(
""" next_greatest_element():""",
timeit("""next_greatest_element(arr)""", setup=setup),
)
| 77 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
__UpperCAmelCase : Any = 0
__UpperCAmelCase : Dict = 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Tuple = 0
# compute the shape of the output matrix
__UpperCAmelCase : Optional[int] = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
__UpperCAmelCase : List[str] = np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
__UpperCAmelCase : str = np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
__UpperCAmelCase : int = 0
__UpperCAmelCase : int = 0
return updated_arr
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : List[str] = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Any = 0
# compute the shape of the output matrix
__UpperCAmelCase : Tuple = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
__UpperCAmelCase : str = np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
__UpperCAmelCase : Tuple = int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Optional[Any] = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name="""avgpooling""", verbose=True)
# Loading the image
A = Image.open("""path_to_image""")
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
if index == r:
for j in range(UpperCamelCase ):
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 : Tuple = arr[i]
combination_util(UpperCamelCase , UpperCamelCase , UpperCamelCase , index + 1 , UpperCamelCase , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Dict:
"""simple docstring"""
# A temporary array to store all combination one by one
__UpperCAmelCase : Optional[Any] = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(UpperCamelCase , UpperCamelCase , UpperCamelCase , 0 , UpperCamelCase , 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
| 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A = None
A = logging.get_logger(__name__)
A = """▁"""
A = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
A = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
A = {
"""google/pegasus-xsum""": 512,
}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = PegasusTokenizer
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : str , UpperCamelCase_ : str=None , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : int="<pad>" , UpperCamelCase_ : Optional[Any]="</s>" , UpperCamelCase_ : Any="<unk>" , UpperCamelCase_ : Tuple="<mask_2>" , UpperCamelCase_ : Any="<mask_1>" , UpperCamelCase_ : Tuple=None , UpperCamelCase_ : str=103 , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = offset
if additional_special_tokens is not None:
if not isinstance(UpperCamelCase_ , UpperCamelCase_):
raise TypeError(
F"additional_special_tokens should be of type {type(UpperCamelCase_)}, but is"
F" {type(UpperCamelCase_)}")
__UpperCAmelCase : Any = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F"<unk_{i}>" for i in range(len(UpperCamelCase_) , self.offset - 1)
]
if len(set(UpperCamelCase_)) != len(UpperCamelCase_):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.")
__UpperCAmelCase : str = additional_special_tokens_extended
else:
__UpperCAmelCase : Tuple = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F"<unk_{i}>" for i in range(2 , self.offset)]
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , pad_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , mask_token_sent=UpperCamelCase_ , offset=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Optional[int] = vocab_file
__UpperCAmelCase : List[str] = False if not self.vocab_file else True
def a_ ( self : Union[str, Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : int = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
F" {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}")
return [1 if x in all_special_ids else 0 for x in seq]
def a_ ( self : Union[str, Any] , UpperCamelCase_ : List , UpperCamelCase_ : Optional[List] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(UpperCamelCase_)
elif token_ids_a is None:
return self._special_token_mask(UpperCamelCase_) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a) + [1]
def a_ ( self : int , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[Any]=None):
"""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 a_ ( self : Union[str, Any] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer.")
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : List[str] = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_):
copyfile(self.vocab_file , UpperCamelCase_)
return (out_vocab_file,)
| 77 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
A = None
A = logging.get_logger(__name__)
A = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
A = {
"""vocab_file""": {
"""google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
),
},
"""tokenizer_file""": {
"""google/bigbird-roberta-base""": (
"""https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
),
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
),
},
}
A = {
"""google/bigbird-roberta-base""": 4_096,
"""google/bigbird-roberta-large""": 4_096,
"""google/bigbird-base-trivia-itc""": 4_096,
}
A = """▁"""
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = BigBirdTokenizer
lowercase_ = ["input_ids", "attention_mask"]
lowercase_ = []
def __init__( self : List[Any] , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : Dict=None , UpperCamelCase_ : List[str]="<unk>" , UpperCamelCase_ : Union[str, Any]="<s>" , UpperCamelCase_ : Optional[int]="</s>" , UpperCamelCase_ : List[Any]="<pad>" , UpperCamelCase_ : str="[SEP]" , UpperCamelCase_ : Optional[Any]="[MASK]" , UpperCamelCase_ : Union[str, Any]="[CLS]" , **UpperCamelCase_ : Tuple , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else bos_token
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else eos_token
__UpperCAmelCase : Optional[Any] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else unk_token
__UpperCAmelCase : List[Any] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else pad_token
__UpperCAmelCase : str = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else cls_token
__UpperCAmelCase : List[Any] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
__UpperCAmelCase : Optional[Any] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else mask_token
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , cls_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Any = vocab_file
__UpperCAmelCase : List[Any] = False if not self.vocab_file else True
def a_ ( self : Optional[int] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : List[str] = [self.sep_token_id]
__UpperCAmelCase : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self : List[Any] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model.")
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(UpperCamelCase_)) + [1]
return [1] + ([0] * len(UpperCamelCase_)) + [1] + ([0] * len(UpperCamelCase_)) + [1]
def a_ ( self : str , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : List[str] = [self.sep_token_id]
__UpperCAmelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]
def a_ ( self : int , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer.")
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : Optional[int] = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_):
copyfile(self.vocab_file , UpperCamelCase_)
return (out_vocab_file,)
| 77 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[str]:
"""simple docstring"""
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
__UpperCAmelCase : Optional[Any] = TapasConfig.from_json_file(UpperCamelCase )
# set absolute/relative position embeddings parameter
__UpperCAmelCase : Optional[Any] = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
__UpperCAmelCase : List[str] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "WTQ":
# run_task_main.py hparams
__UpperCAmelCase : Tuple = 4
__UpperCAmelCase : Any = True
# hparam_utils.py hparams
__UpperCAmelCase : Union[str, Any] = 0.664694
__UpperCAmelCase : Union[str, Any] = 0.207951
__UpperCAmelCase : int = 0.121194
__UpperCAmelCase : Optional[int] = True
__UpperCAmelCase : List[str] = True
__UpperCAmelCase : Union[str, Any] = False
__UpperCAmelCase : List[str] = 0.0352513
__UpperCAmelCase : Optional[int] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
__UpperCAmelCase : int = 4
__UpperCAmelCase : Optional[int] = False
# hparam_utils.py hparams
__UpperCAmelCase : int = 36.4519
__UpperCAmelCase : str = 0.903421
__UpperCAmelCase : Dict = 222.088
__UpperCAmelCase : Dict = True
__UpperCAmelCase : Union[str, Any] = True
__UpperCAmelCase : Tuple = True
__UpperCAmelCase : Any = 0.763141
__UpperCAmelCase : Optional[Any] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "TABFACT":
__UpperCAmelCase : Union[str, Any] = TapasForSequenceClassification(config=UpperCamelCase )
elif task == "MLM":
__UpperCAmelCase : Tuple = TapasForMaskedLM(config=UpperCamelCase )
elif task == "INTERMEDIATE_PRETRAINING":
__UpperCAmelCase : List[str] = TapasModel(config=UpperCamelCase )
else:
raise ValueError(f"Task {task} not supported." )
print(f"Building PyTorch model from configuration: {config}" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(UpperCamelCase , UpperCamelCase , UpperCamelCase )
# Save pytorch-model (weights and configuration)
print(f"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(UpperCamelCase )
# Save tokenizer files
print(f"Save tokenizer files to {pytorch_dump_path}" )
__UpperCAmelCase : str = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt" , model_max_length=512 )
tokenizer.save_pretrained(UpperCamelCase )
print("Used relative position embeddings:" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
)
parser.add_argument(
"""--reset_position_index_per_cell""",
default=False,
action="""store_true""",
help="""Whether to use relative position embeddings or not. Defaults to True.""",
)
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--tapas_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained TAPAS model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 77 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import List
from unittest.mock import Mock
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class a__ ( __magic_name__ ):
def __init__( self : Tuple , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : int = data
def __iter__( self : Optional[int]):
"""simple docstring"""
for element in self.data:
yield element
def _UpperCamelCase ( UpperCamelCase=True ) -> Optional[Any]:
"""simple docstring"""
__UpperCAmelCase : str = Accelerator(even_batches=UpperCamelCase )
assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
return accelerator
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = False ) -> Tuple:
"""simple docstring"""
if iterable:
__UpperCAmelCase : Dict = DummyIterableDataset(torch.as_tensor(range(UpperCamelCase ) ) )
else:
__UpperCAmelCase : List[Any] = TensorDataset(torch.as_tensor(range(UpperCamelCase ) ) )
__UpperCAmelCase : List[Any] = DataLoader(UpperCamelCase , batch_size=UpperCamelCase )
__UpperCAmelCase : Optional[Any] = accelerator.prepare(UpperCamelCase )
return dl
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , ) -> str:
"""simple docstring"""
__UpperCAmelCase : List[Any] = create_dataloader(accelerator=UpperCamelCase , dataset_size=UpperCamelCase , batch_size=UpperCamelCase )
__UpperCAmelCase : List[str] = [len(batch[0] ) for batch in dl]
if accelerator.process_index == 0:
assert batch_sizes == process_0_expected_batch_sizes
elif accelerator.process_index == 1:
assert batch_sizes == process_1_expected_batch_sizes
def _UpperCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = create_accelerator()
# without padding, we would expect a different number of batches
verify_dataloader_batch_sizes(
UpperCamelCase , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1, 1] , )
# without padding, we would expect the same number of batches, but different sizes
verify_dataloader_batch_sizes(
UpperCamelCase , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 2] , )
def _UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : Any = create_accelerator(even_batches=UpperCamelCase )
verify_dataloader_batch_sizes(
UpperCamelCase , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1] , )
verify_dataloader_batch_sizes(
UpperCamelCase , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 1] , )
def _UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = create_accelerator(even_batches=UpperCamelCase )
__UpperCAmelCase : Dict = torch.nn.Linear(1 , 1 )
__UpperCAmelCase : Tuple = accelerator.prepare(UpperCamelCase )
__UpperCAmelCase : Any = create_dataloader(UpperCamelCase , dataset_size=3 , batch_size=1 )
__UpperCAmelCase : Optional[int] = []
with accelerator.join_uneven_inputs([ddp_model] ):
for batch_idx, batch in enumerate(UpperCamelCase ):
__UpperCAmelCase : Tuple = ddp_model(batch[0].float() )
__UpperCAmelCase : List[str] = output.sum()
loss.backward()
batch_idxs.append(UpperCamelCase )
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
assert batch_idxs == [0, 1]
elif accelerator.process_index == 1:
assert batch_idxs == [0]
def _UpperCamelCase ( UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
with warnings.catch_warnings(record=UpperCamelCase ) as w:
with accelerator.join_uneven_inputs([Mock()] ):
pass
assert issubclass(w[-1].category , UpperCamelCase )
assert "only supported for multi-GPU" in str(w[-1].message )
def _UpperCamelCase ( ) -> Tuple:
"""simple docstring"""
__UpperCAmelCase : int = True
__UpperCAmelCase : Optional[int] = False
__UpperCAmelCase : Any = create_accelerator(even_batches=UpperCamelCase )
__UpperCAmelCase : int = torch.nn.Linear(1 , 1 )
__UpperCAmelCase : int = accelerator.prepare(UpperCamelCase )
__UpperCAmelCase : Optional[Any] = create_dataloader(UpperCamelCase , dataset_size=3 , batch_size=1 )
__UpperCAmelCase : Any = create_dataloader(UpperCamelCase , dataset_size=3 , batch_size=1 )
with accelerator.join_uneven_inputs([ddp_model] , even_batches=UpperCamelCase ):
__UpperCAmelCase : Tuple = train_dl.batch_sampler.even_batches
__UpperCAmelCase : Optional[int] = valid_dl.batch_sampler.even_batches
assert train_dl_overridden_value == overridden_even_batches
assert valid_dl_overridden_value == overridden_even_batches
assert train_dl.batch_sampler.even_batches == default_even_batches
assert valid_dl.batch_sampler.even_batches == default_even_batches
def _UpperCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = True
__UpperCAmelCase : Any = False
__UpperCAmelCase : str = create_accelerator(even_batches=UpperCamelCase )
__UpperCAmelCase : Any = torch.nn.Linear(1 , 1 )
__UpperCAmelCase : Union[str, Any] = accelerator.prepare(UpperCamelCase )
create_dataloader(UpperCamelCase , dataset_size=3 , batch_size=1 , iterable=UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = create_dataloader(UpperCamelCase , dataset_size=3 , batch_size=1 )
with warnings.catch_warnings():
warnings.filterwarnings("ignore" )
try:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=UpperCamelCase ):
__UpperCAmelCase : Any = batch_dl.batch_sampler.even_batches
except AttributeError:
# ensure attribute error is not raised when processing iterable dl
raise AssertionError
assert batch_dl_overridden_value == overridden_even_batches
assert batch_dl.batch_sampler.even_batches == default_even_batches
def _UpperCamelCase ( ) -> Tuple:
"""simple docstring"""
__UpperCAmelCase : Optional[int] = create_accelerator()
__UpperCAmelCase : str = torch.nn.Linear(1 , 1 )
__UpperCAmelCase : Union[str, Any] = accelerator.prepare(UpperCamelCase )
create_dataloader(UpperCamelCase , dataset_size=3 , batch_size=1 , iterable=UpperCamelCase )
with warnings.catch_warnings(record=UpperCamelCase ) as w:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=UpperCamelCase ):
pass
assert issubclass(w[-1].category , UpperCamelCase )
assert "only supported for map-style datasets" in str(w[-1].message )
def _UpperCamelCase ( ) -> Any:
"""simple docstring"""
__UpperCAmelCase : int = create_accelerator()
accelerator.print("Test that even_batches variable ensures uniform batches across processes" )
test_default_ensures_even_batch_sizes()
accelerator.print("Run tests with even_batches disabled" )
test_can_disable_even_batches()
accelerator.print("Test joining uneven inputs" )
test_can_join_uneven_inputs()
accelerator.print("Test overriding even_batches when joining uneven inputs" )
test_join_can_override_even_batches()
accelerator.print("Test overriding even_batches for mixed dataloader types" )
test_join_can_override_for_mixed_type_dataloaders()
accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders" )
test_join_raises_warning_for_iterable_when_overriding_even_batches()
accelerator.print("Test join with non DDP distributed raises warning" )
__UpperCAmelCase : Optional[Any] = accelerator.state.distributed_type
__UpperCAmelCase : str = DistributedType.FSDP
test_join_raises_warning_for_non_ddp_distributed(UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = original_state
if __name__ == "__main__":
main()
| 77 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = "cpu" , UpperCamelCase = None ) -> None:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = torch.load(UpperCamelCase , map_location=UpperCamelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(UpperCamelCase , torch.Tensor ):
raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin" )
__UpperCAmelCase : Optional[Any] = v.half()
if save_path is None: # overwrite src_path
__UpperCAmelCase : str = src_path
torch.save(UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 77 | 1 |
"""simple docstring"""
from __future__ import annotations
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Tuple:
"""simple docstring"""
# Checks if the entire collection has been sorted
if len(UpperCamelCase ) <= 1 or n <= 1:
return
insert_next(UpperCamelCase , n - 1 )
rec_insertion_sort(UpperCamelCase , n - 1 )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Dict:
"""simple docstring"""
# Checks order between adjacent elements
if index >= len(UpperCamelCase ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
__UpperCAmelCase , __UpperCAmelCase : Dict = (
collection[index],
collection[index - 1],
)
insert_next(UpperCamelCase , index + 1 )
if __name__ == "__main__":
A = input("""Enter integers separated by spaces: """)
A = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| 77 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = df.shape[:1][0]
# If you're using some other dataset input the target column
A = df.iloc[:, 1:2]
A = actual_data.values.reshape(len_data, 1)
A = MinMaxScaler().fit_transform(actual_data)
A = 10
A = 5
A = 20
A = len_data - periods * look_back
A = actual_data[:division]
A = actual_data[division - look_back :]
A , A = [], []
A , A = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
A = np.array(train_x)
A = np.array(test_x)
A = np.array([list(i.ravel()) for i in train_y])
A = np.array([list(i.ravel()) for i in test_y])
A = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="""mean_squared_error""", optimizer="""adam""")
A = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
A = model.predict(x_test)
| 77 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
A = """3"""
print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""", torch.cuda.is_available())
print("""Cuda version:""", torch.version.cuda)
print("""CuDNN version:""", torch.backends.cudnn.version())
print("""Number of GPUs available:""", torch.cuda.device_count())
print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
print("""Torch version:""", None)
try:
import deepspeed
print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
print("""DeepSpeed version:""", None)
try:
import tensorflow as tf
print("""TensorFlow version:""", tf.__version__)
print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
print("""TensorFlow version:""", None)
| 77 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
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__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = MBartTokenizer
lowercase_ = MBartTokenizerFast
lowercase_ = True
lowercase_ = True
def a_ ( self : str):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__UpperCAmelCase : Any = MBartTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_)
tokenizer.save_pretrained(self.tmpdirname)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Dict = MBartTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = tokenizer.tokenize("This is a test")
self.assertListEqual(UpperCamelCase_ , ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCamelCase_) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__UpperCAmelCase : List[Any] = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
UpperCamelCase_ , [
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 : Any = tokenizer.convert_tokens_to_ids(UpperCamelCase_)
self.assertListEqual(
UpperCamelCase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(UpperCamelCase_)
self.assertListEqual(
UpperCamelCase_ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def a_ ( self : Dict):
"""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 : Dict = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
__UpperCAmelCase : List[str] = self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : int = self.tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : int = tempfile.mkdtemp()
__UpperCAmelCase : Optional[int] = tokenizer_r.save_pretrained(UpperCamelCase_)
__UpperCAmelCase : Any = tokenizer_p.save_pretrained(UpperCamelCase_)
# 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(UpperCamelCase_ , UpperCamelCase_)
# Checks everything loads correctly in the same way
__UpperCAmelCase : int = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : Tuple = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCamelCase_)
# Save tokenizer rust, legacy_format=True
__UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
__UpperCAmelCase : Dict = tokenizer_r.save_pretrained(UpperCamelCase_ , legacy_format=UpperCamelCase_)
__UpperCAmelCase : int = tokenizer_p.save_pretrained(UpperCamelCase_)
# Checks it save with the same files
self.assertSequenceEqual(UpperCamelCase_ , UpperCamelCase_)
# Checks everything loads correctly in the same way
__UpperCAmelCase : int = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
shutil.rmtree(UpperCamelCase_)
# Save tokenizer rust, legacy_format=False
__UpperCAmelCase : Tuple = tempfile.mkdtemp()
__UpperCAmelCase : int = tokenizer_r.save_pretrained(UpperCamelCase_ , legacy_format=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = tokenizer_p.save_pretrained(UpperCamelCase_)
# 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 : Optional[Any] = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : str = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
shutil.rmtree(UpperCamelCase_)
@require_torch
@require_sentencepiece
@require_tokenizers
class a__ ( unittest.TestCase ):
lowercase_ = "facebook/mbart-large-en-ro"
lowercase_ = [
" 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.",
]
lowercase_ = [
"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.",
]
lowercase_ = [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]
@classmethod
def a_ ( cls : int):
"""simple docstring"""
__UpperCAmelCase : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en_XX" , tgt_lang="ro_RO")
__UpperCAmelCase : Union[str, Any] = 1
return cls
def a_ ( self : List[Any]):
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] , 250001)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] , 250004)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] , 250020)
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase_)
def a_ ( self : Optional[int]):
"""simple docstring"""
self.assertIn(UpperCamelCase_ , self.tokenizer.all_special_ids)
__UpperCAmelCase : Union[str, Any] = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
__UpperCAmelCase : Optional[Any] = self.tokenizer.decode(UpperCamelCase_ , skip_special_tokens=UpperCamelCase_)
__UpperCAmelCase : int = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , UpperCamelCase_)
self.assertNotIn(self.tokenizer.eos_token , UpperCamelCase_)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , UpperCamelCase_)
__UpperCAmelCase : Tuple = 10
__UpperCAmelCase : List[Any] = self.tokenizer(UpperCamelCase_ , max_length=UpperCamelCase_ , truncation=UpperCamelCase_).input_ids[0]
self.assertEqual(ids[-2] , 2)
self.assertEqual(ids[-1] , UpperCamelCase_)
self.assertEqual(len(UpperCamelCase_) , UpperCamelCase_)
def a_ ( self : Any):
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]) , [250026, 250001])
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = tempfile.mkdtemp()
__UpperCAmelCase : Union[str, Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCamelCase_)
__UpperCAmelCase : List[Any] = MBartTokenizer.from_pretrained(UpperCamelCase_)
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , UpperCamelCase_)
@require_torch
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCamelCase_ , 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][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=len(self.expected_src_tokens) , return_tensors="pt" , )
__UpperCAmelCase : Tuple = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id)
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_)
self.assertEqual((2, 14) , batch.input_ids.shape)
self.assertEqual((2, 14) , batch.attention_mask.shape)
__UpperCAmelCase : List[str] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase_)
self.assertEqual(2 , batch.decoder_input_ids[0, -1]) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [])
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE])
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : List[str] = self.tokenizer(self.src_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=3 , return_tensors="pt")
__UpperCAmelCase : Any = self.tokenizer(
text_target=self.tgt_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=10 , return_tensors="pt")
__UpperCAmelCase : int = targets["input_ids"]
__UpperCAmelCase : Any = shift_tokens_right(UpperCamelCase_ , self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1] , 3)
self.assertEqual(batch.decoder_input_ids.shape[1] , 10)
@require_torch
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : int = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="ar_AR")
self.assertEqual(
nested_simplify(UpperCamelCase_) , {
# A, test, EOS, en_XX
"input_ids": [[62, 3034, 2, 250004]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 250001,
} , )
| 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( ) -> int:
"""simple docstring"""
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(UpperCamelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 77 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
class a__ :
def __init__( self : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = None
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.head
while temp is not None:
print(temp.data , end=" ")
__UpperCAmelCase : Tuple = temp.next
print()
def a_ ( self : int , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : List[str] = Node(UpperCamelCase_)
__UpperCAmelCase : str = self.head
__UpperCAmelCase : Optional[int] = new_node
def a_ ( self : str , UpperCamelCase_ : List[Any] , UpperCamelCase_ : str):
"""simple docstring"""
if node_data_a == node_data_a:
return
else:
__UpperCAmelCase : int = self.head
while node_a is not None and node_a.data != node_data_a:
__UpperCAmelCase : Tuple = node_a.next
__UpperCAmelCase : List[Any] = self.head
while node_a is not None and node_a.data != node_data_a:
__UpperCAmelCase : Optional[Any] = node_a.next
if node_a is None or node_a is None:
return
__UpperCAmelCase , __UpperCAmelCase : Any = 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()
| 77 | 1 |
"""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,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
A = logging.get_logger(__name__)
def _UpperCamelCase ( UpperCamelCase ) -> List[List[ImageInput]]:
"""simple docstring"""
if isinstance(UpperCamelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(UpperCamelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(UpperCamelCase ):
return [[videos]]
raise ValueError(f"Could not make batched video from {videos}" )
class a__ ( __magic_name__ ):
lowercase_ = ["pixel_values"]
def __init__( self : Optional[int] , UpperCamelCase_ : bool = True , UpperCamelCase_ : Dict[str, int] = None , UpperCamelCase_ : PILImageResampling = PILImageResampling.BILINEAR , UpperCamelCase_ : bool = True , UpperCamelCase_ : Dict[str, int] = None , UpperCamelCase_ : bool = True , UpperCamelCase_ : Union[int, float] = 1 / 255 , UpperCamelCase_ : bool = True , UpperCamelCase_ : Optional[Union[float, List[float]]] = None , UpperCamelCase_ : Optional[Union[float, List[float]]] = None , **UpperCamelCase_ : int , ):
"""simple docstring"""
super().__init__(**UpperCamelCase_)
__UpperCAmelCase : Dict = size if size is not None else {"shortest_edge": 224}
__UpperCAmelCase : Any = get_size_dict(UpperCamelCase_ , default_to_square=UpperCamelCase_)
__UpperCAmelCase : Any = crop_size if crop_size is not None else {"height": 224, "width": 224}
__UpperCAmelCase : Tuple = get_size_dict(UpperCamelCase_ , param_name="crop_size")
__UpperCAmelCase : int = do_resize
__UpperCAmelCase : Tuple = size
__UpperCAmelCase : str = do_center_crop
__UpperCAmelCase : str = crop_size
__UpperCAmelCase : List[Any] = resample
__UpperCAmelCase : List[Any] = do_rescale
__UpperCAmelCase : int = rescale_factor
__UpperCAmelCase : Any = do_normalize
__UpperCAmelCase : Optional[int] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__UpperCAmelCase : List[str] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self : Union[str, Any] , UpperCamelCase_ : np.ndarray , UpperCamelCase_ : Dict[str, int] , UpperCamelCase_ : PILImageResampling = PILImageResampling.BILINEAR , UpperCamelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase_ : Dict , ):
"""simple docstring"""
__UpperCAmelCase : int = get_size_dict(UpperCamelCase_ , default_to_square=UpperCamelCase_)
if "shortest_edge" in size:
__UpperCAmelCase : Optional[int] = get_resize_output_image_size(UpperCamelCase_ , size["shortest_edge"] , default_to_square=UpperCamelCase_)
elif "height" in size and "width" in size:
__UpperCAmelCase : List[str] = (size["height"], size["width"])
else:
raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
return resize(UpperCamelCase_ , size=UpperCamelCase_ , resample=UpperCamelCase_ , data_format=UpperCamelCase_ , **UpperCamelCase_)
def a_ ( self : Dict , UpperCamelCase_ : np.ndarray , UpperCamelCase_ : Dict[str, int] , UpperCamelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase_ : Tuple , ):
"""simple docstring"""
__UpperCAmelCase : int = get_size_dict(UpperCamelCase_)
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}")
return center_crop(UpperCamelCase_ , size=(size["height"], size["width"]) , data_format=UpperCamelCase_ , **UpperCamelCase_)
def a_ ( self : int , UpperCamelCase_ : np.ndarray , UpperCamelCase_ : Union[int, float] , UpperCamelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase_ : str , ):
"""simple docstring"""
return rescale(UpperCamelCase_ , scale=UpperCamelCase_ , data_format=UpperCamelCase_ , **UpperCamelCase_)
def a_ ( self : Dict , UpperCamelCase_ : np.ndarray , UpperCamelCase_ : Union[float, List[float]] , UpperCamelCase_ : Union[float, List[float]] , UpperCamelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase_ : List[str] , ):
"""simple docstring"""
return normalize(UpperCamelCase_ , mean=UpperCamelCase_ , std=UpperCamelCase_ , data_format=UpperCamelCase_ , **UpperCamelCase_)
def a_ ( self : Optional[int] , UpperCamelCase_ : ImageInput , UpperCamelCase_ : bool = None , UpperCamelCase_ : Dict[str, int] = None , UpperCamelCase_ : PILImageResampling = None , UpperCamelCase_ : bool = None , UpperCamelCase_ : Dict[str, int] = None , UpperCamelCase_ : bool = None , UpperCamelCase_ : float = None , UpperCamelCase_ : bool = None , UpperCamelCase_ : Optional[Union[float, List[float]]] = None , UpperCamelCase_ : Optional[Union[float, List[float]]] = None , UpperCamelCase_ : Optional[ChannelDimension] = ChannelDimension.FIRST , ):
"""simple docstring"""
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True.")
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True.")
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True.")
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True.")
# All transformations expect numpy arrays.
__UpperCAmelCase : Any = to_numpy_array(UpperCamelCase_)
if do_resize:
__UpperCAmelCase : Optional[Any] = self.resize(image=UpperCamelCase_ , size=UpperCamelCase_ , resample=UpperCamelCase_)
if do_center_crop:
__UpperCAmelCase : Dict = self.center_crop(UpperCamelCase_ , size=UpperCamelCase_)
if do_rescale:
__UpperCAmelCase : List[Any] = self.rescale(image=UpperCamelCase_ , scale=UpperCamelCase_)
if do_normalize:
__UpperCAmelCase : Optional[Any] = self.normalize(image=UpperCamelCase_ , mean=UpperCamelCase_ , std=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = to_channel_dimension_format(UpperCamelCase_ , UpperCamelCase_)
return image
def a_ ( self : Optional[Any] , UpperCamelCase_ : ImageInput , UpperCamelCase_ : bool = None , UpperCamelCase_ : Dict[str, int] = None , UpperCamelCase_ : PILImageResampling = None , UpperCamelCase_ : bool = None , UpperCamelCase_ : Dict[str, int] = None , UpperCamelCase_ : bool = None , UpperCamelCase_ : float = None , UpperCamelCase_ : bool = None , UpperCamelCase_ : Optional[Union[float, List[float]]] = None , UpperCamelCase_ : Optional[Union[float, List[float]]] = None , UpperCamelCase_ : Optional[Union[str, TensorType]] = None , UpperCamelCase_ : ChannelDimension = ChannelDimension.FIRST , **UpperCamelCase_ : Dict , ):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = do_resize if do_resize is not None else self.do_resize
__UpperCAmelCase : List[str] = 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 : str = do_rescale if do_rescale is not None else self.do_rescale
__UpperCAmelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
__UpperCAmelCase : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
__UpperCAmelCase : Optional[Any] = image_mean if image_mean is not None else self.image_mean
__UpperCAmelCase : Optional[int] = image_std if image_std is not None else self.image_std
__UpperCAmelCase : Tuple = size if size is not None else self.size
__UpperCAmelCase : Dict = get_size_dict(UpperCamelCase_ , default_to_square=UpperCamelCase_)
__UpperCAmelCase : int = crop_size if crop_size is not None else self.crop_size
__UpperCAmelCase : Dict = get_size_dict(UpperCamelCase_ , param_name="crop_size")
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.")
__UpperCAmelCase : Tuple = make_batched(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = [
[
self._preprocess_image(
image=UpperCamelCase_ , do_resize=UpperCamelCase_ , size=UpperCamelCase_ , resample=UpperCamelCase_ , do_center_crop=UpperCamelCase_ , crop_size=UpperCamelCase_ , do_rescale=UpperCamelCase_ , rescale_factor=UpperCamelCase_ , do_normalize=UpperCamelCase_ , image_mean=UpperCamelCase_ , image_std=UpperCamelCase_ , data_format=UpperCamelCase_ , )
for img in video
]
for video in videos
]
__UpperCAmelCase : Optional[Any] = {"pixel_values": videos}
return BatchFeature(data=UpperCamelCase_ , tensor_type=UpperCamelCase_)
| 77 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
A = """
Human: <<task>>
Assistant: """
A = """huggingface-tools/default-prompts"""
A = {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase="run" ) -> List[str]:
"""simple docstring"""
if prompt_or_repo_id is None:
__UpperCAmelCase : Optional[int] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , UpperCamelCase ) is not None:
return prompt_or_repo_id
__UpperCAmelCase : str = cached_file(
UpperCamelCase , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(UpperCamelCase , "r" , encoding="utf-8" ) as f:
return f.read()
| 77 | 1 |
"""simple docstring"""
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer
from ...test_tokenization_common import TokenizerTesterMixin
@require_phonemizer
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = WavaVecaPhonemeCTCTokenizer
lowercase_ = False
def a_ ( self : Optional[Any]):
"""simple docstring"""
super().setUp()
__UpperCAmelCase : str = (
"<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
"əʊ S ɡʲ onɡ2 u\" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ "
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
__UpperCAmelCase : Union[str, Any] = dict(zip(UpperCamelCase_ , range(len(UpperCamelCase_))))
__UpperCAmelCase : Any = {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
__UpperCAmelCase : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file , "w" , encoding="utf-8") as fp:
fp.write(json.dumps(UpperCamelCase_) + "\n")
def a_ ( self : Tuple , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[Any]=False , UpperCamelCase_ : Any=20 , UpperCamelCase_ : Optional[int]=5):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = [(i, tokenizer.decode([i] , clean_up_tokenization_spaces=UpperCamelCase_)) for i in range(len(UpperCamelCase_))]
__UpperCAmelCase : List[str] = list(filter(lambda UpperCamelCase_: [t[0]] == tokenizer.encode(t[1] , do_phonemize=UpperCamelCase_) , UpperCamelCase_))
if max_length is not None and len(UpperCamelCase_) > max_length:
__UpperCAmelCase : Optional[int] = toks[:max_length]
if min_length is not None and len(UpperCamelCase_) < min_length and len(UpperCamelCase_) > 0:
while len(UpperCamelCase_) < min_length:
__UpperCAmelCase : Optional[int] = toks + toks
# toks_str = [t[1] for t in toks]
__UpperCAmelCase : Tuple = [t[0] for t in toks]
# Ensure consistency
__UpperCAmelCase : Union[str, Any] = tokenizer.decode(UpperCamelCase_ , clean_up_tokenization_spaces=UpperCamelCase_)
if " " not in output_txt and len(UpperCamelCase_) > 1:
__UpperCAmelCase : Optional[Any] = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=UpperCamelCase_)
+ " "
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=UpperCamelCase_)
)
if with_prefix_space:
__UpperCAmelCase : str = " " + output_txt
__UpperCAmelCase : str = tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_)
return output_txt, output_ids
def a_ ( self : Optional[int] , **UpperCamelCase_ : Dict):
"""simple docstring"""
kwargs.update(self.special_tokens_map)
return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase_)
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
# check adding a single token
tokenizer.add_tokens("xxx")
__UpperCAmelCase : Dict = tokenizer("m xxx ɪ" , do_phonemize=UpperCamelCase_).input_ids
self.assertEqual(UpperCamelCase_ , [13, 392, 17]) # xxx should be last token
tokenizer.add_tokens(["aaa", "bbb", "ccc"])
__UpperCAmelCase : List[Any] = tokenizer("m aaa ɪ ccc" , do_phonemize=UpperCamelCase_).input_ids
self.assertEqual(UpperCamelCase_ , [13, 393, 17, 395]) # aaa and ccc should be after xxx and 2 after aaa
__UpperCAmelCase : int = tokenizer("maɪ c" , do_phonemize=UpperCamelCase_).input_ids
self.assertEqual(UpperCamelCase_ , [3, 200]) # mai should be <unk> (=3)
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
__UpperCAmelCase : int = "Hello how are you"
__UpperCAmelCase : Optional[Any] = tokenizer.phonemize(UpperCamelCase_ , phonemizer_lang="en-us")
self.assertEqual(UpperCamelCase_ , "h ə l oʊ h aʊ ɑːɹ j uː")
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
__UpperCAmelCase : int = "Hello how are you"
__UpperCAmelCase : Optional[int] = tokenizer.phonemize(UpperCamelCase_ , phonemizer_lang="en-us")
self.assertEqual(tokenizer(UpperCamelCase_).input_ids , tokenizer(UpperCamelCase_ , do_phonemize=UpperCamelCase_).input_ids)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Any = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
__UpperCAmelCase : Dict = "Hello how are you"
__UpperCAmelCase : List[str] = tokenizer.phonemize(UpperCamelCase_ , phonemizer_lang="en-us")
__UpperCAmelCase : List[Any] = tokenizer.decode(tokenizer(UpperCamelCase_).input_ids)
self.assertEqual(UpperCamelCase_ , UpperCamelCase_)
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
__UpperCAmelCase : int = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
[24, 22, 5, 24, 22, 5, 77],
]
__UpperCAmelCase : Any = tokenizer.decode(sample_ids[0])
__UpperCAmelCase : Optional[int] = tokenizer.batch_decode(UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , batch_tokens[0])
self.assertEqual(UpperCamelCase_ , ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"])
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|")
tokenizer.add_tokens("|")
__UpperCAmelCase : Optional[Any] = "Hello how are you"
__UpperCAmelCase : Dict = tokenizer.phonemize(UpperCamelCase_ , phonemizer_lang="en-us")
self.assertEqual(UpperCamelCase_ , "h ə l oʊ | h aʊ | ɑːɹ | j uː |")
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|")
tokenizer.add_tokens("|")
__UpperCAmelCase : int = "Hello how are you"
__UpperCAmelCase : List[Any] = tokenizer.phonemize(UpperCamelCase_ , phonemizer_lang="en-us")
self.assertEqual(tokenizer(UpperCamelCase_).input_ids , tokenizer(UpperCamelCase_ , do_phonemize=UpperCamelCase_).input_ids)
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : int = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|")
tokenizer.add_tokens("|")
# fmt: off
__UpperCAmelCase : Optional[int] = [
[11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
[tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
]
# fmt: on
# decode with word_del_token filter
__UpperCAmelCase : List[str] = tokenizer.decode(sample_ids[0])
__UpperCAmelCase : str = tokenizer.batch_decode(UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , batch_tokens[0])
self.assertEqual(UpperCamelCase_ , ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"])
# decode with no word_del_token filter
__UpperCAmelCase : List[Any] = tokenizer.decode(sample_ids[0] , filter_word_delimiter_token=UpperCamelCase_)
__UpperCAmelCase : str = tokenizer.batch_decode(UpperCamelCase_ , filter_word_delimiter_token=UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , batch_tokens[0])
self.assertEqual(UpperCamelCase_ , ["k s ɾ | ɾ l | ɭʲ", "| j ð | s j ð s oːɹ"])
def a_ ( self : str):
"""simple docstring"""
__UpperCAmelCase : int = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|")
tokenizer.add_tokens("|")
__UpperCAmelCase : List[str] = "Hello how are you"
__UpperCAmelCase : str = tokenizer.phonemize(UpperCamelCase_ , phonemizer_lang="en-us")
__UpperCAmelCase : Dict = tokenizer.decode(tokenizer(UpperCamelCase_).input_ids , filter_word_delimiter_token=UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , UpperCamelCase_)
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Any = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|")
tokenizer.add_tokens("|")
__UpperCAmelCase : List[str] = "Hello how are you"
__UpperCAmelCase : List[str] = tokenizer.phonemize(UpperCamelCase_ , phonemizer_lang="en-us")
__UpperCAmelCase : Tuple = tokenizer.decode(tokenizer(UpperCamelCase_).input_ids , filter_word_delimiter_token=UpperCamelCase_)
self.assertEqual(" ".join([p.strip() for p in phonemes.split(" |")]).strip() , UpperCamelCase_)
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Any = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token=UpperCamelCase_)
__UpperCAmelCase : Any = "Hello how are you"
__UpperCAmelCase : Optional[Any] = tokenizer(UpperCamelCase_ , phonemizer_lang="en-us").input_ids
__UpperCAmelCase : List[Any] = tokenizer(UpperCamelCase_ , phonemizer_lang="fr-fr").input_ids
self.assertNotEqual(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : List[str] = tokenizer.decode(UpperCamelCase_)
__UpperCAmelCase : Tuple = tokenizer.decode(UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , "h ə l oʊ h aʊ ɑːɹ j uː")
self.assertEqual(UpperCamelCase_ , "ɛ l o h aʊ a ʁ j u")
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
__UpperCAmelCase : List[Any] = "Hello how Are you"
__UpperCAmelCase : Tuple = "hello how are you"
__UpperCAmelCase : str = tokenizer(UpperCamelCase_).input_ids
__UpperCAmelCase : Dict = tokenizer(UpperCamelCase_).input_ids
self.assertEqual(UpperCamelCase_ , UpperCamelCase_)
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
tokenizer.add_tokens(["!", "?"])
tokenizer.add_special_tokens({"cls_token": "$$$"})
# fmt: off
__UpperCAmelCase : Any = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
[24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
]
# fmt: on
__UpperCAmelCase : str = tokenizer.batch_decode(UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , ["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"])
@staticmethod
def a_ ( UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = [d[key] for d in offsets]
return retrieved_list
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.get_tokenizer(word_delimiter_token="|")
tokenizer.add_tokens("|")
# fmt: off
# ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
__UpperCAmelCase : str = [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
# fmt: on
__UpperCAmelCase : Optional[int] = tokenizer.decode(UpperCamelCase_ , output_char_offsets=UpperCamelCase_ , filter_word_delimiter_token=UpperCamelCase_)
# check Wav2Vec2CTCTokenizerOutput keys for char
self.assertEqual(len(outputs.keys()) , 2)
self.assertTrue("text" in outputs)
self.assertTrue("char_offsets" in outputs)
self.assertTrue(isinstance(UpperCamelCase_ , UpperCamelCase_))
# check that order of chars is correct and identical for both outputs
self.assertEqual(" ".join(self.get_from_offsets(outputs["char_offsets"] , "char")) , outputs.text)
self.assertListEqual(
self.get_from_offsets(outputs["char_offsets"] , "char") , ["k", "s", "ɾ", "ɾ", "|", "ɾ", "l", "|", "ɭʲ"])
# check that offsets are actually correct for char
# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
self.assertListEqual(
self.get_from_offsets(outputs["char_offsets"] , "start_offset") , [0, 1, 4, 7, 9, 11, 12, 15, 16])
self.assertListEqual(
self.get_from_offsets(outputs["char_offsets"] , "end_offset") , [1, 4, 6, 9, 10, 12, 15, 16, 17])
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Any = self.get_tokenizer(word_delimiter_token="|")
def check_list_tuples_equal(UpperCamelCase_ : Optional[int] , UpperCamelCase_ : List[Any]):
self.assertTrue(isinstance(UpperCamelCase_ , UpperCamelCase_))
self.assertTrue(isinstance(outputs_list[0] , UpperCamelCase_))
# transform list to ModelOutput
__UpperCAmelCase : Optional[Any] = WavaVecaPhonemeCTCTokenizerOutput(
{k: [d[k] for d in outputs_list] for k in outputs_list[0]})
self.assertListEqual(outputs_batch["text"] , outputs_batch_a["text"])
def recursive_check(UpperCamelCase_ : List[Any] , UpperCamelCase_ : List[Any]):
if isinstance(UpperCamelCase_ , UpperCamelCase_):
[recursive_check(UpperCamelCase_ , UpperCamelCase_) for la, la in zip(UpperCamelCase_ , UpperCamelCase_)]
self.assertEqual(UpperCamelCase_ , UpperCamelCase_)
if "char_offsets" in outputs_batch:
recursive_check(outputs_batch["char_offsets"] , outputs_batch_a["char_offsets"])
# fmt: off
__UpperCAmelCase : Union[str, Any] = [
[11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
[24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
]
# fmt: on
# We assume that `decode` works as expected. All we will check now is
# the output type is correct and the output is identical to `decode`
# char
__UpperCAmelCase : List[Any] = tokenizer.batch_decode(UpperCamelCase_ , output_char_offsets=UpperCamelCase_)
__UpperCAmelCase : Any = [tokenizer.decode(UpperCamelCase_ , output_char_offsets=UpperCamelCase_) for ids in sample_ids]
check_list_tuples_equal(UpperCamelCase_ , UpperCamelCase_)
@unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes")
def a_ ( self : Any):
"""simple docstring"""
pass
@unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes")
def a_ ( self : Tuple):
"""simple docstring"""
pass
@unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency")
def a_ ( self : Any):
"""simple docstring"""
pass
@unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing")
def a_ ( self : int):
"""simple docstring"""
pass
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Any = self.get_tokenizers(do_lower_case=UpperCamelCase_)
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}"):
__UpperCAmelCase : Union[str, Any] = tokenizer.vocab_size
__UpperCAmelCase : Any = len(UpperCamelCase_)
self.assertNotEqual(UpperCamelCase_ , 0)
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
__UpperCAmelCase : Tuple = ["aaaaa bbbbbb", "cccccccccdddddddd"]
__UpperCAmelCase : Optional[Any] = tokenizer.add_tokens(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = tokenizer.vocab_size
__UpperCAmelCase : List[str] = len(UpperCamelCase_)
self.assertNotEqual(UpperCamelCase_ , 0)
self.assertEqual(UpperCamelCase_ , UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , len(UpperCamelCase_))
self.assertEqual(UpperCamelCase_ , all_size + len(UpperCamelCase_))
__UpperCAmelCase : List[str] = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l" , add_special_tokens=UpperCamelCase_)
self.assertGreaterEqual(len(UpperCamelCase_) , 4)
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1)
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1)
__UpperCAmelCase : List[Any] = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
__UpperCAmelCase : int = tokenizer.add_special_tokens(UpperCamelCase_)
__UpperCAmelCase : Optional[int] = tokenizer.vocab_size
__UpperCAmelCase : Optional[int] = len(UpperCamelCase_)
self.assertNotEqual(UpperCamelCase_ , 0)
self.assertEqual(UpperCamelCase_ , UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , len(UpperCamelCase_))
self.assertEqual(UpperCamelCase_ , all_size_a + len(UpperCamelCase_))
__UpperCAmelCase : List[str] = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l" , add_special_tokens=UpperCamelCase_)
self.assertGreaterEqual(len(UpperCamelCase_) , 6)
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1)
self.assertGreater(tokens[0] , tokens[1])
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1)
self.assertGreater(tokens[-3] , tokens[-4])
self.assertEqual(tokens[0] , tokenizer.eos_token_id)
self.assertEqual(tokens[-3] , tokenizer.pad_token_id)
@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode.")
def a_ ( self : List[str]):
"""simple docstring"""
pass
@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode.")
def a_ ( self : Tuple):
"""simple docstring"""
pass
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.get_tokenizers(fast=UpperCamelCase_ , do_lower_case=UpperCamelCase_)
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}"):
__UpperCAmelCase : Optional[int] = ["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
__UpperCAmelCase : List[str] = tokenizer.convert_tokens_to_string(UpperCamelCase_)
self.assertIsInstance(output["text"] , UpperCamelCase_)
| 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ErnieForCausalLM""",
"""ErnieForMaskedLM""",
"""ErnieForMultipleChoice""",
"""ErnieForNextSentencePrediction""",
"""ErnieForPreTraining""",
"""ErnieForQuestionAnswering""",
"""ErnieForSequenceClassification""",
"""ErnieForTokenClassification""",
"""ErnieModel""",
"""ErniePreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 | 1 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
A = """facebook/wmt19-en-de"""
A = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
A = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
A = FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')
# Test
A = tokenizer(["""Making tiny model"""], return_tensors="""pt""")
A = tiny_model(**batch)
print("""test output:""", len(outputs.logits[0]))
# Save
A = """tiny-wmt19-en-de"""
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(f'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 77 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ( nn.Module ):
def __init__( self : Union[str, Any]):
"""simple docstring"""
super().__init__()
__UpperCAmelCase : Optional[int] = nn.Linear(3 , 4)
__UpperCAmelCase : str = nn.BatchNormad(4)
__UpperCAmelCase : int = nn.Linear(4 , 5)
def a_ ( self : str , UpperCamelCase_ : List[str]):
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(UpperCamelCase_)))
class a__ ( unittest.TestCase ):
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = ModelForTest()
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , model.state_dict())
__UpperCAmelCase : Union[str, Any] = os.path.join(UpperCamelCase_ , "index.json")
self.assertTrue(os.path.isfile(UpperCamelCase_))
# TODO: add tests on what is inside the index
for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
__UpperCAmelCase : Optional[int] = os.path.join(UpperCamelCase_ , F"{key}.dat")
self.assertTrue(os.path.isfile(UpperCamelCase_))
# TODO: add tests on the fact weights are properly loaded
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : int = [torch.floataa, torch.floataa, torch.bfloataa]
for dtype in dtypes:
__UpperCAmelCase : List[Any] = torch.randn(2 , 3 , dtype=UpperCamelCase_)
with TemporaryDirectory() as tmp_dir:
__UpperCAmelCase : Tuple = offload_weight(UpperCamelCase_ , "weight" , UpperCamelCase_ , {})
__UpperCAmelCase : Dict = os.path.join(UpperCamelCase_ , "weight.dat")
self.assertTrue(os.path.isfile(UpperCamelCase_))
self.assertDictEqual(UpperCamelCase_ , {"weight": {"shape": [2, 3], "dtype": str(UpperCamelCase_).split(".")[1]}})
__UpperCAmelCase : Optional[Any] = load_offloaded_weight(UpperCamelCase_ , index["weight"])
self.assertTrue(torch.equal(UpperCamelCase_ , UpperCamelCase_))
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = ModelForTest()
__UpperCAmelCase : Optional[int] = model.state_dict()
__UpperCAmelCase : List[str] = {k: v for k, v in state_dict.items() if "linear2" not in k}
__UpperCAmelCase : Optional[int] = {k: v for k, v in state_dict.items() if "linear2" in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : List[str] = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
__UpperCAmelCase : Optional[int] = {k: v for k, v in state_dict.items() if "weight" in k}
__UpperCAmelCase : Optional[Any] = {k: v for k, v in state_dict.items() if "weight" not in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
# Duplicates are removed
__UpperCAmelCase : str = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Any = {"a.1": 0, "a.10": 1, "a.2": 2}
__UpperCAmelCase : Union[str, Any] = extract_submodules_state_dict(UpperCamelCase_ , ["a.1", "a.2"])
self.assertDictEqual(UpperCamelCase_ , {"a.1": 0, "a.2": 2})
__UpperCAmelCase : int = {"a.1.a": 0, "a.10.a": 1, "a.2.a": 2}
__UpperCAmelCase : int = extract_submodules_state_dict(UpperCamelCase_ , ["a.1", "a.2"])
self.assertDictEqual(UpperCamelCase_ , {"a.1.a": 0, "a.2.a": 2})
| 77 | 1 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A = logging.get_logger(__name__)
A = {"""vocab_file""": """spiece.model"""}
A = {
"""vocab_file""": {
"""AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
"""AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
"""AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
"""AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
"""AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
}
}
A = {
"""AI-Sweden/gpt-sw3-126m""": 2_048,
"""AI-Sweden/gpt-sw3-350m""": 2_048,
"""AI-Sweden/gpt-sw3-1.6b""": 2_048,
"""AI-Sweden/gpt-sw3-6.7b""": 2_048,
"""AI-Sweden/gpt-sw3-20b""": 2_048,
}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : Dict , UpperCamelCase_ : str , UpperCamelCase_ : Union[str, Any]=False , UpperCamelCase_ : Dict=False , UpperCamelCase_ : List[Any]=False , UpperCamelCase_ : Tuple=None , UpperCamelCase_ : str=None , UpperCamelCase_ : Dict=None , UpperCamelCase_ : Tuple=None , UpperCamelCase_ : Optional[Dict[str, Any]] = None , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
__UpperCAmelCase : Tuple = {} if sp_model_kwargs is None else sp_model_kwargs
__UpperCAmelCase : Dict = kwargs.get("name_or_path")
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored")
__UpperCAmelCase : Optional[int] = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__UpperCAmelCase : str = "<|endoftext|>" if eos_token is None else eos_token
__UpperCAmelCase : Optional[Any] = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__UpperCAmelCase : Dict = unk_token if pad_token is None else pad_token
__UpperCAmelCase : Any = eos_token if bos_token is None else bos_token
else:
__UpperCAmelCase : List[Any] = "<pad>" if pad_token is None else pad_token
__UpperCAmelCase : List[str] = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=UpperCamelCase_ , remove_space=UpperCamelCase_ , keep_accents=UpperCamelCase_ , bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase_ , )
__UpperCAmelCase : Optional[int] = do_lower_case
__UpperCAmelCase : List[str] = remove_space
__UpperCAmelCase : Any = keep_accents
__UpperCAmelCase : int = vocab_file
__UpperCAmelCase : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(UpperCamelCase_)
# Used for whitespace normalization in input texts
# fmt : off
__UpperCAmelCase : int = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__UpperCAmelCase : Dict = re.compile(
F"[{''.join(map(UpperCamelCase_ , list(range(0 , 9)) + list(range(11 , 32)) + list(range(127 , 160)) + [160, 173, 8203]))}]")
def __getstate__( self : Any):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.__dict__.copy()
__UpperCAmelCase : Optional[Any] = None
return state
def __setstate__( self : Union[str, Any] , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs"):
__UpperCAmelCase : Optional[Any] = {}
__UpperCAmelCase : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def a_ ( self : Any):
"""simple docstring"""
return len(self.sp_model)
def a_ ( self : Any , UpperCamelCase_ : str):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.non_printing_characters_re.sub("" , UpperCamelCase_)
# Normalize whitespaces
__UpperCAmelCase : List[Any] = "".join([char if char not in self.whitespaces else " " for char in text])
# NFC Unicode normalization
__UpperCAmelCase : Any = unicodedata.normalize("NFC" , UpperCamelCase_)
return text
def a_ ( self : Dict , UpperCamelCase_ : str , **UpperCamelCase_ : List[str]):
"""simple docstring"""
__UpperCAmelCase : List[str] = self.preprocess_text(UpperCamelCase_)
return self.sp_model.encode(UpperCamelCase_ , out_type=UpperCamelCase_)
def a_ ( self : Union[str, Any] , UpperCamelCase_ : str):
"""simple docstring"""
return self.sp_model.PieceToId(UpperCamelCase_)
def a_ ( self : List[str] , UpperCamelCase_ : int):
"""simple docstring"""
return self.sp_model.IdToPiece(UpperCamelCase_)
@staticmethod
def a_ ( UpperCamelCase_ : str):
"""simple docstring"""
return out_string
def a_ ( self : int , UpperCamelCase_ : List[str]):
"""simple docstring"""
__UpperCAmelCase : List[str] = []
__UpperCAmelCase : Tuple = ""
__UpperCAmelCase : Any = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(UpperCamelCase_) + token
__UpperCAmelCase : int = True
__UpperCAmelCase : Dict = []
else:
current_sub_tokens.append(UpperCamelCase_)
__UpperCAmelCase : List[Any] = False
out_string += self.sp_model.decode(UpperCamelCase_)
return out_string
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = {self.convert_ids_to_tokens(UpperCamelCase_): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def a_ ( self : Union[str, Any] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : List[str] = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , UpperCamelCase_)
elif not os.path.isfile(self.vocab_file):
with open(UpperCamelCase_ , "wb") as fi:
__UpperCAmelCase : Optional[int] = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase_)
return (out_vocab_file,)
def a_ ( self : Any , UpperCamelCase_ : Union[str, List[str]] , UpperCamelCase_ : Union[str, bool] = False):
"""simple docstring"""
if isinstance(UpperCamelCase_ , UpperCamelCase_):
__UpperCAmelCase : Any = self.preprocess_text(UpperCamelCase_)
__UpperCAmelCase : Any = self.sp_model.encode(UpperCamelCase_)
else:
__UpperCAmelCase : Dict = [self.preprocess_text(UpperCamelCase_) for t in text]
__UpperCAmelCase : List[Any] = self.sp_model.encode(UpperCamelCase_)
if return_tensors is True or return_tensors == "pt":
__UpperCAmelCase : Optional[Any] = torch.tensor(UpperCamelCase_)
return token_ids
def a_ ( self : int , UpperCamelCase_ : Union[int, List[int]]):
"""simple docstring"""
return self.sp_model.decode(UpperCamelCase_)
def a_ ( self : Tuple , UpperCamelCase_ : "Conversation"):
"""simple docstring"""
__UpperCAmelCase : int = [F"User: {text}" if is_user else F"Bot: {text}" for is_user, text in conversation.iter_texts()]
__UpperCAmelCase : Optional[Any] = (
F"{self.eos_token}{self.bos_token}" + F"{self.bos_token}".join(UpperCamelCase_) + F"{self.bos_token}Bot:"
)
return self.encode(text=UpperCamelCase_)
| 77 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelCase : Union[str, Any] = n - k
# Calculate C(n,k)
for i in range(UpperCamelCase ):
result *= n - i
result //= i + 1
return result
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
return binomial_coefficient(2 * node_count , UpperCamelCase ) // (node_count + 1)
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
if n < 0:
raise ValueError("factorial() not defined for negative values" )
__UpperCAmelCase : Optional[Any] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
return catalan_number(UpperCamelCase ) * factorial(UpperCamelCase )
if __name__ == "__main__":
A = int(input("""Enter the number of nodes: """).strip() or 0)
if node_count <= 0:
raise ValueError("""We need some nodes to work with.""")
print(
f'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
f'''binary trees and {catalan_number(node_count)} binary search trees.'''
)
| 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : List[Any] = [1]
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Any = 0, 0, 0
__UpperCAmelCase : Tuple = ugly_nums[ia] * 2
__UpperCAmelCase : List[Any] = ugly_nums[ia] * 3
__UpperCAmelCase : Optional[int] = ugly_nums[ia] * 5
for _ in range(1 , UpperCamelCase ):
__UpperCAmelCase : Union[str, Any] = min(UpperCamelCase , UpperCamelCase , UpperCamelCase )
ugly_nums.append(UpperCamelCase )
if next_num == next_a:
ia += 1
__UpperCAmelCase : Dict = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
__UpperCAmelCase : Optional[int] = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
__UpperCAmelCase : Tuple = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f'''{ugly_numbers(200) = }''')
| 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
"""processing_trocr""": ["""TrOCRProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TrOCRForCausalLM""",
"""TrOCRPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def _UpperCamelCase ( UpperCamelCase=None ) -> int:
"""simple docstring"""
if subparsers is not None:
__UpperCAmelCase : List[str] = subparsers.add_parser("test" )
else:
__UpperCAmelCase : Dict = argparse.ArgumentParser("Accelerate test command" )
parser.add_argument(
"--config_file" , default=UpperCamelCase , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=UpperCamelCase )
return parser
def _UpperCamelCase ( UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : Dict = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["test_utils", "scripts", "test_script.py"] )
if args.config_file is None:
__UpperCAmelCase : List[str] = script_name
else:
__UpperCAmelCase : int = f"--config_file={args.config_file} {script_name}"
__UpperCAmelCase : Dict = ["accelerate-launch"] + test_args.split()
__UpperCAmelCase : Tuple = execute_subprocess_async(UpperCamelCase , env=os.environ.copy() )
if result.returncode == 0:
print("Test is a success! You are ready for your distributed training!" )
def _UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
__UpperCAmelCase : List[Any] = test_command_parser()
__UpperCAmelCase : Any = parser.parse_args()
test_command(UpperCamelCase )
if __name__ == "__main__":
main()
| 77 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self : Optional[Any] , UpperCamelCase_ : int , UpperCamelCase_ : Any=13 , UpperCamelCase_ : Optional[int]=3 , UpperCamelCase_ : int=224 , UpperCamelCase_ : int=30 , UpperCamelCase_ : str=400 , UpperCamelCase_ : List[Any]=True , UpperCamelCase_ : Optional[Any]=None , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Optional[int]=[0.5, 0.5, 0.5] , UpperCamelCase_ : Optional[Any]=[0.5, 0.5, 0.5] , ):
"""simple docstring"""
__UpperCAmelCase : Tuple = size if size is not None else {"height": 18, "width": 18}
__UpperCAmelCase : List[Any] = parent
__UpperCAmelCase : Tuple = batch_size
__UpperCAmelCase : Tuple = num_channels
__UpperCAmelCase : List[Any] = image_size
__UpperCAmelCase : str = min_resolution
__UpperCAmelCase : Tuple = max_resolution
__UpperCAmelCase : Optional[Any] = do_resize
__UpperCAmelCase : Any = size
__UpperCAmelCase : Any = do_normalize
__UpperCAmelCase : Any = image_mean
__UpperCAmelCase : Optional[Any] = image_std
def a_ ( self : str):
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = ViTImageProcessor if is_vision_available() else None
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = EfficientFormerImageProcessorTester(self)
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
return self.image_proc_tester.prepare_image_processor_dict()
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(UpperCamelCase_ , "image_mean"))
self.assertTrue(hasattr(UpperCamelCase_ , "image_std"))
self.assertTrue(hasattr(UpperCamelCase_ , "do_normalize"))
self.assertTrue(hasattr(UpperCamelCase_ , "do_resize"))
self.assertTrue(hasattr(UpperCamelCase_ , "size"))
def a_ ( self : Dict):
"""simple docstring"""
pass
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
__UpperCAmelCase : str = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , Image.Image)
# Test not batched input
__UpperCAmelCase : Optional[int] = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Optional[int] = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : int = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
__UpperCAmelCase : Union[str, Any] = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_ , numpify=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , np.ndarray)
# Test not batched input
__UpperCAmelCase : Tuple = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Any = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
__UpperCAmelCase : Any = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_ , torchify=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , torch.Tensor)
# Test not batched input
__UpperCAmelCase : Optional[Any] = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Optional[int] = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
| 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : int = len(UpperCamelCase )
__UpperCAmelCase : int = len(UpperCamelCase )
__UpperCAmelCase : int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
__UpperCAmelCase : list = []
for char_count in range(UpperCamelCase ):
if char_count < first_str_length:
output_list.append(first_str[char_count] )
if char_count < second_str_length:
output_list.append(second_str[char_count] )
return "".join(UpperCamelCase )
if __name__ == "__main__":
print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)
| 77 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": from_to(0.76455, 1.30795),
"""cubicfoot""": from_to(0.028, 35.3147),
"""cup""": from_to(0.000236588, 4226.75),
}
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> float:
"""simple docstring"""
if from_type not in METRIC_CONVERSION:
raise ValueError(
f"Invalid 'from_type' value: {from_type!r} Supported values are:\n"
+ ", ".join(UpperCamelCase ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
f"Invalid 'to_type' value: {to_type!r}. Supported values are:\n"
+ ", ".join(UpperCamelCase ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 77 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ErnieForCausalLM""",
"""ErnieForMaskedLM""",
"""ErnieForMultipleChoice""",
"""ErnieForNextSentencePrediction""",
"""ErnieForPreTraining""",
"""ErnieForQuestionAnswering""",
"""ErnieForSequenceClassification""",
"""ErnieForTokenClassification""",
"""ErnieModel""",
"""ErniePreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = ShapEPipeline
lowercase_ = ["prompt"]
lowercase_ = ["prompt"]
lowercase_ = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
lowercase_ = False
@property
def a_ ( self : Optional[int]):
"""simple docstring"""
return 32
@property
def a_ ( self : Any):
"""simple docstring"""
return 32
@property
def a_ ( self : int):
"""simple docstring"""
return self.time_input_dim * 4
@property
def a_ ( self : List[Any]):
"""simple docstring"""
return 8
@property
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def a_ ( self : List[str]):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(UpperCamelCase_)
@property
def a_ ( self : Any):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Union[str, Any] = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
__UpperCAmelCase : Dict = PriorTransformer(**UpperCamelCase_)
return model
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Tuple = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
__UpperCAmelCase : List[Any] = ShapERenderer(**UpperCamelCase_)
return model
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.dummy_prior
__UpperCAmelCase : str = self.dummy_text_encoder
__UpperCAmelCase : int = self.dummy_tokenizer
__UpperCAmelCase : int = self.dummy_renderer
__UpperCAmelCase : Tuple = HeunDiscreteScheduler(
beta_schedule="exp" , num_train_timesteps=1024 , prediction_type="sample" , use_karras_sigmas=UpperCamelCase_ , clip_sample=UpperCamelCase_ , clip_sample_range=1.0 , )
__UpperCAmelCase : str = {
"prior": prior,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"renderer": renderer,
"scheduler": scheduler,
}
return components
def a_ ( self : Optional[int] , UpperCamelCase_ : Dict , UpperCamelCase_ : Any=0):
"""simple docstring"""
if str(UpperCamelCase_).startswith("mps"):
__UpperCAmelCase : List[Any] = torch.manual_seed(UpperCamelCase_)
else:
__UpperCAmelCase : str = torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)
__UpperCAmelCase : List[Any] = {
"prompt": "horse",
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "np",
}
return inputs
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : str = "cpu"
__UpperCAmelCase : Union[str, Any] = self.get_dummy_components()
__UpperCAmelCase : Union[str, Any] = self.pipeline_class(**UpperCamelCase_)
__UpperCAmelCase : Any = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = pipe(**self.get_dummy_inputs(UpperCamelCase_))
__UpperCAmelCase : Union[str, Any] = output.images[0]
__UpperCAmelCase : str = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__UpperCAmelCase : Union[str, Any] = np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def a_ ( self : Tuple):
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2])
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = torch_device == "cpu"
__UpperCAmelCase : Optional[Any] = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=UpperCamelCase_ , relax_max_difference=UpperCamelCase_ , )
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.get_dummy_components()
__UpperCAmelCase : List[str] = self.pipeline_class(**UpperCamelCase_)
__UpperCAmelCase : int = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = 1
__UpperCAmelCase : Any = 2
__UpperCAmelCase : Optional[Any] = self.get_dummy_inputs(UpperCamelCase_)
for key in inputs.keys():
if key in self.batch_params:
__UpperCAmelCase : List[Any] = batch_size * [inputs[key]]
__UpperCAmelCase : List[Any] = pipe(**UpperCamelCase_ , num_images_per_prompt=UpperCamelCase_)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def a_ ( self : List[str]):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Dict = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_np_out.npy")
__UpperCAmelCase : Optional[Any] = ShapEPipeline.from_pretrained("openai/shap-e")
__UpperCAmelCase : Any = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Dict = torch.Generator(device=UpperCamelCase_).manual_seed(0)
__UpperCAmelCase : int = pipe(
"a shark" , generator=UpperCamelCase_ , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type="np" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(UpperCamelCase_ , UpperCamelCase_)
| 77 | 1 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase=5 ) -> Optional[Any]:
"""simple docstring"""
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count("<mask>" ) == 1
__UpperCAmelCase : Union[str, Any] = torch.tensor(tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) ).unsqueeze(0 ) # Batch size 1
__UpperCAmelCase : Tuple = model(UpperCamelCase )[0] # The last hidden-state is the first element of the output tuple
__UpperCAmelCase : Tuple = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
__UpperCAmelCase : str = logits[0, masked_index, :]
__UpperCAmelCase : List[str] = logits.softmax(dim=0 )
__UpperCAmelCase , __UpperCAmelCase : int = prob.topk(k=UpperCamelCase , dim=0 )
__UpperCAmelCase : Optional[Any] = " ".join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(UpperCamelCase ) )] )
__UpperCAmelCase : Any = tokenizer.mask_token
__UpperCAmelCase : List[Any] = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" " ) ):
__UpperCAmelCase : Dict = predicted_token_bpe.replace("\u2581" , " " )
if " {0}".format(UpperCamelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(" {0}".format(UpperCamelCase ) , UpperCamelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(UpperCamelCase , UpperCamelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
A = CamembertTokenizer.from_pretrained("""camembert-base""")
A = CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()
A = """Le camembert est <mask> :)"""
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 77 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_features", "is_longer"]
def __init__( self : List[str] , UpperCamelCase_ : Dict=64 , UpperCamelCase_ : Tuple=48000 , UpperCamelCase_ : List[Any]=480 , UpperCamelCase_ : List[str]=10 , UpperCamelCase_ : str=1024 , UpperCamelCase_ : List[str]=0.0 , UpperCamelCase_ : Optional[int]=False , UpperCamelCase_ : float = 0 , UpperCamelCase_ : float = 14000 , UpperCamelCase_ : int = None , UpperCamelCase_ : str = "fusion" , UpperCamelCase_ : str = "repeatpad" , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
feature_size=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , padding_value=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Union[str, Any] = top_db
__UpperCAmelCase : Optional[Any] = truncation
__UpperCAmelCase : str = padding
__UpperCAmelCase : int = fft_window_size
__UpperCAmelCase : str = (fft_window_size >> 1) + 1
__UpperCAmelCase : List[Any] = hop_length
__UpperCAmelCase : Optional[Any] = max_length_s
__UpperCAmelCase : Tuple = max_length_s * sampling_rate
__UpperCAmelCase : str = sampling_rate
__UpperCAmelCase : int = frequency_min
__UpperCAmelCase : Optional[Any] = frequency_max
__UpperCAmelCase : Any = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCamelCase_ , min_frequency=UpperCamelCase_ , max_frequency=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , norm=UpperCamelCase_ , mel_scale="htk" , )
__UpperCAmelCase : Any = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCamelCase_ , min_frequency=UpperCamelCase_ , max_frequency=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , norm="slaney" , mel_scale="slaney" , )
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Dict = copy.deepcopy(self.__dict__)
__UpperCAmelCase : str = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def a_ ( self : int , UpperCamelCase_ : np.array , UpperCamelCase_ : Optional[np.array] = None):
"""simple docstring"""
__UpperCAmelCase : List[Any] = spectrogram(
UpperCamelCase_ , window_function(self.fft_window_size , "hann") , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=UpperCamelCase_ , log_mel="dB" , )
return log_mel_spectrogram.T
def a_ ( self : List[Any] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = np.array_split(list(range(0 , total_frames - chunk_frames + 1)) , 3)
if len(ranges[1]) == 0:
# if the audio is too short, we just use the first chunk
__UpperCAmelCase : str = [0]
if len(ranges[2]) == 0:
# if the audio is too short, we just use the first chunk
__UpperCAmelCase : Dict = [0]
# randomly choose index for each part
__UpperCAmelCase : Dict = np.random.choice(ranges[0])
__UpperCAmelCase : List[str] = np.random.choice(ranges[1])
__UpperCAmelCase : List[Any] = np.random.choice(ranges[2])
__UpperCAmelCase : List[Any] = mel[idx_front : idx_front + chunk_frames, :]
__UpperCAmelCase : List[str] = mel[idx_middle : idx_middle + chunk_frames, :]
__UpperCAmelCase : List[str] = mel[idx_back : idx_back + chunk_frames, :]
__UpperCAmelCase : Tuple = torch.tensor(mel[None, None, :])
__UpperCAmelCase : Union[str, Any] = torch.nn.functional.interpolate(
UpperCamelCase_ , size=[chunk_frames, 64] , mode="bilinear" , align_corners=UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = mel_shrink[0][0].numpy()
__UpperCAmelCase : Optional[int] = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0)
return mel_fusion
def a_ ( self : Optional[Any] , UpperCamelCase_ : np.array , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
__UpperCAmelCase : List[str] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
__UpperCAmelCase : List[Any] = len(UpperCamelCase_) - max_length
__UpperCAmelCase : int = np.random.randint(0 , overflow + 1)
__UpperCAmelCase : Union[str, Any] = waveform[idx : idx + max_length]
__UpperCAmelCase : Union[str, Any] = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters_slaney)[None, :]
elif truncation == "fusion":
__UpperCAmelCase : Any = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters)
__UpperCAmelCase : Dict = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
__UpperCAmelCase : Tuple = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
__UpperCAmelCase : List[str] = np.stack([mel, mel, mel, mel] , axis=0)
__UpperCAmelCase : Any = False
else:
__UpperCAmelCase : List[str] = self._random_mel_fusion(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = True
else:
raise NotImplementedError(F"data_truncating {truncation} not implemented")
else:
__UpperCAmelCase : Optional[Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
__UpperCAmelCase : Tuple = int(max_length / len(UpperCamelCase_))
__UpperCAmelCase : List[str] = np.stack(np.tile(UpperCamelCase_ , n_repeat + 1))[:max_length]
if padding == "repeatpad":
__UpperCAmelCase : Union[str, Any] = int(max_length / len(UpperCamelCase_))
__UpperCAmelCase : Optional[Any] = np.stack(np.tile(UpperCamelCase_ , UpperCamelCase_))
__UpperCAmelCase : int = np.pad(UpperCamelCase_ , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0)
if truncation == "fusion":
__UpperCAmelCase : Any = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters)
__UpperCAmelCase : List[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0)
else:
__UpperCAmelCase : Optional[int] = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters_slaney)[None, :]
return input_mel, longer
def __call__( self : Dict , UpperCamelCase_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , UpperCamelCase_ : str = None , UpperCamelCase_ : Optional[str] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[Union[str, TensorType]] = None , **UpperCamelCase_ : Any , ):
"""simple docstring"""
__UpperCAmelCase : int = truncation if truncation is not None else self.truncation
__UpperCAmelCase : Optional[Any] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
F" was sampled with {self.sampling_rate} and not {sampling_rate}.")
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug.")
__UpperCAmelCase : List[str] = isinstance(UpperCamelCase_ , np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(F"Only mono-channel audio is supported for input to {self}")
__UpperCAmelCase : str = is_batched_numpy or (
isinstance(UpperCamelCase_ , (list, tuple)) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)))
)
if is_batched:
__UpperCAmelCase : Dict = [np.asarray(UpperCamelCase_ , dtype=np.floataa) for speech in raw_speech]
elif not is_batched and not isinstance(UpperCamelCase_ , np.ndarray):
__UpperCAmelCase : Tuple = np.asarray(UpperCamelCase_ , dtype=np.floataa)
elif isinstance(UpperCamelCase_ , np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
__UpperCAmelCase : Optional[int] = raw_speech.astype(np.floataa)
# always return batch
if not is_batched:
__UpperCAmelCase : int = [np.asarray(UpperCamelCase_)]
# convert to mel spectrogram, truncate and pad if needed.
__UpperCAmelCase : Optional[int] = [
self._get_input_mel(UpperCamelCase_ , max_length if max_length else self.nb_max_samples , UpperCamelCase_ , UpperCamelCase_)
for waveform in raw_speech
]
__UpperCAmelCase : Tuple = []
__UpperCAmelCase : List[Any] = []
for mel, longer in padded_inputs:
input_mel.append(UpperCamelCase_)
is_longer.append(UpperCamelCase_)
if truncation == "fusion" and sum(UpperCamelCase_) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
__UpperCAmelCase : Any = np.random.randint(0 , len(UpperCamelCase_))
__UpperCAmelCase : Optional[int] = True
if isinstance(input_mel[0] , UpperCamelCase_):
__UpperCAmelCase : Tuple = [np.asarray(UpperCamelCase_ , dtype=np.floataa) for feature in input_mel]
# is_longer is a list of bool
__UpperCAmelCase : List[str] = [[longer] for longer in is_longer]
__UpperCAmelCase : Optional[int] = {"input_features": input_mel, "is_longer": is_longer}
__UpperCAmelCase : Optional[int] = BatchFeature(UpperCamelCase_)
if return_tensors is not None:
__UpperCAmelCase : Any = input_features.convert_to_tensors(UpperCamelCase_)
return input_features
| 77 | 1 |
"""simple docstring"""
# Algorithm for the pigeonhole sorting
def _UpperCamelCase ( UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : List[str] = min(UpperCamelCase ) # min() finds the minimum value
__UpperCAmelCase : List[str] = max(UpperCamelCase ) # max() finds the maximum value
__UpperCAmelCase : List[str] = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
__UpperCAmelCase : Dict = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(UpperCamelCase , UpperCamelCase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
__UpperCAmelCase : int = 0
for count in range(UpperCamelCase ):
while holes[count] > 0:
holes[count] -= 1
__UpperCAmelCase : List[Any] = count + min_val
i += 1
def _UpperCamelCase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : str = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(UpperCamelCase )
print("Sorted order is:" , " ".join(UpperCamelCase ) )
if __name__ == "__main__":
main()
| 77 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : Optional[Any] , UpperCamelCase_ : List[Any]="</s>" , UpperCamelCase_ : Tuple="<unk>" , UpperCamelCase_ : List[str]="<pad>" , UpperCamelCase_ : Union[str, Any]=125 , UpperCamelCase_ : Dict=None , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
if extra_ids > 0 and additional_special_tokens is None:
__UpperCAmelCase : int = [F"<extra_id_{i}>" for i in range(UpperCamelCase_)]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__UpperCAmelCase : Dict = len(set(filter(lambda UpperCamelCase_: bool("extra_id" in str(UpperCamelCase_)) , UpperCamelCase_)))
if extra_tokens != extra_ids:
raise ValueError(
F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
" extra_ids tokens")
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else pad_token
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else eos_token
__UpperCAmelCase : Optional[int] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else unk_token
super().__init__(
eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , extra_ids=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : List[str] = extra_ids
__UpperCAmelCase : int = 2**8 # utf is 8 bits
# define special tokens dict
__UpperCAmelCase : Dict[int, str] = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
__UpperCAmelCase : Any = len(self.special_tokens_encoder)
__UpperCAmelCase : List[Any] = len(UpperCamelCase_)
for i, token in enumerate(UpperCamelCase_):
__UpperCAmelCase : Union[str, Any] = self.vocab_size + i - n
__UpperCAmelCase : Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def a_ ( self : List[Any]):
"""simple docstring"""
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def a_ ( self : List[str] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase_ , token_ids_a=UpperCamelCase_ , already_has_special_tokens=UpperCamelCase_)
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(UpperCamelCase_)) + [1]
return ([0] * len(UpperCamelCase_)) + [1] + ([0] * len(UpperCamelCase_)) + [1]
def a_ ( self : Optional[Any] , UpperCamelCase_ : List[int]):
"""simple docstring"""
if len(UpperCamelCase_) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
" eos tokens being added.")
return token_ids
else:
return token_ids + [self.eos_token_id]
def a_ ( self : Dict , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Dict = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos) * [0]
return len(token_ids_a + eos + token_ids_a + eos) * [0]
def a_ ( self : Optional[int] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self._add_eos_if_not_present(UpperCamelCase_)
if token_ids_a is None:
return token_ids_a
else:
__UpperCAmelCase : List[Any] = self._add_eos_if_not_present(UpperCamelCase_)
return token_ids_a + token_ids_a
def a_ ( self : List[str] , UpperCamelCase_ : str):
"""simple docstring"""
__UpperCAmelCase : Any = [chr(UpperCamelCase_) for i in text.encode("utf-8")]
return tokens
def a_ ( self : Tuple , UpperCamelCase_ : List[Any]):
"""simple docstring"""
if token in self.special_tokens_encoder:
__UpperCAmelCase : Any = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
__UpperCAmelCase : int = self.added_tokens_encoder[token]
elif len(UpperCamelCase_) != 1:
__UpperCAmelCase : Optional[Any] = self.unk_token_id
else:
__UpperCAmelCase : Any = ord(UpperCamelCase_) + self._num_special_tokens
return token_id
def a_ ( self : Any , UpperCamelCase_ : List[str]):
"""simple docstring"""
if index in self.special_tokens_decoder:
__UpperCAmelCase : Any = self.special_tokens_decoder[index]
else:
__UpperCAmelCase : List[str] = chr(index - self._num_special_tokens)
return token
def a_ ( self : Dict , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : str = b""
for token in tokens:
if token in self.special_tokens_decoder:
__UpperCAmelCase : Tuple = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.added_tokens_decoder:
__UpperCAmelCase : Any = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.special_tokens_encoder:
__UpperCAmelCase : Optional[int] = token.encode("utf-8")
elif token in self.added_tokens_encoder:
__UpperCAmelCase : Optional[Any] = token.encode("utf-8")
else:
__UpperCAmelCase : Any = bytes([ord(UpperCamelCase_)])
bstring += tok_string
__UpperCAmelCase : List[Any] = bstring.decode("utf-8" , errors="ignore")
return string
def a_ ( self : Optional[int] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
return ()
| 77 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _UpperCamelCase ( UpperCamelCase = "AAPL" ) -> str:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = f"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
__UpperCAmelCase : str = BeautifulSoup(requests.get(UpperCamelCase ).text , "html.parser" )
__UpperCAmelCase : str = "My(6px) Pos(r) smartphone_Mt(6px)"
return soup.find("div" , class_=class_ ).find("span" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(f'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')
| 77 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self : List[Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple=3 , UpperCamelCase_ : Optional[int]=32 , UpperCamelCase_ : Dict=3 , UpperCamelCase_ : List[str]=10 , UpperCamelCase_ : str=[10, 20, 30, 40] , UpperCamelCase_ : Tuple=[1, 1, 2, 1] , UpperCamelCase_ : str=True , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Dict="relu" , UpperCamelCase_ : str=3 , UpperCamelCase_ : int=None , ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = parent
__UpperCAmelCase : List[str] = batch_size
__UpperCAmelCase : List[str] = image_size
__UpperCAmelCase : Tuple = num_channels
__UpperCAmelCase : Union[str, Any] = embeddings_size
__UpperCAmelCase : Dict = hidden_sizes
__UpperCAmelCase : Dict = depths
__UpperCAmelCase : Tuple = is_training
__UpperCAmelCase : List[Any] = use_labels
__UpperCAmelCase : Optional[int] = hidden_act
__UpperCAmelCase : str = num_labels
__UpperCAmelCase : Optional[int] = scope
__UpperCAmelCase : Dict = len(UpperCamelCase_)
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__UpperCAmelCase : Dict = self.get_config()
return config, pixel_values
def a_ ( self : Dict):
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def a_ ( self : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = FlaxRegNetModel(config=UpperCamelCase_)
__UpperCAmelCase : Dict = model(UpperCamelCase_)
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def a_ ( self : Any , UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.num_labels
__UpperCAmelCase : Tuple = FlaxRegNetForImageClassification(config=UpperCamelCase_)
__UpperCAmelCase : str = model(UpperCamelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Any = self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase : Tuple = config_and_inputs
__UpperCAmelCase : Optional[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
lowercase_ = False
lowercase_ = False
lowercase_ = False
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = FlaxRegNetModelTester(self)
__UpperCAmelCase : str = ConfigTester(self , config_class=UpperCamelCase_ , has_text_modality=UpperCamelCase_)
def a_ ( self : Dict):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def a_ ( self : Tuple):
"""simple docstring"""
return
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_)
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_)
@unittest.skip(reason="RegNet does not use inputs_embeds")
def a_ ( self : Union[str, Any]):
"""simple docstring"""
pass
@unittest.skip(reason="RegNet does not support input and output embeddings")
def a_ ( self : Optional[int]):
"""simple docstring"""
pass
def a_ ( self : str):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : int = model_class(UpperCamelCase_)
__UpperCAmelCase : Optional[int] = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase : Any = [*signature.parameters.keys()]
__UpperCAmelCase : Dict = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCamelCase_)
def a_ ( self : int):
"""simple docstring"""
def check_hidden_states_output(UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Union[str, Any]):
__UpperCAmelCase : Union[str, Any] = model_class(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_))
__UpperCAmelCase : List[str] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__UpperCAmelCase : str = self.model_tester.num_stages
self.assertEqual(len(UpperCamelCase_) , expected_num_stages + 1)
__UpperCAmelCase , __UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : List[str] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase : Optional[int] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
def a_ ( self : Tuple):
"""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 : List[Any] = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Optional[int] = model_class(UpperCamelCase_)
@jax.jit
def model_jitted(UpperCamelCase_ : int , **UpperCamelCase_ : Optional[int]):
return model(pixel_values=UpperCamelCase_ , **UpperCamelCase_)
with self.subTest("JIT Enabled"):
__UpperCAmelCase : Optional[Any] = model_jitted(**UpperCamelCase_).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
__UpperCAmelCase : Dict = model_jitted(**UpperCamelCase_).to_tuple()
self.assertEqual(len(UpperCamelCase_) , len(UpperCamelCase_))
for jitted_output, output in zip(UpperCamelCase_ , UpperCamelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCamelCase ( ) -> Any:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_flax
class a__ ( unittest.TestCase ):
@cached_property
def a_ ( self : Optional[int]):
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/regnet-y-040") if is_vision_available() else None
@slow
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Any = FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")
__UpperCAmelCase : Dict = self.default_image_processor
__UpperCAmelCase : str = prepare_img()
__UpperCAmelCase : int = image_processor(images=UpperCamelCase_ , return_tensors="np")
__UpperCAmelCase : Dict = model(**UpperCamelCase_)
# verify the logits
__UpperCAmelCase : Dict = (1, 1000)
self.assertEqual(outputs.logits.shape , UpperCamelCase_)
__UpperCAmelCase : Any = jnp.array([-0.4180, -1.5051, -3.4836])
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , UpperCamelCase_ , atol=1e-4))
| 77 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
A = logging.get_logger(__name__)
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase : int = []
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f"encoder.deit.blocks.{i}.norm1.weight", f"encoder.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((f"encoder.deit.blocks.{i}.norm1.bias", f"encoder.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append(
(f"encoder.deit.blocks.{i}.attn.proj.weight", f"encoder.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append(
(f"encoder.deit.blocks.{i}.attn.proj.bias", f"encoder.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append(
(f"encoder.deit.blocks.{i}.norm2.weight", f"encoder.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((f"encoder.deit.blocks.{i}.norm2.bias", f"encoder.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append(
(f"encoder.deit.blocks.{i}.mlp.fc1.weight", f"encoder.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append(
(f"encoder.deit.blocks.{i}.mlp.fc1.bias", f"encoder.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append(
(f"encoder.deit.blocks.{i}.mlp.fc2.weight", f"encoder.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((f"encoder.deit.blocks.{i}.mlp.fc2.bias", f"encoder.encoder.layer.{i}.output.dense.bias") )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
("encoder.deit.cls_token", "encoder.embeddings.cls_token"),
("encoder.deit.pos_embed", "encoder.embeddings.position_embeddings"),
("encoder.deit.patch_embed.proj.weight", "encoder.embeddings.patch_embeddings.projection.weight"),
("encoder.deit.patch_embed.proj.bias", "encoder.embeddings.patch_embeddings.projection.bias"),
("encoder.deit.norm.weight", "encoder.layernorm.weight"),
("encoder.deit.norm.bias", "encoder.layernorm.bias"),
] )
return rename_keys
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
__UpperCAmelCase : Optional[Any] = state_dict.pop(f"encoder.deit.blocks.{i}.attn.qkv.weight" )
__UpperCAmelCase : List[Any] = in_proj_weight[
: encoder_config.hidden_size, :
]
__UpperCAmelCase : List[str] = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
__UpperCAmelCase : List[Any] = in_proj_weight[
-encoder_config.hidden_size :, :
]
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Any:
"""simple docstring"""
__UpperCAmelCase : str = dct.pop(UpperCamelCase )
__UpperCAmelCase : List[Any] = val
def _UpperCamelCase ( UpperCamelCase ) -> str:
"""simple docstring"""
if "handwritten" in checkpoint_url:
__UpperCAmelCase : Any = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg" # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
__UpperCAmelCase : Any = "https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"
__UpperCAmelCase : Tuple = Image.open(requests.get(UpperCamelCase , stream=UpperCamelCase ).raw ).convert("RGB" )
return im
@torch.no_grad()
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> List[Any]:
"""simple docstring"""
__UpperCAmelCase : List[str] = ViTConfig(image_size=384 , qkv_bias=UpperCamelCase )
__UpperCAmelCase : Any = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
__UpperCAmelCase : Optional[int] = 768
elif "large" in checkpoint_url:
# use ViT-large encoder
__UpperCAmelCase : Any = 1024
__UpperCAmelCase : int = 4096
__UpperCAmelCase : Tuple = 24
__UpperCAmelCase : Any = 16
__UpperCAmelCase : Union[str, Any] = 1024
else:
raise ValueError("Should either find 'base' or 'large' in checkpoint URL" )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
__UpperCAmelCase : List[str] = False
__UpperCAmelCase : Any = "relu"
__UpperCAmelCase : Any = 1024
__UpperCAmelCase : str = True
__UpperCAmelCase : Any = False
__UpperCAmelCase : Optional[int] = False
# load HuggingFace model
__UpperCAmelCase : Optional[Any] = ViTModel(UpperCamelCase , add_pooling_layer=UpperCamelCase )
__UpperCAmelCase : str = TrOCRForCausalLM(UpperCamelCase )
__UpperCAmelCase : Optional[int] = VisionEncoderDecoderModel(encoder=UpperCamelCase , decoder=UpperCamelCase )
model.eval()
# load state_dict of original model, rename some keys
__UpperCAmelCase : Optional[int] = torch.hub.load_state_dict_from_url(UpperCamelCase , map_location="cpu" , check_hash=UpperCamelCase )["model"]
__UpperCAmelCase : Optional[Any] = create_rename_keys(UpperCamelCase , UpperCamelCase )
for src, dest in rename_keys:
rename_key(UpperCamelCase , UpperCamelCase , UpperCamelCase )
read_in_q_k_v(UpperCamelCase , UpperCamelCase )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
__UpperCAmelCase : Optional[int] = state_dict.pop(UpperCamelCase )
if key.startswith("decoder" ) and "output_projection" not in key:
__UpperCAmelCase : Any = val
else:
__UpperCAmelCase : int = val
# load state dict
model.load_state_dict(UpperCamelCase )
# Check outputs on an image
__UpperCAmelCase : List[str] = ViTImageProcessor(size=encoder_config.image_size )
__UpperCAmelCase : Union[str, Any] = RobertaTokenizer.from_pretrained("roberta-large" )
__UpperCAmelCase : Dict = TrOCRProcessor(UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Optional[Any] = processor(images=prepare_img(UpperCamelCase ) , return_tensors="pt" ).pixel_values
# verify logits
__UpperCAmelCase : Dict = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
__UpperCAmelCase : Optional[Any] = model(pixel_values=UpperCamelCase , decoder_input_ids=UpperCamelCase )
__UpperCAmelCase : List[Any] = outputs.logits
__UpperCAmelCase : List[str] = torch.Size([1, 1, 5_0265] )
if "trocr-base-handwritten" in checkpoint_url:
__UpperCAmelCase : str = torch.tensor(
[-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311] )
elif "trocr-large-handwritten" in checkpoint_url:
__UpperCAmelCase : int = torch.tensor(
[-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170] )
elif "trocr-base-printed" in checkpoint_url:
__UpperCAmelCase : str = torch.tensor(
[-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210] )
elif "trocr-large-printed" in checkpoint_url:
__UpperCAmelCase : Union[str, Any] = torch.tensor(
[-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :10] , UpperCamelCase , atol=1e-3 ), "First elements of logits not as expected"
Path(UpperCamelCase ).mkdir(exist_ok=UpperCamelCase )
print(f"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(UpperCamelCase )
print(f"Saving processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(UpperCamelCase )
if __name__ == "__main__":
A = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
type=str,
help="""URL 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."""
)
A = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 77 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
A = """
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results['spearmanr'])
-0.7
>>> print(round(results['spearmanr_pvalue'], 2))
0.19
"""
A = r"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
def a_ ( self : Any):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("float"),
"references": datasets.Value("float"),
}) , reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"] , )
def a_ ( self : Union[str, Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : int=False):
"""simple docstring"""
__UpperCAmelCase : List[str] = spearmanr(UpperCamelCase_ , UpperCamelCase_)
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
while a != 0:
__UpperCAmelCase , __UpperCAmelCase : str = b % a, a
return b
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
if gcd(UpperCamelCase , UpperCamelCase ) != 1:
__UpperCAmelCase : Optional[Any] = f"mod inverse of {a!r} and {m!r} does not exist"
raise ValueError(UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : str = 1, 0, a
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Tuple = 0, 1, m
while va != 0:
__UpperCAmelCase : List[str] = ua // va
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : int = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 77 |
"""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""": """spiece.model"""}
A = {
"""vocab_file""": {
"""bert_for_seq_generation""": (
"""https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
),
}
}
A = {"""bert_for_seq_generation""": 512}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = []
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : List[str] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : List[str]="<s>" , UpperCamelCase_ : Optional[Any]="</s>" , UpperCamelCase_ : Optional[int]="<unk>" , UpperCamelCase_ : int="<pad>" , UpperCamelCase_ : List[Any]="<::::>" , UpperCamelCase_ : Optional[Dict[str, Any]] = None , **UpperCamelCase_ : List[Any] , ):
"""simple docstring"""
__UpperCAmelCase : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase_ , )
__UpperCAmelCase : Dict = vocab_file
__UpperCAmelCase : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(UpperCamelCase_)
@property
def a_ ( self : List[str]):
"""simple docstring"""
return self.sp_model.get_piece_size()
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : int = {self.convert_ids_to_tokens(UpperCamelCase_): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self : int):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = self.__dict__.copy()
__UpperCAmelCase : List[Any] = None
return state
def __setstate__( self : Optional[Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs"):
__UpperCAmelCase : List[Any] = {}
__UpperCAmelCase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def a_ ( self : Any , UpperCamelCase_ : str):
"""simple docstring"""
return self.sp_model.encode(UpperCamelCase_ , out_type=UpperCamelCase_)
def a_ ( self : Optional[Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
return self.sp_model.piece_to_id(UpperCamelCase_)
def a_ ( self : Tuple , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : int = self.sp_model.IdToPiece(UpperCamelCase_)
return token
def a_ ( self : Dict , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : int = []
__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(UpperCamelCase_) + token
__UpperCAmelCase : List[Any] = []
else:
current_sub_tokens.append(UpperCamelCase_)
out_string += self.sp_model.decode(UpperCamelCase_)
return out_string.strip()
def a_ ( self : List[str] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : Tuple = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , UpperCamelCase_)
elif not os.path.isfile(self.vocab_file):
with open(UpperCamelCase_ , "wb") as fi:
__UpperCAmelCase : List[str] = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase_)
return (out_vocab_file,)
| 77 | 1 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class a__ ( unittest.TestCase ):
def a_ ( self : Any):
"""simple docstring"""
debug_launcher(test_script.main)
def a_ ( self : int):
"""simple docstring"""
debug_launcher(test_ops.main)
| 77 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
A = """true"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=82 , UpperCamelCase=16 ) -> Tuple:
"""simple docstring"""
set_seed(42 )
__UpperCAmelCase : Dict = RegressionModel()
__UpperCAmelCase : Optional[Any] = deepcopy(UpperCamelCase )
__UpperCAmelCase : Any = RegressionDataset(length=UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = DataLoader(UpperCamelCase , batch_size=UpperCamelCase )
model.to(accelerator.device )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = accelerator.prepare(UpperCamelCase , UpperCamelCase )
return model, ddp_model, dataloader
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=False ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : List[str] = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
__UpperCAmelCase : Dict = load_dataset("glue" , "mrpc" , split="validation" )
def tokenize_function(UpperCamelCase ):
__UpperCAmelCase : Dict = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=UpperCamelCase , max_length=UpperCamelCase )
return outputs
with accelerator.main_process_first():
__UpperCAmelCase : str = dataset.map(
UpperCamelCase , batched=UpperCamelCase , remove_columns=["idx", "sentence1", "sentence2"] , )
__UpperCAmelCase : List[Any] = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(UpperCamelCase ):
if use_longest:
return tokenizer.pad(UpperCamelCase , padding="longest" , return_tensors="pt" )
return tokenizer.pad(UpperCamelCase , padding="max_length" , max_length=128 , return_tensors="pt" )
return DataLoader(UpperCamelCase , shuffle=UpperCamelCase , collate_fn=UpperCamelCase , batch_size=16 )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : List[Any] = Accelerator(dispatch_batches=UpperCamelCase , split_batches=UpperCamelCase )
__UpperCAmelCase : int = get_dataloader(UpperCamelCase , not dispatch_batches )
__UpperCAmelCase : Any = AutoModelForSequenceClassification.from_pretrained(
"hf-internal-testing/mrpc-bert-base-cased" , return_dict=UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Dict = accelerator.prepare(UpperCamelCase , UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase : Dict = []
for batch in dataloader:
__UpperCAmelCase , __UpperCAmelCase : int = batch.values()
with torch.no_grad():
__UpperCAmelCase : int = model(UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : List[str] = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = [], []
for logit, targ in logits_and_targets:
logits.append(UpperCamelCase )
targs.append(UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = torch.cat(UpperCamelCase ), torch.cat(UpperCamelCase )
return logits, targs
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=82 , UpperCamelCase=False , UpperCamelCase=False , UpperCamelCase=16 ) -> int:
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = get_basic_setup(UpperCamelCase , UpperCamelCase , UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = generate_predictions(UpperCamelCase , UpperCamelCase , UpperCamelCase )
assert (
len(UpperCamelCase ) == num_samples
), f"Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(UpperCamelCase )}"
def _UpperCamelCase ( UpperCamelCase = False , UpperCamelCase = False ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : List[str] = evaluate.load("glue" , "mrpc" )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = get_mrpc_setup(UpperCamelCase , UpperCamelCase )
# First do baseline
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Tuple = setup["no"]
model.to(UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(UpperCamelCase )
with torch.inference_mode():
__UpperCAmelCase : List[str] = model(**UpperCamelCase )
__UpperCAmelCase : str = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=UpperCamelCase , references=batch["labels"] )
__UpperCAmelCase : str = metric.compute()
# Then do distributed
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
__UpperCAmelCase : Any = model(**UpperCamelCase )
__UpperCAmelCase : str = outputs.logits.argmax(dim=-1 )
__UpperCAmelCase : Union[str, Any] = batch["labels"]
__UpperCAmelCase , __UpperCAmelCase : Any = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=UpperCamelCase , references=UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"
def _UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
__UpperCAmelCase : Dict = Accelerator(split_batches=UpperCamelCase , dispatch_batches=UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("**Testing gather_for_metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`" )
test_mrpc(UpperCamelCase , UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test torch metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__UpperCAmelCase : Union[str, Any] = Accelerator(split_batches=UpperCamelCase , dispatch_batches=UpperCamelCase )
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99" )
test_torch_metrics(UpperCamelCase , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test last batch is not dropped when perfectly divisible**" )
__UpperCAmelCase : Any = Accelerator()
test_torch_metrics(UpperCamelCase , 512 )
accelerator.state._reset_state()
def _UpperCamelCase ( UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 77 | 1 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = df.shape[:1][0]
# If you're using some other dataset input the target column
A = df.iloc[:, 1:2]
A = actual_data.values.reshape(len_data, 1)
A = MinMaxScaler().fit_transform(actual_data)
A = 10
A = 5
A = 20
A = len_data - periods * look_back
A = actual_data[:division]
A = actual_data[division - look_back :]
A , A = [], []
A , A = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
A = np.array(train_x)
A = np.array(test_x)
A = np.array([list(i.ravel()) for i in train_y])
A = np.array([list(i.ravel()) for i in test_y])
A = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="""mean_squared_error""", optimizer="""adam""")
A = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
A = model.predict(x_test)
| 77 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in range(UpperCamelCase , UpperCamelCase ):
__UpperCAmelCase : List[Any] = i
__UpperCAmelCase : Any = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
__UpperCAmelCase : Dict = array[temp_index - 1]
temp_index -= 1
__UpperCAmelCase : str = temp_index_value
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> None: # Max Heap
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = index
__UpperCAmelCase : List[str] = 2 * index + 1 # Left Node
__UpperCAmelCase : Union[str, Any] = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
__UpperCAmelCase : Tuple = left_index
if right_index < heap_size and array[largest] < array[right_index]:
__UpperCAmelCase : int = right_index
if largest != index:
__UpperCAmelCase , __UpperCAmelCase : List[str] = array[largest], array[index]
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
__UpperCAmelCase : List[Any] = len(UpperCamelCase )
for i in range(n // 2 , -1 , -1 ):
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
for i in range(n - 1 , 0 , -1 ):
__UpperCAmelCase , __UpperCAmelCase : int = array[0], array[i]
heapify(UpperCamelCase , 0 , UpperCamelCase )
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = low
__UpperCAmelCase : List[str] = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = array[j], array[i]
i += 1
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
if len(UpperCamelCase ) == 0:
return array
__UpperCAmelCase : Optional[int] = 2 * math.ceil(math.loga(len(UpperCamelCase ) ) )
__UpperCAmelCase : List[Any] = 16
return intro_sort(UpperCamelCase , 0 , len(UpperCamelCase ) , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> list:
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(UpperCamelCase )
max_depth -= 1
__UpperCAmelCase : List[Any] = median_of_a(UpperCamelCase , UpperCamelCase , start + ((end - start) // 2) + 1 , end - 1 )
__UpperCAmelCase : Union[str, Any] = partition(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
intro_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Optional[Any] = p
return insertion_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
A = input("""Enter numbers separated by a comma : """).strip()
A = [float(item) for item in user_input.split(""",""")]
print(sort(unsorted))
| 77 | 1 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
A = logging.get_logger(__name__)
A = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
"""self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
"""self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """ctc_proj""",
"""mask_emb""": """masked_spec_embed""",
}
A = [
"""ctc_proj""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Dict:
"""simple docstring"""
for attribute in key.split("." ):
__UpperCAmelCase : Optional[int] = getattr(UpperCamelCase , UpperCamelCase )
if weight_type is not None:
__UpperCAmelCase : Optional[int] = getattr(UpperCamelCase , UpperCamelCase ).shape
else:
__UpperCAmelCase : str = hf_pointer.shape
assert hf_shape == value.shape, (
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}"
)
if weight_type == "weight":
__UpperCAmelCase : List[str] = value
elif weight_type == "weight_g":
__UpperCAmelCase : Dict = value
elif weight_type == "weight_v":
__UpperCAmelCase : Optional[Any] = value
elif weight_type == "bias":
__UpperCAmelCase : Optional[int] = value
else:
__UpperCAmelCase : Dict = value
logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Any:
"""simple docstring"""
__UpperCAmelCase : int = []
__UpperCAmelCase : Union[str, Any] = fairseq_model.state_dict()
__UpperCAmelCase : Dict = hf_model.feature_extractor
for name, value in fairseq_dict.items():
__UpperCAmelCase : List[Any] = False
if "conv_layers" in name:
load_conv_layer(
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , hf_model.config.feat_extract_norm == "group" , )
__UpperCAmelCase : Any = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
__UpperCAmelCase : Optional[Any] = True
if "*" in mapped_key:
__UpperCAmelCase : Optional[Any] = name.split(UpperCamelCase )[0].split("." )[-2]
__UpperCAmelCase : Union[str, Any] = mapped_key.replace("*" , UpperCamelCase )
if "weight_g" in name:
__UpperCAmelCase : Dict = "weight_g"
elif "weight_v" in name:
__UpperCAmelCase : List[Any] = "weight_v"
elif "bias" in name and "relative_attention_bias" not in name:
__UpperCAmelCase : Optional[int] = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__UpperCAmelCase : int = "weight"
else:
__UpperCAmelCase : str = None
set_recursively(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
continue
if not is_used:
unused_weights.append(UpperCamelCase )
logger.warning(f"Unused weights: {unused_weights}" )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : List[str] = full_name.split("conv_layers." )[-1]
__UpperCAmelCase : Tuple = name.split("." )
__UpperCAmelCase : List[str] = int(items[0] )
__UpperCAmelCase : Tuple = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
__UpperCAmelCase : Union[str, Any] = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
__UpperCAmelCase : Tuple = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
" found."
)
__UpperCAmelCase : Optional[int] = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
)
__UpperCAmelCase : List[str] = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(UpperCamelCase )
@torch.no_grad()
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase=None ) -> List[Any]:
"""simple docstring"""
# load the pre-trained checkpoints
__UpperCAmelCase : Optional[int] = torch.load(UpperCamelCase )
__UpperCAmelCase : Optional[int] = WavLMConfigOrig(checkpoint["cfg"] )
__UpperCAmelCase : List[str] = WavLMOrig(UpperCamelCase )
model.load_state_dict(checkpoint["model"] )
model.eval()
if config_path is not None:
__UpperCAmelCase : Optional[Any] = WavLMConfig.from_pretrained(UpperCamelCase )
else:
__UpperCAmelCase : str = WavLMConfig()
__UpperCAmelCase : str = WavLMModel(UpperCamelCase )
recursively_load_weights(UpperCamelCase , UpperCamelCase )
hf_wavlm.save_pretrained(UpperCamelCase )
if __name__ == "__main__":
A = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
A = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 77 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
__UpperCAmelCase : Any = 0
__UpperCAmelCase : Dict = 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Tuple = 0
# compute the shape of the output matrix
__UpperCAmelCase : Optional[int] = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
__UpperCAmelCase : List[str] = np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
__UpperCAmelCase : str = np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
__UpperCAmelCase : int = 0
__UpperCAmelCase : int = 0
return updated_arr
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : List[str] = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Any = 0
# compute the shape of the output matrix
__UpperCAmelCase : Tuple = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
__UpperCAmelCase : str = np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
__UpperCAmelCase : Tuple = int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Optional[Any] = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name="""avgpooling""", verbose=True)
# Loading the image
A = Image.open("""path_to_image""")
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 77 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A = None
A = logging.get_logger(__name__)
A = """▁"""
A = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
A = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
A = {
"""google/pegasus-xsum""": 512,
}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = PegasusTokenizer
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : str , UpperCamelCase_ : str=None , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : int="<pad>" , UpperCamelCase_ : Optional[Any]="</s>" , UpperCamelCase_ : Any="<unk>" , UpperCamelCase_ : Tuple="<mask_2>" , UpperCamelCase_ : Any="<mask_1>" , UpperCamelCase_ : Tuple=None , UpperCamelCase_ : str=103 , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = offset
if additional_special_tokens is not None:
if not isinstance(UpperCamelCase_ , UpperCamelCase_):
raise TypeError(
F"additional_special_tokens should be of type {type(UpperCamelCase_)}, but is"
F" {type(UpperCamelCase_)}")
__UpperCAmelCase : Any = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F"<unk_{i}>" for i in range(len(UpperCamelCase_) , self.offset - 1)
]
if len(set(UpperCamelCase_)) != len(UpperCamelCase_):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.")
__UpperCAmelCase : str = additional_special_tokens_extended
else:
__UpperCAmelCase : Tuple = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F"<unk_{i}>" for i in range(2 , self.offset)]
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , pad_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , mask_token_sent=UpperCamelCase_ , offset=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Optional[int] = vocab_file
__UpperCAmelCase : List[str] = False if not self.vocab_file else True
def a_ ( self : Union[str, Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : int = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
F" {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}")
return [1 if x in all_special_ids else 0 for x in seq]
def a_ ( self : Union[str, Any] , UpperCamelCase_ : List , UpperCamelCase_ : Optional[List] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(UpperCamelCase_)
elif token_ids_a is None:
return self._special_token_mask(UpperCamelCase_) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a) + [1]
def a_ ( self : int , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[Any]=None):
"""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 a_ ( self : Union[str, Any] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer.")
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : List[str] = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_):
copyfile(self.vocab_file , UpperCamelCase_)
return (out_vocab_file,)
| 77 | 1 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _UpperCamelCase ( UpperCamelCase = 8 ) -> str:
"""simple docstring"""
__UpperCAmelCase : List[Any] = ascii_letters + digits + punctuation
return "".join(secrets.choice(UpperCamelCase ) for _ in range(UpperCamelCase ) )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(UpperCamelCase )
__UpperCAmelCase : int = i // 3
__UpperCAmelCase : Optional[int] = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
__UpperCAmelCase : Optional[Any] = (
chars_incl
+ random(UpperCamelCase , quotient + remainder )
+ random(UpperCamelCase , UpperCamelCase )
+ random(UpperCamelCase , UpperCamelCase )
)
__UpperCAmelCase : Union[str, Any] = list(UpperCamelCase )
shuffle(UpperCamelCase )
return "".join(UpperCamelCase )
# random is a generalised function for letters, characters and numbers
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
return "".join(secrets.choice(UpperCamelCase ) for _ in range(UpperCamelCase ) )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
pass # Put your code here...
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Any:
"""simple docstring"""
pass # Put your code here...
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
pass # Put your code here...
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 8 ) -> bool:
"""simple docstring"""
if len(UpperCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
__UpperCAmelCase : Tuple = any(char in ascii_uppercase for char in password )
__UpperCAmelCase : Optional[int] = any(char in ascii_lowercase for char in password )
__UpperCAmelCase : Optional[int] = any(char in digits for char in password )
__UpperCAmelCase : Any = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def _UpperCamelCase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : str = int(input("Please indicate the max length of your password: " ).strip() )
__UpperCAmelCase : Dict = input(
"Please indicate the characters that must be in your password: " ).strip()
print("Password generated:" , password_generator(UpperCamelCase ) )
print(
"Alternative Password generated:" , alternative_password_generator(UpperCamelCase , UpperCamelCase ) , )
print("[If you are thinking of using this passsword, You better save it.]" )
if __name__ == "__main__":
main()
| 77 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[str]:
"""simple docstring"""
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
__UpperCAmelCase : Optional[Any] = TapasConfig.from_json_file(UpperCamelCase )
# set absolute/relative position embeddings parameter
__UpperCAmelCase : Optional[Any] = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
__UpperCAmelCase : List[str] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "WTQ":
# run_task_main.py hparams
__UpperCAmelCase : Tuple = 4
__UpperCAmelCase : Any = True
# hparam_utils.py hparams
__UpperCAmelCase : Union[str, Any] = 0.664694
__UpperCAmelCase : Union[str, Any] = 0.207951
__UpperCAmelCase : int = 0.121194
__UpperCAmelCase : Optional[int] = True
__UpperCAmelCase : List[str] = True
__UpperCAmelCase : Union[str, Any] = False
__UpperCAmelCase : List[str] = 0.0352513
__UpperCAmelCase : Optional[int] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
__UpperCAmelCase : int = 4
__UpperCAmelCase : Optional[int] = False
# hparam_utils.py hparams
__UpperCAmelCase : int = 36.4519
__UpperCAmelCase : str = 0.903421
__UpperCAmelCase : Dict = 222.088
__UpperCAmelCase : Dict = True
__UpperCAmelCase : Union[str, Any] = True
__UpperCAmelCase : Tuple = True
__UpperCAmelCase : Any = 0.763141
__UpperCAmelCase : Optional[Any] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "TABFACT":
__UpperCAmelCase : Union[str, Any] = TapasForSequenceClassification(config=UpperCamelCase )
elif task == "MLM":
__UpperCAmelCase : Tuple = TapasForMaskedLM(config=UpperCamelCase )
elif task == "INTERMEDIATE_PRETRAINING":
__UpperCAmelCase : List[str] = TapasModel(config=UpperCamelCase )
else:
raise ValueError(f"Task {task} not supported." )
print(f"Building PyTorch model from configuration: {config}" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(UpperCamelCase , UpperCamelCase , UpperCamelCase )
# Save pytorch-model (weights and configuration)
print(f"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(UpperCamelCase )
# Save tokenizer files
print(f"Save tokenizer files to {pytorch_dump_path}" )
__UpperCAmelCase : str = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt" , model_max_length=512 )
tokenizer.save_pretrained(UpperCamelCase )
print("Used relative position embeddings:" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
)
parser.add_argument(
"""--reset_position_index_per_cell""",
default=False,
action="""store_true""",
help="""Whether to use relative position embeddings or not. Defaults to True.""",
)
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--tapas_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained TAPAS model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 77 | 1 |
"""simple docstring"""
import math
import unittest
from transformers import BioGptConfig, 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 (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Dict , UpperCamelCase_ : Dict=13 , UpperCamelCase_ : Tuple=7 , UpperCamelCase_ : int=True , UpperCamelCase_ : Optional[Any]=True , UpperCamelCase_ : Optional[Any]=False , UpperCamelCase_ : Any=True , UpperCamelCase_ : List[Any]=99 , UpperCamelCase_ : List[Any]=32 , UpperCamelCase_ : Dict=5 , UpperCamelCase_ : List[Any]=4 , UpperCamelCase_ : List[str]=37 , UpperCamelCase_ : Any="gelu" , UpperCamelCase_ : List[Any]=0.1 , UpperCamelCase_ : Tuple=0.1 , UpperCamelCase_ : List[str]=512 , UpperCamelCase_ : Dict=16 , UpperCamelCase_ : Tuple=2 , UpperCamelCase_ : Optional[Any]=0.02 , UpperCamelCase_ : List[str]=3 , UpperCamelCase_ : Tuple=4 , UpperCamelCase_ : List[str]=None , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = parent
__UpperCAmelCase : int = batch_size
__UpperCAmelCase : str = seq_length
__UpperCAmelCase : Any = is_training
__UpperCAmelCase : Dict = use_input_mask
__UpperCAmelCase : Tuple = use_token_type_ids
__UpperCAmelCase : List[Any] = use_labels
__UpperCAmelCase : str = vocab_size
__UpperCAmelCase : int = hidden_size
__UpperCAmelCase : Any = num_hidden_layers
__UpperCAmelCase : Any = num_attention_heads
__UpperCAmelCase : Tuple = intermediate_size
__UpperCAmelCase : int = hidden_act
__UpperCAmelCase : Dict = hidden_dropout_prob
__UpperCAmelCase : Union[str, Any] = attention_probs_dropout_prob
__UpperCAmelCase : List[str] = max_position_embeddings
__UpperCAmelCase : Optional[Any] = type_vocab_size
__UpperCAmelCase : Optional[Any] = type_sequence_label_size
__UpperCAmelCase : Union[str, Any] = initializer_range
__UpperCAmelCase : Optional[int] = num_labels
__UpperCAmelCase : int = num_choices
__UpperCAmelCase : Dict = scope
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__UpperCAmelCase : List[str] = None
if self.use_input_mask:
__UpperCAmelCase : Optional[int] = random_attention_mask([self.batch_size, self.seq_length])
__UpperCAmelCase : Union[str, Any] = None
if self.use_token_type_ids:
__UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
__UpperCAmelCase : Tuple = None
__UpperCAmelCase : List[str] = None
__UpperCAmelCase : Optional[Any] = None
if self.use_labels:
__UpperCAmelCase : int = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
__UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size] , self.num_choices)
__UpperCAmelCase : Any = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self : int):
"""simple docstring"""
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCamelCase_ , initializer_range=self.initializer_range , )
def a_ ( self : Any , UpperCamelCase_ : List[str] , UpperCamelCase_ : Any , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : str , UpperCamelCase_ : str , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : Tuple = BioGptModel(config=UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
__UpperCAmelCase : Optional[int] = model(UpperCamelCase_ , attention_mask=UpperCamelCase_)
__UpperCAmelCase : Tuple = model(UpperCamelCase_)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def a_ ( self : Dict , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Tuple , UpperCamelCase_ : str , UpperCamelCase_ : int , UpperCamelCase_ : Dict , UpperCamelCase_ : str , UpperCamelCase_ : int , UpperCamelCase_ : int , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = BioGptForCausalLM(config=UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
__UpperCAmelCase : Optional[Any] = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def a_ ( self : str , UpperCamelCase_ : Any , UpperCamelCase_ : Any , UpperCamelCase_ : str , UpperCamelCase_ : Any , UpperCamelCase_ : List[str] , *UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = BioGptModel(config=UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
# create attention mask
__UpperCAmelCase : List[Any] = torch.ones(input_ids.shape , dtype=torch.long , device=UpperCamelCase_)
__UpperCAmelCase : List[Any] = self.seq_length // 2
__UpperCAmelCase : Any = 0
# first forward pass
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = model(UpperCamelCase_ , attention_mask=UpperCamelCase_).to_tuple()
# create hypothetical next token and extent to next_input_ids
__UpperCAmelCase : Optional[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size)
# change a random masked slice from input_ids
__UpperCAmelCase : Optional[Any] = ids_tensor((1,) , UpperCamelCase_).item() + 1
__UpperCAmelCase : Dict = ids_tensor((self.batch_size, 1) , config.vocab_size).squeeze(-1)
__UpperCAmelCase : str = random_other_next_tokens
# append to next input_ids and attn_mask
__UpperCAmelCase : Optional[Any] = torch.cat([input_ids, next_tokens] , dim=-1)
__UpperCAmelCase : Optional[int] = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=UpperCamelCase_)] , dim=1 , )
# get two different outputs
__UpperCAmelCase : Optional[Any] = model(UpperCamelCase_ , attention_mask=UpperCamelCase_)["last_hidden_state"]
__UpperCAmelCase : Any = model(UpperCamelCase_ , past_key_values=UpperCamelCase_ , attention_mask=UpperCamelCase_)["last_hidden_state"]
# select random slice
__UpperCAmelCase : List[str] = ids_tensor((1,) , output_from_past.shape[-1]).item()
__UpperCAmelCase : List[str] = output_from_no_past[:, -1, random_slice_idx].detach()
__UpperCAmelCase : Dict = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1e-3))
def a_ ( self : Union[str, Any] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : str , UpperCamelCase_ : Tuple , UpperCamelCase_ : str , UpperCamelCase_ : Any , *UpperCamelCase_ : List[Any]):
"""simple docstring"""
__UpperCAmelCase : str = BioGptModel(config=UpperCamelCase_).to(UpperCamelCase_).eval()
__UpperCAmelCase : Any = torch.ones(input_ids.shape , dtype=torch.long , device=UpperCamelCase_)
# first forward pass
__UpperCAmelCase : int = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , use_cache=UpperCamelCase_)
__UpperCAmelCase , __UpperCAmelCase : Tuple = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
__UpperCAmelCase : List[str] = ids_tensor((self.batch_size, 3) , config.vocab_size)
__UpperCAmelCase : Optional[int] = ids_tensor((self.batch_size, 3) , 2)
# append to next input_ids and
__UpperCAmelCase : Union[str, Any] = torch.cat([input_ids, next_tokens] , dim=-1)
__UpperCAmelCase : List[str] = torch.cat([attention_mask, next_attn_mask] , dim=-1)
__UpperCAmelCase : Any = model(UpperCamelCase_ , attention_mask=UpperCamelCase_)["last_hidden_state"]
__UpperCAmelCase : List[str] = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , past_key_values=UpperCamelCase_)[
"last_hidden_state"
]
# select random slice
__UpperCAmelCase : List[str] = ids_tensor((1,) , output_from_past.shape[-1]).item()
__UpperCAmelCase : List[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
__UpperCAmelCase : Dict = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1e-3))
def a_ ( self : Union[str, Any] , UpperCamelCase_ : List[str] , UpperCamelCase_ : Dict , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : str , *UpperCamelCase_ : List[Any] , UpperCamelCase_ : List[str]=False):
"""simple docstring"""
__UpperCAmelCase : Dict = BioGptForCausalLM(UpperCamelCase_)
model.to(UpperCamelCase_)
if gradient_checkpointing:
model.gradient_checkpointing_enable()
__UpperCAmelCase : List[Any] = model(UpperCamelCase_ , labels=UpperCamelCase_)
self.parent.assertEqual(result.loss.shape , ())
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
result.loss.backward()
def a_ ( self : str , UpperCamelCase_ : int , *UpperCamelCase_ : List[str]):
"""simple docstring"""
__UpperCAmelCase : List[str] = BioGptModel(UpperCamelCase_)
__UpperCAmelCase : List[str] = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers)
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std) , 0.001)
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0) , 0.01)
def a_ ( self : Optional[Any] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : int , UpperCamelCase_ : List[str] , UpperCamelCase_ : List[str] , UpperCamelCase_ : List[Any] , *UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.num_labels
__UpperCAmelCase : Any = BioGptForTokenClassification(UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
__UpperCAmelCase : Optional[int] = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) : Dict = config_and_inputs
__UpperCAmelCase : Any = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class a__ ( __magic_name__ , __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase_ = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
lowercase_ = (BioGptForCausalLM,) if is_torch_available() else ()
lowercase_ = (
{
"feature-extraction": BioGptModel,
"text-classification": BioGptForSequenceClassification,
"text-generation": BioGptForCausalLM,
"token-classification": BioGptForTokenClassification,
"zero-shot": BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase_ = False
def a_ ( self : str):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = BioGptModelTester(self)
__UpperCAmelCase : Dict = ConfigTester(self , config_class=UpperCamelCase_ , hidden_size=37)
def a_ ( self : List[Any]):
"""simple docstring"""
self.config_tester.run_common_tests()
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_)
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__UpperCAmelCase : Union[str, Any] = type
self.model_tester.create_and_check_model(*UpperCamelCase_)
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*UpperCamelCase_)
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*UpperCamelCase_ , gradient_checkpointing=UpperCamelCase_)
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*UpperCamelCase_)
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*UpperCamelCase_)
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*UpperCamelCase_)
@slow
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : int = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
model.to(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = BioGptTokenizer.from_pretrained("microsoft/biogpt")
__UpperCAmelCase : Tuple = "left"
# Define PAD Token = EOS Token = 50256
__UpperCAmelCase : Tuple = tokenizer.eos_token
__UpperCAmelCase : Union[str, Any] = model.config.eos_token_id
# use different length sentences to test batching
__UpperCAmelCase : str = [
"Hello, my dog is a little",
"Today, I",
]
__UpperCAmelCase : List[Any] = tokenizer(UpperCamelCase_ , return_tensors="pt" , padding=UpperCamelCase_)
__UpperCAmelCase : List[Any] = inputs["input_ids"].to(UpperCamelCase_)
__UpperCAmelCase : List[Any] = model.generate(
input_ids=UpperCamelCase_ , attention_mask=inputs["attention_mask"].to(UpperCamelCase_) , )
__UpperCAmelCase : Dict = tokenizer(sentences[0] , return_tensors="pt").input_ids.to(UpperCamelCase_)
__UpperCAmelCase : Tuple = model.generate(input_ids=UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
__UpperCAmelCase : Optional[Any] = tokenizer(sentences[1] , return_tensors="pt").input_ids.to(UpperCamelCase_)
__UpperCAmelCase : str = model.generate(input_ids=UpperCamelCase_ , max_length=model.config.max_length - num_paddings)
__UpperCAmelCase : Any = tokenizer.batch_decode(UpperCamelCase_ , skip_special_tokens=UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = tokenizer.decode(output_non_padded[0] , skip_special_tokens=UpperCamelCase_)
__UpperCAmelCase : Dict = tokenizer.decode(output_padded[0] , skip_special_tokens=UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = [
"Hello, my dog is a little bit bigger than a little bit.",
"Today, I have a good idea of how to use the information",
]
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_)
self.assertListEqual(UpperCamelCase_ , [non_padded_sentence, padded_sentence])
@slow
def a_ ( self : List[Any]):
"""simple docstring"""
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase : str = BioGptModel.from_pretrained(UpperCamelCase_)
self.assertIsNotNone(UpperCamelCase_)
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : Any = 3
__UpperCAmelCase : Union[str, Any] = input_dict["input_ids"]
__UpperCAmelCase : Any = input_ids.ne(1).to(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size)
__UpperCAmelCase : Dict = BioGptForSequenceClassification(UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
__UpperCAmelCase : Any = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_)
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase : int = 3
__UpperCAmelCase : List[Any] = "multi_label_classification"
__UpperCAmelCase : str = input_dict["input_ids"]
__UpperCAmelCase : Any = input_ids.ne(1).to(UpperCamelCase_)
__UpperCAmelCase : List[str] = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size).to(torch.float)
__UpperCAmelCase : List[Any] = BioGptForSequenceClassification(UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
__UpperCAmelCase : str = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_)
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))
@require_torch
class a__ ( unittest.TestCase ):
@slow
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
__UpperCAmelCase : Optional[Any] = torch.tensor([[2, 4805, 9, 656, 21]])
__UpperCAmelCase : Any = model(UpperCamelCase_)[0]
__UpperCAmelCase : Any = 42384
__UpperCAmelCase : str = torch.Size((1, 5, vocab_size))
self.assertEqual(output.shape , UpperCamelCase_)
__UpperCAmelCase : List[Any] = torch.tensor(
[[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]])
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCamelCase_ , atol=1e-4))
@slow
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : int = BioGptTokenizer.from_pretrained("microsoft/biogpt")
__UpperCAmelCase : Union[str, Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
model.to(UpperCamelCase_)
torch.manual_seed(0)
__UpperCAmelCase : Any = tokenizer("COVID-19 is" , return_tensors="pt").to(UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = model.generate(
**UpperCamelCase_ , min_length=100 , max_length=1024 , num_beams=5 , early_stopping=UpperCamelCase_ , )
__UpperCAmelCase : Dict = tokenizer.decode(output_ids[0] , skip_special_tokens=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = (
"COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
" causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
" territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
" and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
" more than 800,000 deaths."
)
self.assertEqual(UpperCamelCase_ , UpperCamelCase_)
| 77 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = "cpu" , UpperCamelCase = None ) -> None:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = torch.load(UpperCamelCase , map_location=UpperCamelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(UpperCamelCase , torch.Tensor ):
raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin" )
__UpperCAmelCase : Optional[Any] = v.half()
if save_path is None: # overwrite src_path
__UpperCAmelCase : str = src_path
torch.save(UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 77 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
A = list[tuple[int, int]]
A = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
A = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class a__ :
def __init__( self : Optional[int] , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : Node | None):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = pos_x
__UpperCAmelCase : List[str] = pos_y
__UpperCAmelCase : Optional[int] = (pos_y, pos_x)
__UpperCAmelCase : Union[str, Any] = goal_x
__UpperCAmelCase : Optional[Any] = goal_y
__UpperCAmelCase : List[str] = parent
class a__ :
def __init__( self : Optional[int] , UpperCamelCase_ : tuple[int, int] , UpperCamelCase_ : tuple[int, int]):
"""simple docstring"""
__UpperCAmelCase : Tuple = Node(start[1] , start[0] , goal[1] , goal[0] , UpperCamelCase_)
__UpperCAmelCase : List[str] = Node(goal[1] , goal[0] , goal[1] , goal[0] , UpperCamelCase_)
__UpperCAmelCase : int = [self.start]
__UpperCAmelCase : Tuple = False
def a_ ( self : str):
"""simple docstring"""
while self.node_queue:
__UpperCAmelCase : Dict = self.node_queue.pop(0)
if current_node.pos == self.target.pos:
__UpperCAmelCase : List[str] = True
return self.retrace_path(UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = self.get_successors(UpperCamelCase_)
for node in successors:
self.node_queue.append(UpperCamelCase_)
if not self.reached:
return [self.start.pos]
return None
def a_ ( self : Tuple , UpperCamelCase_ : Node):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = []
for action in delta:
__UpperCAmelCase : Union[str, Any] = parent.pos_x + action[1]
__UpperCAmelCase : Dict = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(UpperCamelCase_) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(UpperCamelCase_ , UpperCamelCase_ , self.target.pos_y , self.target.pos_x , UpperCamelCase_))
return successors
def a_ ( self : Any , UpperCamelCase_ : Node | None):
"""simple docstring"""
__UpperCAmelCase : str = node
__UpperCAmelCase : Dict = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x))
__UpperCAmelCase : List[Any] = current_node.parent
path.reverse()
return path
class a__ :
def __init__( self : Tuple , UpperCamelCase_ : int , UpperCamelCase_ : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = BreadthFirstSearch(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : List[str] = BreadthFirstSearch(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : int = False
def a_ ( self : Any):
"""simple docstring"""
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
__UpperCAmelCase : Any = self.fwd_bfs.node_queue.pop(0)
__UpperCAmelCase : str = self.bwd_bfs.node_queue.pop(0)
if current_bwd_node.pos == current_fwd_node.pos:
__UpperCAmelCase : List[Any] = True
return self.retrace_bidirectional_path(
UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : List[str] = current_bwd_node
__UpperCAmelCase : List[Any] = current_fwd_node
__UpperCAmelCase : Dict = {
self.fwd_bfs: self.fwd_bfs.get_successors(UpperCamelCase_),
self.bwd_bfs: self.bwd_bfs.get_successors(UpperCamelCase_),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(UpperCamelCase_)
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def a_ ( self : List[Any] , UpperCamelCase_ : Node , UpperCamelCase_ : Node):
"""simple docstring"""
__UpperCAmelCase : str = self.fwd_bfs.retrace_path(UpperCamelCase_)
__UpperCAmelCase : int = self.bwd_bfs.retrace_path(UpperCamelCase_)
bwd_path.pop()
bwd_path.reverse()
__UpperCAmelCase : Optional[Any] = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
A = (0, 0)
A = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
A = time.time()
A = BreadthFirstSearch(init, goal)
A = bfs.search()
A = time.time() - start_bfs_time
print("""Unidirectional BFS computation time : """, bfs_time)
A = time.time()
A = BidirectionalBreadthFirstSearch(init, goal)
A = bd_bfs.search()
A = time.time() - start_bd_bfs_time
print("""Bidirectional BFS computation time : """, bd_bfs_time)
| 77 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = df.shape[:1][0]
# If you're using some other dataset input the target column
A = df.iloc[:, 1:2]
A = actual_data.values.reshape(len_data, 1)
A = MinMaxScaler().fit_transform(actual_data)
A = 10
A = 5
A = 20
A = len_data - periods * look_back
A = actual_data[:division]
A = actual_data[division - look_back :]
A , A = [], []
A , A = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
A = np.array(train_x)
A = np.array(test_x)
A = np.array([list(i.ravel()) for i in train_y])
A = np.array([list(i.ravel()) for i in test_y])
A = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="""mean_squared_error""", optimizer="""adam""")
A = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
A = model.predict(x_test)
| 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : Any = ""
for word_or_phrase in separated:
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise Exception("join() accepts only strings to be joined" )
joined += word_or_phrase + separator
return joined.strip(UpperCamelCase )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 77 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
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__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = MBartTokenizer
lowercase_ = MBartTokenizerFast
lowercase_ = True
lowercase_ = True
def a_ ( self : str):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__UpperCAmelCase : Any = MBartTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_)
tokenizer.save_pretrained(self.tmpdirname)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Dict = MBartTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = tokenizer.tokenize("This is a test")
self.assertListEqual(UpperCamelCase_ , ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCamelCase_) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__UpperCAmelCase : List[Any] = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
UpperCamelCase_ , [
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 : Any = tokenizer.convert_tokens_to_ids(UpperCamelCase_)
self.assertListEqual(
UpperCamelCase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(UpperCamelCase_)
self.assertListEqual(
UpperCamelCase_ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def a_ ( self : Dict):
"""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 : Dict = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
__UpperCAmelCase : List[str] = self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : int = self.tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : int = tempfile.mkdtemp()
__UpperCAmelCase : Optional[int] = tokenizer_r.save_pretrained(UpperCamelCase_)
__UpperCAmelCase : Any = tokenizer_p.save_pretrained(UpperCamelCase_)
# 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(UpperCamelCase_ , UpperCamelCase_)
# Checks everything loads correctly in the same way
__UpperCAmelCase : int = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : Tuple = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCamelCase_)
# Save tokenizer rust, legacy_format=True
__UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
__UpperCAmelCase : Dict = tokenizer_r.save_pretrained(UpperCamelCase_ , legacy_format=UpperCamelCase_)
__UpperCAmelCase : int = tokenizer_p.save_pretrained(UpperCamelCase_)
# Checks it save with the same files
self.assertSequenceEqual(UpperCamelCase_ , UpperCamelCase_)
# Checks everything loads correctly in the same way
__UpperCAmelCase : int = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
shutil.rmtree(UpperCamelCase_)
# Save tokenizer rust, legacy_format=False
__UpperCAmelCase : Tuple = tempfile.mkdtemp()
__UpperCAmelCase : int = tokenizer_r.save_pretrained(UpperCamelCase_ , legacy_format=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = tokenizer_p.save_pretrained(UpperCamelCase_)
# 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 : Optional[Any] = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : str = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
shutil.rmtree(UpperCamelCase_)
@require_torch
@require_sentencepiece
@require_tokenizers
class a__ ( unittest.TestCase ):
lowercase_ = "facebook/mbart-large-en-ro"
lowercase_ = [
" 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.",
]
lowercase_ = [
"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.",
]
lowercase_ = [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]
@classmethod
def a_ ( cls : int):
"""simple docstring"""
__UpperCAmelCase : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en_XX" , tgt_lang="ro_RO")
__UpperCAmelCase : Union[str, Any] = 1
return cls
def a_ ( self : List[Any]):
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] , 250001)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] , 250004)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] , 250020)
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase_)
def a_ ( self : Optional[int]):
"""simple docstring"""
self.assertIn(UpperCamelCase_ , self.tokenizer.all_special_ids)
__UpperCAmelCase : Union[str, Any] = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
__UpperCAmelCase : Optional[Any] = self.tokenizer.decode(UpperCamelCase_ , skip_special_tokens=UpperCamelCase_)
__UpperCAmelCase : int = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , UpperCamelCase_)
self.assertNotIn(self.tokenizer.eos_token , UpperCamelCase_)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , UpperCamelCase_)
__UpperCAmelCase : Tuple = 10
__UpperCAmelCase : List[Any] = self.tokenizer(UpperCamelCase_ , max_length=UpperCamelCase_ , truncation=UpperCamelCase_).input_ids[0]
self.assertEqual(ids[-2] , 2)
self.assertEqual(ids[-1] , UpperCamelCase_)
self.assertEqual(len(UpperCamelCase_) , UpperCamelCase_)
def a_ ( self : Any):
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]) , [250026, 250001])
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = tempfile.mkdtemp()
__UpperCAmelCase : Union[str, Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCamelCase_)
__UpperCAmelCase : List[Any] = MBartTokenizer.from_pretrained(UpperCamelCase_)
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , UpperCamelCase_)
@require_torch
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCamelCase_ , 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][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=len(self.expected_src_tokens) , return_tensors="pt" , )
__UpperCAmelCase : Tuple = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id)
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_)
self.assertEqual((2, 14) , batch.input_ids.shape)
self.assertEqual((2, 14) , batch.attention_mask.shape)
__UpperCAmelCase : List[str] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase_)
self.assertEqual(2 , batch.decoder_input_ids[0, -1]) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [])
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE])
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : List[str] = self.tokenizer(self.src_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=3 , return_tensors="pt")
__UpperCAmelCase : Any = self.tokenizer(
text_target=self.tgt_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=10 , return_tensors="pt")
__UpperCAmelCase : int = targets["input_ids"]
__UpperCAmelCase : Any = shift_tokens_right(UpperCamelCase_ , self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1] , 3)
self.assertEqual(batch.decoder_input_ids.shape[1] , 10)
@require_torch
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : int = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="ar_AR")
self.assertEqual(
nested_simplify(UpperCamelCase_) , {
# A, test, EOS, en_XX
"input_ids": [[62, 3034, 2, 250004]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 250001,
} , )
| 77 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Any , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : int , *UpperCamelCase_ : Dict , **UpperCamelCase_ : int):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : List[Any] , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Optional[Any] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : List[str] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Tuple , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Dict , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : Tuple):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Union[str, Any] , *UpperCamelCase_ : Any , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Optional[Any] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[Any] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Union[str, Any] , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Any , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[str] , *UpperCamelCase_ : int , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Union[str, Any] , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : List[str] , *UpperCamelCase_ : Tuple , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Any , *UpperCamelCase_ : int , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : str , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : List[str] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[str] , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Dict , *UpperCamelCase_ : Any , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : int , *UpperCamelCase_ : Dict , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[str] , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Tuple , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Optional[int] , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[str] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Tuple , *UpperCamelCase_ : Any , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Optional[int] , *UpperCamelCase_ : str , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[Any] , *UpperCamelCase_ : Any , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Any , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Any , *UpperCamelCase_ : Any , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[Any] , *UpperCamelCase_ : str , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : List[str] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Optional[int] , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
def _UpperCamelCase ( *UpperCamelCase , **UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
requires_backends(UpperCamelCase , ["torch"] )
def _UpperCamelCase ( *UpperCamelCase , **UpperCamelCase ) -> List[Any]:
"""simple docstring"""
requires_backends(UpperCamelCase , ["torch"] )
def _UpperCamelCase ( *UpperCamelCase , **UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
requires_backends(UpperCamelCase , ["torch"] )
def _UpperCamelCase ( *UpperCamelCase , **UpperCamelCase ) -> Dict:
"""simple docstring"""
requires_backends(UpperCamelCase , ["torch"] )
def _UpperCamelCase ( *UpperCamelCase , **UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
requires_backends(UpperCamelCase , ["torch"] )
def _UpperCamelCase ( *UpperCamelCase , **UpperCamelCase ) -> List[Any]:
"""simple docstring"""
requires_backends(UpperCamelCase , ["torch"] )
def _UpperCamelCase ( *UpperCamelCase , **UpperCamelCase ) -> List[Any]:
"""simple docstring"""
requires_backends(UpperCamelCase , ["torch"] )
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Optional[int] , *UpperCamelCase_ : Optional[int] , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Optional[int] , *UpperCamelCase_ : Any , **UpperCamelCase_ : Tuple):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : str , *UpperCamelCase_ : Tuple , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Tuple , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : int):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Union[str, Any] , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Tuple , *UpperCamelCase_ : Dict , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Tuple , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : List[str] , *UpperCamelCase_ : Any , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : str , *UpperCamelCase_ : int , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Optional[int] , *UpperCamelCase_ : int , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : List[str] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Union[str, Any] , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[Any] , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : List[str] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : int , *UpperCamelCase_ : Tuple , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Union[str, Any] , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Union[str, Any] , *UpperCamelCase_ : Optional[int] , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Dict , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[Any] , *UpperCamelCase_ : int , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Optional[Any] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : List[str] , *UpperCamelCase_ : str , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Optional[Any] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : int , *UpperCamelCase_ : Tuple , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : str , *UpperCamelCase_ : Any , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[Any] , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Union[str, Any] , *UpperCamelCase_ : str , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Optional[int] , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[Any] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Any , *UpperCamelCase_ : Dict , **UpperCamelCase_ : int):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : List[Any] , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[Any] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : int , *UpperCamelCase_ : Optional[int] , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Union[str, Any] , *UpperCamelCase_ : Any , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Dict , *UpperCamelCase_ : Tuple , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Optional[int] , *UpperCamelCase_ : Any , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Optional[int] , *UpperCamelCase_ : str , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[str] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Optional[int] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Optional[int] , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Tuple , *UpperCamelCase_ : Any , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : str , *UpperCamelCase_ : str , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Optional[Any] , *UpperCamelCase_ : str , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[Any] , *UpperCamelCase_ : Tuple , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Dict , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : int , *UpperCamelCase_ : str , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Optional[Any] , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Any , *UpperCamelCase_ : str , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Optional[Any] , *UpperCamelCase_ : int , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : int , *UpperCamelCase_ : Any , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Tuple , *UpperCamelCase_ : str , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Dict , *UpperCamelCase_ : Tuple , **UpperCamelCase_ : int):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : int , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : List[Any] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Any , *UpperCamelCase_ : Dict , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[str] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Tuple , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Optional[Any] , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : int):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Any , *UpperCamelCase_ : Any , **UpperCamelCase_ : int):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Dict , *UpperCamelCase_ : Any , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : int , *UpperCamelCase_ : Optional[int] , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Optional[int] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Tuple , *UpperCamelCase_ : Tuple , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : str , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : int):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[str] , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : int):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Tuple , *UpperCamelCase_ : Any , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Any , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Dict , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : int , *UpperCamelCase_ : Dict , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Optional[Any] , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Any , *UpperCamelCase_ : Any , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : List[str] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : List[str] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Dict , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Dict , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Dict , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : Tuple):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Any , *UpperCamelCase_ : str , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : List[Any] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Tuple):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Any , *UpperCamelCase_ : str , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[Any] , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : int , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Any , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[str] , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : List[Any] , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Union[str, Any] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Union[str, Any] , *UpperCamelCase_ : Any , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Any , *UpperCamelCase_ : Any , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : int , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Optional[int] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Optional[int] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : int , *UpperCamelCase_ : Any , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Optional[Any] , *UpperCamelCase_ : Any , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Optional[Any] , *UpperCamelCase_ : str , **UpperCamelCase_ : Tuple):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Dict , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Optional[Any] , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Dict , *UpperCamelCase_ : str , **UpperCamelCase_ : Tuple):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Any , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Tuple , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Any):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : str , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Any , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : List[str] , *UpperCamelCase_ : Any , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Optional[int] , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : int):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Optional[Any] , *UpperCamelCase_ : Optional[int] , **UpperCamelCase_ : int):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Optional[int] , *UpperCamelCase_ : Any , **UpperCamelCase_ : int):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : List[Any] , *UpperCamelCase_ : Optional[int] , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Tuple , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Union[str, Any] , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Union[str, Any] , *UpperCamelCase_ : Optional[int] , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Union[str, Any] , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Optional[int] , *UpperCamelCase_ : int , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Any , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : str , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : Tuple):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Optional[Any] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Dict , *UpperCamelCase_ : Optional[int] , **UpperCamelCase_ : Dict):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Dict , *UpperCamelCase_ : List[Any] , **UpperCamelCase_ : int):
"""simple docstring"""
requires_backends(cls , ["torch"])
class a__ ( metaclass=__magic_name__ ):
lowercase_ = ["torch"]
def __init__( self : Tuple , *UpperCamelCase_ : Tuple , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
requires_backends(self , ["torch"])
@classmethod
def a_ ( cls : Dict , *UpperCamelCase_ : Dict , **UpperCamelCase_ : List[str]):
"""simple docstring"""
requires_backends(cls , ["torch"])
@classmethod
def a_ ( cls : Union[str, Any] , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : str):
"""simple docstring"""
requires_backends(cls , ["torch"])
| 77 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
class a__ :
def __init__( self : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = None
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.head
while temp is not None:
print(temp.data , end=" ")
__UpperCAmelCase : Tuple = temp.next
print()
def a_ ( self : int , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : List[str] = Node(UpperCamelCase_)
__UpperCAmelCase : str = self.head
__UpperCAmelCase : Optional[int] = new_node
def a_ ( self : str , UpperCamelCase_ : List[Any] , UpperCamelCase_ : str):
"""simple docstring"""
if node_data_a == node_data_a:
return
else:
__UpperCAmelCase : int = self.head
while node_a is not None and node_a.data != node_data_a:
__UpperCAmelCase : Tuple = node_a.next
__UpperCAmelCase : List[Any] = self.head
while node_a is not None and node_a.data != node_data_a:
__UpperCAmelCase : Optional[Any] = node_a.next
if node_a is None or node_a is None:
return
__UpperCAmelCase , __UpperCAmelCase : Any = 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()
| 77 | 1 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : Optional[Any] , UpperCamelCase_ : List[Any]="</s>" , UpperCamelCase_ : Tuple="<unk>" , UpperCamelCase_ : List[str]="<pad>" , UpperCamelCase_ : Union[str, Any]=125 , UpperCamelCase_ : Dict=None , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
if extra_ids > 0 and additional_special_tokens is None:
__UpperCAmelCase : int = [F"<extra_id_{i}>" for i in range(UpperCamelCase_)]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__UpperCAmelCase : Dict = len(set(filter(lambda UpperCamelCase_: bool("extra_id" in str(UpperCamelCase_)) , UpperCamelCase_)))
if extra_tokens != extra_ids:
raise ValueError(
F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
" extra_ids tokens")
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else pad_token
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else eos_token
__UpperCAmelCase : Optional[int] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else unk_token
super().__init__(
eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , extra_ids=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : List[str] = extra_ids
__UpperCAmelCase : int = 2**8 # utf is 8 bits
# define special tokens dict
__UpperCAmelCase : Dict[int, str] = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
__UpperCAmelCase : Any = len(self.special_tokens_encoder)
__UpperCAmelCase : List[Any] = len(UpperCamelCase_)
for i, token in enumerate(UpperCamelCase_):
__UpperCAmelCase : Union[str, Any] = self.vocab_size + i - n
__UpperCAmelCase : Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def a_ ( self : List[Any]):
"""simple docstring"""
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def a_ ( self : List[str] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase_ , token_ids_a=UpperCamelCase_ , already_has_special_tokens=UpperCamelCase_)
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(UpperCamelCase_)) + [1]
return ([0] * len(UpperCamelCase_)) + [1] + ([0] * len(UpperCamelCase_)) + [1]
def a_ ( self : Optional[Any] , UpperCamelCase_ : List[int]):
"""simple docstring"""
if len(UpperCamelCase_) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
" eos tokens being added.")
return token_ids
else:
return token_ids + [self.eos_token_id]
def a_ ( self : Dict , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Dict = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos) * [0]
return len(token_ids_a + eos + token_ids_a + eos) * [0]
def a_ ( self : Optional[int] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self._add_eos_if_not_present(UpperCamelCase_)
if token_ids_a is None:
return token_ids_a
else:
__UpperCAmelCase : List[Any] = self._add_eos_if_not_present(UpperCamelCase_)
return token_ids_a + token_ids_a
def a_ ( self : List[str] , UpperCamelCase_ : str):
"""simple docstring"""
__UpperCAmelCase : Any = [chr(UpperCamelCase_) for i in text.encode("utf-8")]
return tokens
def a_ ( self : Tuple , UpperCamelCase_ : List[Any]):
"""simple docstring"""
if token in self.special_tokens_encoder:
__UpperCAmelCase : Any = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
__UpperCAmelCase : int = self.added_tokens_encoder[token]
elif len(UpperCamelCase_) != 1:
__UpperCAmelCase : Optional[Any] = self.unk_token_id
else:
__UpperCAmelCase : Any = ord(UpperCamelCase_) + self._num_special_tokens
return token_id
def a_ ( self : Any , UpperCamelCase_ : List[str]):
"""simple docstring"""
if index in self.special_tokens_decoder:
__UpperCAmelCase : Any = self.special_tokens_decoder[index]
else:
__UpperCAmelCase : List[str] = chr(index - self._num_special_tokens)
return token
def a_ ( self : Dict , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : str = b""
for token in tokens:
if token in self.special_tokens_decoder:
__UpperCAmelCase : Tuple = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.added_tokens_decoder:
__UpperCAmelCase : Any = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.special_tokens_encoder:
__UpperCAmelCase : Optional[int] = token.encode("utf-8")
elif token in self.added_tokens_encoder:
__UpperCAmelCase : Optional[Any] = token.encode("utf-8")
else:
__UpperCAmelCase : Any = bytes([ord(UpperCamelCase_)])
bstring += tok_string
__UpperCAmelCase : List[Any] = bstring.decode("utf-8" , errors="ignore")
return string
def a_ ( self : Optional[int] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
return ()
| 77 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
A = """
Human: <<task>>
Assistant: """
A = """huggingface-tools/default-prompts"""
A = {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase="run" ) -> List[str]:
"""simple docstring"""
if prompt_or_repo_id is None:
__UpperCAmelCase : Optional[int] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , UpperCamelCase ) is not None:
return prompt_or_repo_id
__UpperCAmelCase : str = cached_file(
UpperCamelCase , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(UpperCamelCase , "r" , encoding="utf-8" ) as f:
return f.read()
| 77 | 1 |
"""simple docstring"""
import pytest
A = """__dummy_dataset1__"""
A = """
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
\"tokens\": datasets.Sequence(datasets.Value(\"string\")),
\"ner_tags\": datasets.Sequence(
datasets.features.ClassLabel(
names=[
\"O\",
\"B-PER\",
\"I-PER\",
\"B-ORG\",
\"I-ORG\",
\"B-LOC\",
\"I-LOC\",
]
)
),
\"langs\": datasets.Sequence(datasets.Value(\"string\")),
\"spans\": datasets.Sequence(datasets.Value(\"string\")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),
]
def _generate_examples(self, filepath):
with open(filepath, \"r\", encoding=\"utf-8\") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
"""
@pytest.fixture
def _UpperCamelCase ( ) -> Any:
"""simple docstring"""
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def _UpperCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[Any]:
"""simple docstring"""
__UpperCAmelCase : int = dataset_loading_script_name
__UpperCAmelCase : Union[str, Any] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=UpperCamelCase )
__UpperCAmelCase : List[str] = script_dir / f"{script_name}.py"
with open(UpperCamelCase , "w" ) as f:
f.write(UpperCamelCase )
return str(UpperCamelCase )
| 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ErnieForCausalLM""",
"""ErnieForMaskedLM""",
"""ErnieForMultipleChoice""",
"""ErnieForNextSentencePrediction""",
"""ErnieForPreTraining""",
"""ErnieForQuestionAnswering""",
"""ErnieForSequenceClassification""",
"""ErnieForTokenClassification""",
"""ErnieModel""",
"""ErniePreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 | 1 |
"""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__)
A = {
"""facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}
class a__ ( __magic_name__ , __magic_name__ ):
lowercase_ = "convnextv2"
def __init__( self : Any , UpperCamelCase_ : str=3 , UpperCamelCase_ : Any=4 , UpperCamelCase_ : Optional[Any]=4 , UpperCamelCase_ : List[Any]=None , UpperCamelCase_ : Union[str, Any]=None , UpperCamelCase_ : Optional[Any]="gelu" , UpperCamelCase_ : Tuple=0.02 , UpperCamelCase_ : int=1e-12 , UpperCamelCase_ : Any=0.0 , UpperCamelCase_ : Dict=224 , UpperCamelCase_ : Optional[Any]=None , UpperCamelCase_ : Dict=None , **UpperCamelCase_ : Tuple , ):
"""simple docstring"""
super().__init__(**UpperCamelCase_)
__UpperCAmelCase : Tuple = num_channels
__UpperCAmelCase : Any = patch_size
__UpperCAmelCase : Dict = num_stages
__UpperCAmelCase : Any = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
__UpperCAmelCase : List[Any] = [3, 3, 9, 3] if depths is None else depths
__UpperCAmelCase : Optional[Any] = hidden_act
__UpperCAmelCase : Any = initializer_range
__UpperCAmelCase : Any = layer_norm_eps
__UpperCAmelCase : List[str] = drop_path_rate
__UpperCAmelCase : List[Any] = image_size
__UpperCAmelCase : Optional[Any] = ["stem"] + [F"stage{idx}" for idx in range(1 , len(self.depths) + 1)]
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = get_aligned_output_features_output_indices(
out_features=UpperCamelCase_ , out_indices=UpperCamelCase_ , stage_names=self.stage_names)
| 77 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ( nn.Module ):
def __init__( self : Union[str, Any]):
"""simple docstring"""
super().__init__()
__UpperCAmelCase : Optional[int] = nn.Linear(3 , 4)
__UpperCAmelCase : str = nn.BatchNormad(4)
__UpperCAmelCase : int = nn.Linear(4 , 5)
def a_ ( self : str , UpperCamelCase_ : List[str]):
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(UpperCamelCase_)))
class a__ ( unittest.TestCase ):
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = ModelForTest()
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , model.state_dict())
__UpperCAmelCase : Union[str, Any] = os.path.join(UpperCamelCase_ , "index.json")
self.assertTrue(os.path.isfile(UpperCamelCase_))
# TODO: add tests on what is inside the index
for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
__UpperCAmelCase : Optional[int] = os.path.join(UpperCamelCase_ , F"{key}.dat")
self.assertTrue(os.path.isfile(UpperCamelCase_))
# TODO: add tests on the fact weights are properly loaded
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : int = [torch.floataa, torch.floataa, torch.bfloataa]
for dtype in dtypes:
__UpperCAmelCase : List[Any] = torch.randn(2 , 3 , dtype=UpperCamelCase_)
with TemporaryDirectory() as tmp_dir:
__UpperCAmelCase : Tuple = offload_weight(UpperCamelCase_ , "weight" , UpperCamelCase_ , {})
__UpperCAmelCase : Dict = os.path.join(UpperCamelCase_ , "weight.dat")
self.assertTrue(os.path.isfile(UpperCamelCase_))
self.assertDictEqual(UpperCamelCase_ , {"weight": {"shape": [2, 3], "dtype": str(UpperCamelCase_).split(".")[1]}})
__UpperCAmelCase : Optional[Any] = load_offloaded_weight(UpperCamelCase_ , index["weight"])
self.assertTrue(torch.equal(UpperCamelCase_ , UpperCamelCase_))
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = ModelForTest()
__UpperCAmelCase : Optional[int] = model.state_dict()
__UpperCAmelCase : List[str] = {k: v for k, v in state_dict.items() if "linear2" not in k}
__UpperCAmelCase : Optional[int] = {k: v for k, v in state_dict.items() if "linear2" in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : List[str] = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
__UpperCAmelCase : Optional[int] = {k: v for k, v in state_dict.items() if "weight" in k}
__UpperCAmelCase : Optional[Any] = {k: v for k, v in state_dict.items() if "weight" not in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
# Duplicates are removed
__UpperCAmelCase : str = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Any = {"a.1": 0, "a.10": 1, "a.2": 2}
__UpperCAmelCase : Union[str, Any] = extract_submodules_state_dict(UpperCamelCase_ , ["a.1", "a.2"])
self.assertDictEqual(UpperCamelCase_ , {"a.1": 0, "a.2": 2})
__UpperCAmelCase : int = {"a.1.a": 0, "a.10.a": 1, "a.2.a": 2}
__UpperCAmelCase : int = extract_submodules_state_dict(UpperCamelCase_ , ["a.1", "a.2"])
self.assertDictEqual(UpperCamelCase_ , {"a.1.a": 0, "a.2.a": 2})
| 77 | 1 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
class a__ :
def __init__( self : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = None
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.head
while temp is not None:
print(temp.data , end=" ")
__UpperCAmelCase : Tuple = temp.next
print()
def a_ ( self : int , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : List[str] = Node(UpperCamelCase_)
__UpperCAmelCase : str = self.head
__UpperCAmelCase : Optional[int] = new_node
def a_ ( self : str , UpperCamelCase_ : List[Any] , UpperCamelCase_ : str):
"""simple docstring"""
if node_data_a == node_data_a:
return
else:
__UpperCAmelCase : int = self.head
while node_a is not None and node_a.data != node_data_a:
__UpperCAmelCase : Tuple = node_a.next
__UpperCAmelCase : List[Any] = self.head
while node_a is not None and node_a.data != node_data_a:
__UpperCAmelCase : Optional[Any] = node_a.next
if node_a is None or node_a is None:
return
__UpperCAmelCase , __UpperCAmelCase : Any = 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()
| 77 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelCase : Union[str, Any] = n - k
# Calculate C(n,k)
for i in range(UpperCamelCase ):
result *= n - i
result //= i + 1
return result
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
return binomial_coefficient(2 * node_count , UpperCamelCase ) // (node_count + 1)
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
if n < 0:
raise ValueError("factorial() not defined for negative values" )
__UpperCAmelCase : Optional[Any] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
return catalan_number(UpperCamelCase ) * factorial(UpperCamelCase )
if __name__ == "__main__":
A = int(input("""Enter the number of nodes: """).strip() or 0)
if node_count <= 0:
raise ValueError("""We need some nodes to work with.""")
print(
f'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
f'''binary trees and {catalan_number(node_count)} binary search trees.'''
)
| 77 | 1 |
"""simple docstring"""
A = """Alexander Joslin"""
import operator as op
from .stack import Stack
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[int] = {"*": 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(UpperCamelCase ) )
elif i in operators:
# RULE 2
operator_stack.push(UpperCamelCase )
elif i == ")":
# RULE 4
__UpperCAmelCase : Dict = operator_stack.peek()
operator_stack.pop()
__UpperCAmelCase : List[str] = operand_stack.peek()
operand_stack.pop()
__UpperCAmelCase : List[Any] = operand_stack.peek()
operand_stack.pop()
__UpperCAmelCase : Any = operators[opr](UpperCamelCase , UpperCamelCase )
operand_stack.push(UpperCamelCase )
# 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)}''')
| 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
"""processing_trocr""": ["""TrOCRProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TrOCRForCausalLM""",
"""TrOCRPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self : Optional[Any] , UpperCamelCase_ : int , UpperCamelCase_ : Any=13 , UpperCamelCase_ : Optional[int]=3 , UpperCamelCase_ : int=224 , UpperCamelCase_ : int=30 , UpperCamelCase_ : str=400 , UpperCamelCase_ : List[Any]=True , UpperCamelCase_ : Optional[Any]=None , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Optional[int]=[0.5, 0.5, 0.5] , UpperCamelCase_ : Optional[Any]=[0.5, 0.5, 0.5] , ):
"""simple docstring"""
__UpperCAmelCase : Tuple = size if size is not None else {"height": 18, "width": 18}
__UpperCAmelCase : List[Any] = parent
__UpperCAmelCase : Tuple = batch_size
__UpperCAmelCase : Tuple = num_channels
__UpperCAmelCase : List[Any] = image_size
__UpperCAmelCase : str = min_resolution
__UpperCAmelCase : Tuple = max_resolution
__UpperCAmelCase : Optional[Any] = do_resize
__UpperCAmelCase : Any = size
__UpperCAmelCase : Any = do_normalize
__UpperCAmelCase : Any = image_mean
__UpperCAmelCase : Optional[Any] = image_std
def a_ ( self : str):
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = ViTImageProcessor if is_vision_available() else None
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = EfficientFormerImageProcessorTester(self)
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
return self.image_proc_tester.prepare_image_processor_dict()
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(UpperCamelCase_ , "image_mean"))
self.assertTrue(hasattr(UpperCamelCase_ , "image_std"))
self.assertTrue(hasattr(UpperCamelCase_ , "do_normalize"))
self.assertTrue(hasattr(UpperCamelCase_ , "do_resize"))
self.assertTrue(hasattr(UpperCamelCase_ , "size"))
def a_ ( self : Dict):
"""simple docstring"""
pass
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
__UpperCAmelCase : str = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , Image.Image)
# Test not batched input
__UpperCAmelCase : Optional[int] = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Optional[int] = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : int = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
__UpperCAmelCase : Union[str, Any] = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_ , numpify=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , np.ndarray)
# Test not batched input
__UpperCAmelCase : Tuple = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Any = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
__UpperCAmelCase : Any = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_ , torchify=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , torch.Tensor)
# Test not batched input
__UpperCAmelCase : Optional[Any] = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Optional[int] = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
| 77 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self : Optional[Any] , UpperCamelCase_ : int , UpperCamelCase_ : Any=13 , UpperCamelCase_ : Optional[int]=3 , UpperCamelCase_ : int=224 , UpperCamelCase_ : int=30 , UpperCamelCase_ : str=400 , UpperCamelCase_ : List[Any]=True , UpperCamelCase_ : Optional[Any]=None , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Optional[int]=[0.5, 0.5, 0.5] , UpperCamelCase_ : Optional[Any]=[0.5, 0.5, 0.5] , ):
"""simple docstring"""
__UpperCAmelCase : Tuple = size if size is not None else {"height": 18, "width": 18}
__UpperCAmelCase : List[Any] = parent
__UpperCAmelCase : Tuple = batch_size
__UpperCAmelCase : Tuple = num_channels
__UpperCAmelCase : List[Any] = image_size
__UpperCAmelCase : str = min_resolution
__UpperCAmelCase : Tuple = max_resolution
__UpperCAmelCase : Optional[Any] = do_resize
__UpperCAmelCase : Any = size
__UpperCAmelCase : Any = do_normalize
__UpperCAmelCase : Any = image_mean
__UpperCAmelCase : Optional[Any] = image_std
def a_ ( self : str):
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = ViTImageProcessor if is_vision_available() else None
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = EfficientFormerImageProcessorTester(self)
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
return self.image_proc_tester.prepare_image_processor_dict()
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(UpperCamelCase_ , "image_mean"))
self.assertTrue(hasattr(UpperCamelCase_ , "image_std"))
self.assertTrue(hasattr(UpperCamelCase_ , "do_normalize"))
self.assertTrue(hasattr(UpperCamelCase_ , "do_resize"))
self.assertTrue(hasattr(UpperCamelCase_ , "size"))
def a_ ( self : Dict):
"""simple docstring"""
pass
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
__UpperCAmelCase : str = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , Image.Image)
# Test not batched input
__UpperCAmelCase : Optional[int] = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Optional[int] = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : int = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
__UpperCAmelCase : Union[str, Any] = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_ , numpify=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , np.ndarray)
# Test not batched input
__UpperCAmelCase : Tuple = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Any = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
__UpperCAmelCase : Any = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_ , torchify=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , torch.Tensor)
# Test not batched input
__UpperCAmelCase : Optional[Any] = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Optional[int] = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
| 77 | 1 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
A = logging.get_logger(__name__)
A = {
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
# See all BART models at https://huggingface.co/models?filter=bart
}
class a__ ( __magic_name__ ):
lowercase_ = "bart"
lowercase_ = ["past_key_values"]
lowercase_ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : List[Any] , UpperCamelCase_ : Optional[Any]=50265 , UpperCamelCase_ : int=1024 , UpperCamelCase_ : List[str]=12 , UpperCamelCase_ : List[str]=4096 , UpperCamelCase_ : int=16 , UpperCamelCase_ : Optional[int]=12 , UpperCamelCase_ : Any=4096 , UpperCamelCase_ : Dict=16 , UpperCamelCase_ : Optional[Any]=0.0 , UpperCamelCase_ : int=0.0 , UpperCamelCase_ : Dict="gelu" , UpperCamelCase_ : int=1024 , UpperCamelCase_ : Optional[int]=0.1 , UpperCamelCase_ : int=0.0 , UpperCamelCase_ : Optional[Any]=0.0 , UpperCamelCase_ : Any=0.02 , UpperCamelCase_ : str=0.0 , UpperCamelCase_ : Tuple=False , UpperCamelCase_ : Dict=True , UpperCamelCase_ : Union[str, Any]=3 , UpperCamelCase_ : Optional[int]=1 , UpperCamelCase_ : Any=0 , UpperCamelCase_ : Tuple=2 , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Dict=2 , UpperCamelCase_ : Optional[int]=2 , **UpperCamelCase_ : Any , ):
"""simple docstring"""
__UpperCAmelCase : str = vocab_size
__UpperCAmelCase : Optional[int] = max_position_embeddings
__UpperCAmelCase : List[str] = d_model
__UpperCAmelCase : List[str] = encoder_ffn_dim
__UpperCAmelCase : List[str] = encoder_layers
__UpperCAmelCase : Optional[int] = encoder_attention_heads
__UpperCAmelCase : Tuple = decoder_ffn_dim
__UpperCAmelCase : List[Any] = decoder_layers
__UpperCAmelCase : Dict = decoder_attention_heads
__UpperCAmelCase : int = dropout
__UpperCAmelCase : str = attention_dropout
__UpperCAmelCase : Dict = activation_dropout
__UpperCAmelCase : str = activation_function
__UpperCAmelCase : Union[str, Any] = init_std
__UpperCAmelCase : Optional[int] = encoder_layerdrop
__UpperCAmelCase : List[str] = decoder_layerdrop
__UpperCAmelCase : Dict = classifier_dropout
__UpperCAmelCase : Any = use_cache
__UpperCAmelCase : Optional[int] = encoder_layers
__UpperCAmelCase : int = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=UpperCamelCase_ , pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , is_encoder_decoder=UpperCamelCase_ , decoder_start_token_id=UpperCamelCase_ , forced_eos_token_id=UpperCamelCase_ , **UpperCamelCase_ , )
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated" , UpperCamelCase_):
__UpperCAmelCase : Dict = 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.")
class a__ ( __magic_name__ ):
@property
def a_ ( self : int):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
__UpperCAmelCase : Optional[Any] = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
])
if self.use_past:
__UpperCAmelCase : Dict = {0: "batch"}
__UpperCAmelCase : str = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
__UpperCAmelCase : Optional[Any] = {0: "batch", 1: "decoder_sequence"}
__UpperCAmelCase : Optional[int] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(UpperCamelCase_ , direction="inputs")
elif self.task == "causal-lm":
# TODO: figure this case out.
__UpperCAmelCase : Dict = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
])
if self.use_past:
__UpperCAmelCase , __UpperCAmelCase : int = self.num_layers
for i in range(UpperCamelCase_):
__UpperCAmelCase : int = {0: "batch", 2: "past_sequence + sequence"}
__UpperCAmelCase : int = {0: "batch", 2: "past_sequence + sequence"}
else:
__UpperCAmelCase : Union[str, Any] = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
])
return common_inputs
@property
def a_ ( self : Any):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
__UpperCAmelCase : Optional[Any] = super().outputs
else:
__UpperCAmelCase : Any = super(UpperCamelCase_ , self).outputs
if self.use_past:
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = self.num_layers
for i in range(UpperCamelCase_):
__UpperCAmelCase : Tuple = {0: "batch", 2: "past_sequence + sequence"}
__UpperCAmelCase : List[str] = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def a_ ( self : List[str] , UpperCamelCase_ : PreTrainedTokenizer , UpperCamelCase_ : int = -1 , UpperCamelCase_ : int = -1 , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[TensorType] = None , ):
"""simple docstring"""
__UpperCAmelCase : List[str] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
# Generate decoder inputs
__UpperCAmelCase : str = seq_length if not self.use_past else 1
__UpperCAmelCase : Tuple = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
__UpperCAmelCase : Optional[Any] = dict(**UpperCamelCase_ , **UpperCamelCase_)
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
__UpperCAmelCase , __UpperCAmelCase : Tuple = common_inputs["input_ids"].shape
__UpperCAmelCase : str = common_inputs["decoder_input_ids"].shape[1]
__UpperCAmelCase , __UpperCAmelCase : Dict = self.num_attention_heads
__UpperCAmelCase : Dict = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__UpperCAmelCase : List[str] = decoder_seq_length + 3
__UpperCAmelCase : Union[str, Any] = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
__UpperCAmelCase : str = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(UpperCamelCase_ , UpperCamelCase_)] , dim=1)
__UpperCAmelCase : str = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
__UpperCAmelCase , __UpperCAmelCase : List[Any] = self.num_layers
__UpperCAmelCase : Any = min(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : int = max(UpperCamelCase_ , UpperCamelCase_) - min_num_layers
__UpperCAmelCase : int = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(UpperCamelCase_):
common_inputs["past_key_values"].append(
(
torch.zeros(UpperCamelCase_),
torch.zeros(UpperCamelCase_),
torch.zeros(UpperCamelCase_),
torch.zeros(UpperCamelCase_),
))
# TODO: test this.
__UpperCAmelCase : Tuple = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(UpperCamelCase_ , UpperCamelCase_):
common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase_), torch.zeros(UpperCamelCase_)))
return common_inputs
def a_ ( self : str , UpperCamelCase_ : PreTrainedTokenizer , UpperCamelCase_ : int = -1 , UpperCamelCase_ : int = -1 , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[TensorType] = None , ):
"""simple docstring"""
__UpperCAmelCase : Any = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
__UpperCAmelCase , __UpperCAmelCase : Tuple = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
__UpperCAmelCase : Dict = seqlen + 2
__UpperCAmelCase , __UpperCAmelCase : str = self.num_layers
__UpperCAmelCase , __UpperCAmelCase : Tuple = self.num_attention_heads
__UpperCAmelCase : List[Any] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__UpperCAmelCase : List[Any] = common_inputs["attention_mask"].dtype
__UpperCAmelCase : Optional[int] = torch.cat(
[common_inputs["attention_mask"], torch.ones(UpperCamelCase_ , UpperCamelCase_ , dtype=UpperCamelCase_)] , dim=1)
__UpperCAmelCase : Tuple = [
(torch.zeros(UpperCamelCase_), torch.zeros(UpperCamelCase_)) for _ in range(UpperCamelCase_)
]
return common_inputs
def a_ ( self : Tuple , UpperCamelCase_ : PreTrainedTokenizer , UpperCamelCase_ : int = -1 , UpperCamelCase_ : int = -1 , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[TensorType] = None , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = compute_effective_axis_dimension(
UpperCamelCase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0)
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
__UpperCAmelCase : Dict = tokenizer.num_special_tokens_to_add(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = compute_effective_axis_dimension(
UpperCamelCase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=UpperCamelCase_)
# Generate dummy inputs according to compute batch and sequence
__UpperCAmelCase : List[Any] = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
__UpperCAmelCase : Optional[Any] = dict(tokenizer(UpperCamelCase_ , return_tensors=UpperCamelCase_))
return common_inputs
def a_ ( self : Optional[Any] , UpperCamelCase_ : PreTrainedTokenizer , UpperCamelCase_ : int = -1 , UpperCamelCase_ : int = -1 , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[TensorType] = None , ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
__UpperCAmelCase : int = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
UpperCamelCase_ , batch_size=UpperCamelCase_ , seq_length=UpperCamelCase_ , is_pair=UpperCamelCase_ , framework=UpperCamelCase_)
elif self.task == "causal-lm":
__UpperCAmelCase : Tuple = self._generate_dummy_inputs_for_causal_lm(
UpperCamelCase_ , batch_size=UpperCamelCase_ , seq_length=UpperCamelCase_ , is_pair=UpperCamelCase_ , framework=UpperCamelCase_)
else:
__UpperCAmelCase : Dict = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
UpperCamelCase_ , batch_size=UpperCamelCase_ , seq_length=UpperCamelCase_ , is_pair=UpperCamelCase_ , framework=UpperCamelCase_)
return common_inputs
def a_ ( self : Any , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Any , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
__UpperCAmelCase : Any = super()._flatten_past_key_values_(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
else:
__UpperCAmelCase : List[str] = super(UpperCamelCase_ , self)._flatten_past_key_values_(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
| 77 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": from_to(0.76455, 1.30795),
"""cubicfoot""": from_to(0.028, 35.3147),
"""cup""": from_to(0.000236588, 4226.75),
}
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> float:
"""simple docstring"""
if from_type not in METRIC_CONVERSION:
raise ValueError(
f"Invalid 'from_type' value: {from_type!r} Supported values are:\n"
+ ", ".join(UpperCamelCase ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
f"Invalid 'to_type' value: {to_type!r}. Supported values are:\n"
+ ", ".join(UpperCamelCase ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 77 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
A = logging.get_logger(__name__) # pylint: disable=invalid-name
def _UpperCamelCase ( UpperCamelCase ) -> Any:
"""simple docstring"""
warnings.warn(
"The preprocess method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor.preprocess instead" , UpperCamelCase , )
if isinstance(UpperCamelCase , torch.Tensor ):
return image
elif isinstance(UpperCamelCase , PIL.Image.Image ):
__UpperCAmelCase : int = [image]
if isinstance(image[0] , PIL.Image.Image ):
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = image[0].size
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
__UpperCAmelCase : str = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] ) )[None, :] for i in image]
__UpperCAmelCase : Optional[int] = np.concatenate(UpperCamelCase , axis=0 )
__UpperCAmelCase : int = np.array(UpperCamelCase ).astype(np.floataa ) / 255.0
__UpperCAmelCase : Optional[int] = image.transpose(0 , 3 , 1 , 2 )
__UpperCAmelCase : Tuple = 2.0 * image - 1.0
__UpperCAmelCase : List[str] = torch.from_numpy(UpperCamelCase )
elif isinstance(image[0] , torch.Tensor ):
__UpperCAmelCase : Dict = torch.cat(UpperCamelCase , dim=0 )
return image
def _UpperCamelCase ( UpperCamelCase ) -> List[Any]:
"""simple docstring"""
if isinstance(UpperCamelCase , torch.Tensor ):
return mask
elif isinstance(UpperCamelCase , PIL.Image.Image ):
__UpperCAmelCase : str = [mask]
if isinstance(mask[0] , PIL.Image.Image ):
__UpperCAmelCase , __UpperCAmelCase : Dict = mask[0].size
__UpperCAmelCase , __UpperCAmelCase : Tuple = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
__UpperCAmelCase : Union[str, Any] = [np.array(m.convert("L" ).resize((w, h) , resample=PIL_INTERPOLATION["nearest"] ) )[None, :] for m in mask]
__UpperCAmelCase : Optional[Any] = np.concatenate(UpperCamelCase , axis=0 )
__UpperCAmelCase : Dict = mask.astype(np.floataa ) / 255.0
__UpperCAmelCase : int = 0
__UpperCAmelCase : Dict = 1
__UpperCAmelCase : List[str] = torch.from_numpy(UpperCamelCase )
elif isinstance(mask[0] , torch.Tensor ):
__UpperCAmelCase : List[str] = torch.cat(UpperCamelCase , dim=0 )
return mask
class a__ ( __magic_name__ ):
lowercase_ = 42
lowercase_ = 42
def __init__( self : List[Any] , UpperCamelCase_ : Dict , UpperCamelCase_ : List[str]):
"""simple docstring"""
super().__init__()
self.register_modules(unet=UpperCamelCase_ , scheduler=UpperCamelCase_)
@torch.no_grad()
def __call__( self : int , UpperCamelCase_ : Union[torch.Tensor, PIL.Image.Image] , UpperCamelCase_ : Union[torch.Tensor, PIL.Image.Image] , UpperCamelCase_ : int = 250 , UpperCamelCase_ : float = 0.0 , UpperCamelCase_ : int = 10 , UpperCamelCase_ : int = 10 , UpperCamelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCamelCase_ : Optional[str] = "pil" , UpperCamelCase_ : bool = True , ):
"""simple docstring"""
__UpperCAmelCase : Dict = image
__UpperCAmelCase : Any = _preprocess_image(UpperCamelCase_)
__UpperCAmelCase : str = original_image.to(device=self.device , dtype=self.unet.dtype)
__UpperCAmelCase : List[str] = _preprocess_mask(UpperCamelCase_)
__UpperCAmelCase : List[Any] = mask_image.to(device=self.device , dtype=self.unet.dtype)
__UpperCAmelCase : Dict = original_image.shape[0]
# sample gaussian noise to begin the loop
if isinstance(UpperCamelCase_ , UpperCamelCase_) and len(UpperCamelCase_) != batch_size:
raise ValueError(
F"You have passed a list of generators of length {len(UpperCamelCase_)}, but requested an effective batch"
F" size of {batch_size}. Make sure the batch size matches the length of the generators.")
__UpperCAmelCase : Tuple = original_image.shape
__UpperCAmelCase : int = randn_tensor(UpperCamelCase_ , generator=UpperCamelCase_ , device=self.device , dtype=self.unet.dtype)
# set step values
self.scheduler.set_timesteps(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , self.device)
__UpperCAmelCase : Any = eta
__UpperCAmelCase : List[Any] = self.scheduler.timesteps[0] + 1
__UpperCAmelCase : Optional[Any] = generator[0] if isinstance(UpperCamelCase_ , UpperCamelCase_) else generator
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
if t < t_last:
# predict the noise residual
__UpperCAmelCase : Optional[Any] = self.unet(UpperCamelCase_ , UpperCamelCase_).sample
# compute previous image: x_t -> x_t-1
__UpperCAmelCase : int = self.scheduler.step(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
__UpperCAmelCase : Union[str, Any] = self.scheduler.undo_step(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Tuple = t
__UpperCAmelCase : str = (image / 2 + 0.5).clamp(0 , 1)
__UpperCAmelCase : Dict = image.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
__UpperCAmelCase : Tuple = self.numpy_to_pil(UpperCamelCase_)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCamelCase_)
| 77 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = ShapEPipeline
lowercase_ = ["prompt"]
lowercase_ = ["prompt"]
lowercase_ = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
lowercase_ = False
@property
def a_ ( self : Optional[int]):
"""simple docstring"""
return 32
@property
def a_ ( self : Any):
"""simple docstring"""
return 32
@property
def a_ ( self : int):
"""simple docstring"""
return self.time_input_dim * 4
@property
def a_ ( self : List[Any]):
"""simple docstring"""
return 8
@property
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def a_ ( self : List[str]):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(UpperCamelCase_)
@property
def a_ ( self : Any):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Union[str, Any] = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
__UpperCAmelCase : Dict = PriorTransformer(**UpperCamelCase_)
return model
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Tuple = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
__UpperCAmelCase : List[Any] = ShapERenderer(**UpperCamelCase_)
return model
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.dummy_prior
__UpperCAmelCase : str = self.dummy_text_encoder
__UpperCAmelCase : int = self.dummy_tokenizer
__UpperCAmelCase : int = self.dummy_renderer
__UpperCAmelCase : Tuple = HeunDiscreteScheduler(
beta_schedule="exp" , num_train_timesteps=1024 , prediction_type="sample" , use_karras_sigmas=UpperCamelCase_ , clip_sample=UpperCamelCase_ , clip_sample_range=1.0 , )
__UpperCAmelCase : str = {
"prior": prior,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"renderer": renderer,
"scheduler": scheduler,
}
return components
def a_ ( self : Optional[int] , UpperCamelCase_ : Dict , UpperCamelCase_ : Any=0):
"""simple docstring"""
if str(UpperCamelCase_).startswith("mps"):
__UpperCAmelCase : List[Any] = torch.manual_seed(UpperCamelCase_)
else:
__UpperCAmelCase : str = torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)
__UpperCAmelCase : List[Any] = {
"prompt": "horse",
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "np",
}
return inputs
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : str = "cpu"
__UpperCAmelCase : Union[str, Any] = self.get_dummy_components()
__UpperCAmelCase : Union[str, Any] = self.pipeline_class(**UpperCamelCase_)
__UpperCAmelCase : Any = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = pipe(**self.get_dummy_inputs(UpperCamelCase_))
__UpperCAmelCase : Union[str, Any] = output.images[0]
__UpperCAmelCase : str = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__UpperCAmelCase : Union[str, Any] = np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def a_ ( self : Tuple):
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2])
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = torch_device == "cpu"
__UpperCAmelCase : Optional[Any] = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=UpperCamelCase_ , relax_max_difference=UpperCamelCase_ , )
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.get_dummy_components()
__UpperCAmelCase : List[str] = self.pipeline_class(**UpperCamelCase_)
__UpperCAmelCase : int = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = 1
__UpperCAmelCase : Any = 2
__UpperCAmelCase : Optional[Any] = self.get_dummy_inputs(UpperCamelCase_)
for key in inputs.keys():
if key in self.batch_params:
__UpperCAmelCase : List[Any] = batch_size * [inputs[key]]
__UpperCAmelCase : List[Any] = pipe(**UpperCamelCase_ , num_images_per_prompt=UpperCamelCase_)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def a_ ( self : List[str]):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Dict = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_np_out.npy")
__UpperCAmelCase : Optional[Any] = ShapEPipeline.from_pretrained("openai/shap-e")
__UpperCAmelCase : Any = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Dict = torch.Generator(device=UpperCamelCase_).manual_seed(0)
__UpperCAmelCase : int = pipe(
"a shark" , generator=UpperCamelCase_ , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type="np" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(UpperCamelCase_ , UpperCamelCase_)
| 77 | 1 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a__ ( nn.Module ):
def __init__( self : Any , UpperCamelCase_ : int = 16 , UpperCamelCase_ : int = 88 , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : int = 1 , UpperCamelCase_ : float = 0.0 , UpperCamelCase_ : int = 32 , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : str = "geglu" , UpperCamelCase_ : Optional[int] = None , ):
"""simple docstring"""
super().__init__()
__UpperCAmelCase : str = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=UpperCamelCase_ , attention_head_dim=UpperCamelCase_ , in_channels=UpperCamelCase_ , num_layers=UpperCamelCase_ , dropout=UpperCamelCase_ , norm_num_groups=UpperCamelCase_ , cross_attention_dim=UpperCamelCase_ , attention_bias=UpperCamelCase_ , sample_size=UpperCamelCase_ , num_vector_embeds=UpperCamelCase_ , activation_fn=UpperCamelCase_ , num_embeds_ada_norm=UpperCamelCase_ , )
for _ in range(2)
])
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
__UpperCAmelCase : Tuple = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
__UpperCAmelCase : Union[str, Any] = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
__UpperCAmelCase : Tuple = [1, 0]
def a_ ( self : Tuple , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Any=None , UpperCamelCase_ : Dict=None , UpperCamelCase_ : Dict=None , UpperCamelCase_ : bool = True , ):
"""simple docstring"""
__UpperCAmelCase : Any = hidden_states
__UpperCAmelCase : str = []
__UpperCAmelCase : int = 0
# attention_mask is not used yet
for i in range(2):
# for each of the two transformers, pass the corresponding condition tokens
__UpperCAmelCase : List[str] = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
__UpperCAmelCase : Optional[Any] = self.transformer_index_for_condition[i]
__UpperCAmelCase : Dict = self.transformers[transformer_index](
UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , timestep=UpperCamelCase_ , cross_attention_kwargs=UpperCamelCase_ , return_dict=UpperCamelCase_ , )[0]
encoded_states.append(encoded_state - input_states)
tokens_start += self.condition_lengths[i]
__UpperCAmelCase : Union[str, Any] = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
__UpperCAmelCase : Optional[Any] = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=UpperCamelCase_)
| 77 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_features", "is_longer"]
def __init__( self : List[str] , UpperCamelCase_ : Dict=64 , UpperCamelCase_ : Tuple=48000 , UpperCamelCase_ : List[Any]=480 , UpperCamelCase_ : List[str]=10 , UpperCamelCase_ : str=1024 , UpperCamelCase_ : List[str]=0.0 , UpperCamelCase_ : Optional[int]=False , UpperCamelCase_ : float = 0 , UpperCamelCase_ : float = 14000 , UpperCamelCase_ : int = None , UpperCamelCase_ : str = "fusion" , UpperCamelCase_ : str = "repeatpad" , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
feature_size=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , padding_value=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Union[str, Any] = top_db
__UpperCAmelCase : Optional[Any] = truncation
__UpperCAmelCase : str = padding
__UpperCAmelCase : int = fft_window_size
__UpperCAmelCase : str = (fft_window_size >> 1) + 1
__UpperCAmelCase : List[Any] = hop_length
__UpperCAmelCase : Optional[Any] = max_length_s
__UpperCAmelCase : Tuple = max_length_s * sampling_rate
__UpperCAmelCase : str = sampling_rate
__UpperCAmelCase : int = frequency_min
__UpperCAmelCase : Optional[Any] = frequency_max
__UpperCAmelCase : Any = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCamelCase_ , min_frequency=UpperCamelCase_ , max_frequency=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , norm=UpperCamelCase_ , mel_scale="htk" , )
__UpperCAmelCase : Any = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCamelCase_ , min_frequency=UpperCamelCase_ , max_frequency=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , norm="slaney" , mel_scale="slaney" , )
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Dict = copy.deepcopy(self.__dict__)
__UpperCAmelCase : str = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def a_ ( self : int , UpperCamelCase_ : np.array , UpperCamelCase_ : Optional[np.array] = None):
"""simple docstring"""
__UpperCAmelCase : List[Any] = spectrogram(
UpperCamelCase_ , window_function(self.fft_window_size , "hann") , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=UpperCamelCase_ , log_mel="dB" , )
return log_mel_spectrogram.T
def a_ ( self : List[Any] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = np.array_split(list(range(0 , total_frames - chunk_frames + 1)) , 3)
if len(ranges[1]) == 0:
# if the audio is too short, we just use the first chunk
__UpperCAmelCase : str = [0]
if len(ranges[2]) == 0:
# if the audio is too short, we just use the first chunk
__UpperCAmelCase : Dict = [0]
# randomly choose index for each part
__UpperCAmelCase : Dict = np.random.choice(ranges[0])
__UpperCAmelCase : List[str] = np.random.choice(ranges[1])
__UpperCAmelCase : List[Any] = np.random.choice(ranges[2])
__UpperCAmelCase : List[Any] = mel[idx_front : idx_front + chunk_frames, :]
__UpperCAmelCase : List[str] = mel[idx_middle : idx_middle + chunk_frames, :]
__UpperCAmelCase : List[str] = mel[idx_back : idx_back + chunk_frames, :]
__UpperCAmelCase : Tuple = torch.tensor(mel[None, None, :])
__UpperCAmelCase : Union[str, Any] = torch.nn.functional.interpolate(
UpperCamelCase_ , size=[chunk_frames, 64] , mode="bilinear" , align_corners=UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = mel_shrink[0][0].numpy()
__UpperCAmelCase : Optional[int] = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0)
return mel_fusion
def a_ ( self : Optional[Any] , UpperCamelCase_ : np.array , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
__UpperCAmelCase : List[str] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
__UpperCAmelCase : List[Any] = len(UpperCamelCase_) - max_length
__UpperCAmelCase : int = np.random.randint(0 , overflow + 1)
__UpperCAmelCase : Union[str, Any] = waveform[idx : idx + max_length]
__UpperCAmelCase : Union[str, Any] = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters_slaney)[None, :]
elif truncation == "fusion":
__UpperCAmelCase : Any = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters)
__UpperCAmelCase : Dict = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
__UpperCAmelCase : Tuple = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
__UpperCAmelCase : List[str] = np.stack([mel, mel, mel, mel] , axis=0)
__UpperCAmelCase : Any = False
else:
__UpperCAmelCase : List[str] = self._random_mel_fusion(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = True
else:
raise NotImplementedError(F"data_truncating {truncation} not implemented")
else:
__UpperCAmelCase : Optional[Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
__UpperCAmelCase : Tuple = int(max_length / len(UpperCamelCase_))
__UpperCAmelCase : List[str] = np.stack(np.tile(UpperCamelCase_ , n_repeat + 1))[:max_length]
if padding == "repeatpad":
__UpperCAmelCase : Union[str, Any] = int(max_length / len(UpperCamelCase_))
__UpperCAmelCase : Optional[Any] = np.stack(np.tile(UpperCamelCase_ , UpperCamelCase_))
__UpperCAmelCase : int = np.pad(UpperCamelCase_ , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0)
if truncation == "fusion":
__UpperCAmelCase : Any = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters)
__UpperCAmelCase : List[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0)
else:
__UpperCAmelCase : Optional[int] = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters_slaney)[None, :]
return input_mel, longer
def __call__( self : Dict , UpperCamelCase_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , UpperCamelCase_ : str = None , UpperCamelCase_ : Optional[str] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[Union[str, TensorType]] = None , **UpperCamelCase_ : Any , ):
"""simple docstring"""
__UpperCAmelCase : int = truncation if truncation is not None else self.truncation
__UpperCAmelCase : Optional[Any] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
F" was sampled with {self.sampling_rate} and not {sampling_rate}.")
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug.")
__UpperCAmelCase : List[str] = isinstance(UpperCamelCase_ , np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(F"Only mono-channel audio is supported for input to {self}")
__UpperCAmelCase : str = is_batched_numpy or (
isinstance(UpperCamelCase_ , (list, tuple)) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)))
)
if is_batched:
__UpperCAmelCase : Dict = [np.asarray(UpperCamelCase_ , dtype=np.floataa) for speech in raw_speech]
elif not is_batched and not isinstance(UpperCamelCase_ , np.ndarray):
__UpperCAmelCase : Tuple = np.asarray(UpperCamelCase_ , dtype=np.floataa)
elif isinstance(UpperCamelCase_ , np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
__UpperCAmelCase : Optional[int] = raw_speech.astype(np.floataa)
# always return batch
if not is_batched:
__UpperCAmelCase : int = [np.asarray(UpperCamelCase_)]
# convert to mel spectrogram, truncate and pad if needed.
__UpperCAmelCase : Optional[int] = [
self._get_input_mel(UpperCamelCase_ , max_length if max_length else self.nb_max_samples , UpperCamelCase_ , UpperCamelCase_)
for waveform in raw_speech
]
__UpperCAmelCase : Tuple = []
__UpperCAmelCase : List[Any] = []
for mel, longer in padded_inputs:
input_mel.append(UpperCamelCase_)
is_longer.append(UpperCamelCase_)
if truncation == "fusion" and sum(UpperCamelCase_) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
__UpperCAmelCase : Any = np.random.randint(0 , len(UpperCamelCase_))
__UpperCAmelCase : Optional[int] = True
if isinstance(input_mel[0] , UpperCamelCase_):
__UpperCAmelCase : Tuple = [np.asarray(UpperCamelCase_ , dtype=np.floataa) for feature in input_mel]
# is_longer is a list of bool
__UpperCAmelCase : List[str] = [[longer] for longer in is_longer]
__UpperCAmelCase : Optional[int] = {"input_features": input_mel, "is_longer": is_longer}
__UpperCAmelCase : Optional[int] = BatchFeature(UpperCamelCase_)
if return_tensors is not None:
__UpperCAmelCase : Any = input_features.convert_to_tensors(UpperCamelCase_)
return input_features
| 77 | 1 |
"""simple docstring"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
A = logging.get_logger(__name__)
A = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
A = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class a__ :
lowercase_ = field(
default=__magic_name__ , metadata={"help": "Model type selected in the list: " + ", ".join(__magic_name__ )} )
lowercase_ = field(
default=__magic_name__ , metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."} )
lowercase_ = field(
default=1_2_8 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
lowercase_ = field(
default=1_2_8 , metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."} , )
lowercase_ = field(
default=6_4 , metadata={
"help": (
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length."
)
} , )
lowercase_ = field(
default=3_0 , metadata={
"help": (
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another."
)
} , )
lowercase_ = field(
default=__magic_name__ , metadata={"help": "Overwrite the cached training and evaluation sets"} )
lowercase_ = field(
default=__magic_name__ , metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."} )
lowercase_ = field(
default=0.0 , metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
lowercase_ = field(
default=2_0 , metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
lowercase_ = field(
default=0 , metadata={
"help": (
"language id of input for language-specific xlm models (see"
" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
)
} , )
lowercase_ = field(default=1 , metadata={"help": "multiple threads for converting example to features"} )
class a__ ( __magic_name__ ):
lowercase_ = "train"
lowercase_ = "dev"
class a__ ( __magic_name__ ):
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
def __init__( self : Tuple , UpperCamelCase_ : SquadDataTrainingArguments , UpperCamelCase_ : PreTrainedTokenizer , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Union[str, Split] = Split.train , UpperCamelCase_ : Optional[bool] = False , UpperCamelCase_ : Optional[str] = None , UpperCamelCase_ : Optional[str] = "pt" , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = args
__UpperCAmelCase : Any = is_language_sensitive
__UpperCAmelCase : Optional[Any] = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(UpperCamelCase_ , UpperCamelCase_):
try:
__UpperCAmelCase : Any = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name")
__UpperCAmelCase : Optional[int] = mode
# Load data features from cache or dataset file
__UpperCAmelCase : int = "v2" if args.version_2_with_negative else "v1"
__UpperCAmelCase : Optional[Any] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}" , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__UpperCAmelCase : Optional[int] = cached_features_file + ".lock"
with FileLock(UpperCamelCase_):
if os.path.exists(UpperCamelCase_) and not args.overwrite_cache:
__UpperCAmelCase : Union[str, Any] = time.time()
__UpperCAmelCase : Union[str, Any] = torch.load(UpperCamelCase_)
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
__UpperCAmelCase : Optional[Any] = self.old_features["features"]
__UpperCAmelCase : int = self.old_features.get("dataset" , UpperCamelCase_)
__UpperCAmelCase : Tuple = self.old_features.get("examples" , UpperCamelCase_)
logger.info(
F"Loading features from cached file {cached_features_file} [took %.3f s]" , time.time() - start)
if self.dataset is None or self.examples is None:
logger.warning(
F"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
" future run")
else:
if mode == Split.dev:
__UpperCAmelCase : Union[str, Any] = self.processor.get_dev_examples(args.data_dir)
else:
__UpperCAmelCase : List[str] = self.processor.get_train_examples(args.data_dir)
__UpperCAmelCase , __UpperCAmelCase : int = squad_convert_examples_to_features(
examples=self.examples , tokenizer=UpperCamelCase_ , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=UpperCamelCase_ , )
__UpperCAmelCase : Union[str, Any] = time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples} , UpperCamelCase_ , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]")
def __len__( self : Union[str, Any]):
"""simple docstring"""
return len(self.features)
def __getitem__( self : int , UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.features[i]
__UpperCAmelCase : List[Any] = torch.tensor(feature.input_ids , dtype=torch.long)
__UpperCAmelCase : Tuple = torch.tensor(feature.attention_mask , dtype=torch.long)
__UpperCAmelCase : int = torch.tensor(feature.token_type_ids , dtype=torch.long)
__UpperCAmelCase : str = torch.tensor(feature.cls_index , dtype=torch.long)
__UpperCAmelCase : Optional[int] = torch.tensor(feature.p_mask , dtype=torch.float)
__UpperCAmelCase : List[Any] = torch.tensor(feature.is_impossible , dtype=torch.float)
__UpperCAmelCase : str = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": cls_index, "p_mask": p_mask})
if self.args.version_2_with_negative:
inputs.update({"is_impossible": is_impossible})
if self.is_language_sensitive:
inputs.update({"langs": (torch.ones(input_ids.shape , dtype=torch.intaa) * self.args.lang_id)})
if self.mode == Split.train:
__UpperCAmelCase : Tuple = torch.tensor(feature.start_position , dtype=torch.long)
__UpperCAmelCase : List[Any] = torch.tensor(feature.end_position , dtype=torch.long)
inputs.update({"start_positions": start_positions, "end_positions": end_positions})
return inputs
| 77 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : Optional[Any] , UpperCamelCase_ : List[Any]="</s>" , UpperCamelCase_ : Tuple="<unk>" , UpperCamelCase_ : List[str]="<pad>" , UpperCamelCase_ : Union[str, Any]=125 , UpperCamelCase_ : Dict=None , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
if extra_ids > 0 and additional_special_tokens is None:
__UpperCAmelCase : int = [F"<extra_id_{i}>" for i in range(UpperCamelCase_)]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__UpperCAmelCase : Dict = len(set(filter(lambda UpperCamelCase_: bool("extra_id" in str(UpperCamelCase_)) , UpperCamelCase_)))
if extra_tokens != extra_ids:
raise ValueError(
F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
" extra_ids tokens")
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else pad_token
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else eos_token
__UpperCAmelCase : Optional[int] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else unk_token
super().__init__(
eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , extra_ids=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : List[str] = extra_ids
__UpperCAmelCase : int = 2**8 # utf is 8 bits
# define special tokens dict
__UpperCAmelCase : Dict[int, str] = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
__UpperCAmelCase : Any = len(self.special_tokens_encoder)
__UpperCAmelCase : List[Any] = len(UpperCamelCase_)
for i, token in enumerate(UpperCamelCase_):
__UpperCAmelCase : Union[str, Any] = self.vocab_size + i - n
__UpperCAmelCase : Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def a_ ( self : List[Any]):
"""simple docstring"""
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def a_ ( self : List[str] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase_ , token_ids_a=UpperCamelCase_ , already_has_special_tokens=UpperCamelCase_)
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(UpperCamelCase_)) + [1]
return ([0] * len(UpperCamelCase_)) + [1] + ([0] * len(UpperCamelCase_)) + [1]
def a_ ( self : Optional[Any] , UpperCamelCase_ : List[int]):
"""simple docstring"""
if len(UpperCamelCase_) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
" eos tokens being added.")
return token_ids
else:
return token_ids + [self.eos_token_id]
def a_ ( self : Dict , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Dict = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos) * [0]
return len(token_ids_a + eos + token_ids_a + eos) * [0]
def a_ ( self : Optional[int] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self._add_eos_if_not_present(UpperCamelCase_)
if token_ids_a is None:
return token_ids_a
else:
__UpperCAmelCase : List[Any] = self._add_eos_if_not_present(UpperCamelCase_)
return token_ids_a + token_ids_a
def a_ ( self : List[str] , UpperCamelCase_ : str):
"""simple docstring"""
__UpperCAmelCase : Any = [chr(UpperCamelCase_) for i in text.encode("utf-8")]
return tokens
def a_ ( self : Tuple , UpperCamelCase_ : List[Any]):
"""simple docstring"""
if token in self.special_tokens_encoder:
__UpperCAmelCase : Any = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
__UpperCAmelCase : int = self.added_tokens_encoder[token]
elif len(UpperCamelCase_) != 1:
__UpperCAmelCase : Optional[Any] = self.unk_token_id
else:
__UpperCAmelCase : Any = ord(UpperCamelCase_) + self._num_special_tokens
return token_id
def a_ ( self : Any , UpperCamelCase_ : List[str]):
"""simple docstring"""
if index in self.special_tokens_decoder:
__UpperCAmelCase : Any = self.special_tokens_decoder[index]
else:
__UpperCAmelCase : List[str] = chr(index - self._num_special_tokens)
return token
def a_ ( self : Dict , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : str = b""
for token in tokens:
if token in self.special_tokens_decoder:
__UpperCAmelCase : Tuple = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.added_tokens_decoder:
__UpperCAmelCase : Any = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.special_tokens_encoder:
__UpperCAmelCase : Optional[int] = token.encode("utf-8")
elif token in self.added_tokens_encoder:
__UpperCAmelCase : Optional[Any] = token.encode("utf-8")
else:
__UpperCAmelCase : Any = bytes([ord(UpperCamelCase_)])
bstring += tok_string
__UpperCAmelCase : List[Any] = bstring.decode("utf-8" , errors="ignore")
return string
def a_ ( self : Optional[int] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
return ()
| 77 | 1 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
A = HfApi()
A = {}
# fmt: off
A = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
A = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
A = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
A = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
A = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
A = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
A = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
A = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
A = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
A = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
A = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
A = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
A = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
A = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
A = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
A = api.list_models(filter="""diffusers""")
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
A = """/home/patrick/google_checkpoints/""" + mod.modelId.split("""/""")[-1]
print(f'''Started running {mod.modelId}!!!''')
if mod.modelId.startswith("""CompVis"""):
A = UNetaDModel.from_pretrained(local_checkpoint, subfolder="""unet""")
else:
A = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
A = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
A = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
A = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results["""_""".join("""_""".join(mod.modelId.split("""/""")).split("""-"""))], atol=1E-3
)
print(f'''{mod.modelId} has passed successfully!!!''')
| 77 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self : List[Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple=3 , UpperCamelCase_ : Optional[int]=32 , UpperCamelCase_ : Dict=3 , UpperCamelCase_ : List[str]=10 , UpperCamelCase_ : str=[10, 20, 30, 40] , UpperCamelCase_ : Tuple=[1, 1, 2, 1] , UpperCamelCase_ : str=True , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Dict="relu" , UpperCamelCase_ : str=3 , UpperCamelCase_ : int=None , ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = parent
__UpperCAmelCase : List[str] = batch_size
__UpperCAmelCase : List[str] = image_size
__UpperCAmelCase : Tuple = num_channels
__UpperCAmelCase : Union[str, Any] = embeddings_size
__UpperCAmelCase : Dict = hidden_sizes
__UpperCAmelCase : Dict = depths
__UpperCAmelCase : Tuple = is_training
__UpperCAmelCase : List[Any] = use_labels
__UpperCAmelCase : Optional[int] = hidden_act
__UpperCAmelCase : str = num_labels
__UpperCAmelCase : Optional[int] = scope
__UpperCAmelCase : Dict = len(UpperCamelCase_)
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__UpperCAmelCase : Dict = self.get_config()
return config, pixel_values
def a_ ( self : Dict):
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def a_ ( self : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = FlaxRegNetModel(config=UpperCamelCase_)
__UpperCAmelCase : Dict = model(UpperCamelCase_)
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def a_ ( self : Any , UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.num_labels
__UpperCAmelCase : Tuple = FlaxRegNetForImageClassification(config=UpperCamelCase_)
__UpperCAmelCase : str = model(UpperCamelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Any = self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase : Tuple = config_and_inputs
__UpperCAmelCase : Optional[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
lowercase_ = False
lowercase_ = False
lowercase_ = False
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = FlaxRegNetModelTester(self)
__UpperCAmelCase : str = ConfigTester(self , config_class=UpperCamelCase_ , has_text_modality=UpperCamelCase_)
def a_ ( self : Dict):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def a_ ( self : Tuple):
"""simple docstring"""
return
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_)
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_)
@unittest.skip(reason="RegNet does not use inputs_embeds")
def a_ ( self : Union[str, Any]):
"""simple docstring"""
pass
@unittest.skip(reason="RegNet does not support input and output embeddings")
def a_ ( self : Optional[int]):
"""simple docstring"""
pass
def a_ ( self : str):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : int = model_class(UpperCamelCase_)
__UpperCAmelCase : Optional[int] = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase : Any = [*signature.parameters.keys()]
__UpperCAmelCase : Dict = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCamelCase_)
def a_ ( self : int):
"""simple docstring"""
def check_hidden_states_output(UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Union[str, Any]):
__UpperCAmelCase : Union[str, Any] = model_class(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_))
__UpperCAmelCase : List[str] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__UpperCAmelCase : str = self.model_tester.num_stages
self.assertEqual(len(UpperCamelCase_) , expected_num_stages + 1)
__UpperCAmelCase , __UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : List[str] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase : Optional[int] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
def a_ ( self : Tuple):
"""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 : List[Any] = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Optional[int] = model_class(UpperCamelCase_)
@jax.jit
def model_jitted(UpperCamelCase_ : int , **UpperCamelCase_ : Optional[int]):
return model(pixel_values=UpperCamelCase_ , **UpperCamelCase_)
with self.subTest("JIT Enabled"):
__UpperCAmelCase : Optional[Any] = model_jitted(**UpperCamelCase_).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
__UpperCAmelCase : Dict = model_jitted(**UpperCamelCase_).to_tuple()
self.assertEqual(len(UpperCamelCase_) , len(UpperCamelCase_))
for jitted_output, output in zip(UpperCamelCase_ , UpperCamelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCamelCase ( ) -> Any:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_flax
class a__ ( unittest.TestCase ):
@cached_property
def a_ ( self : Optional[int]):
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/regnet-y-040") if is_vision_available() else None
@slow
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Any = FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")
__UpperCAmelCase : Dict = self.default_image_processor
__UpperCAmelCase : str = prepare_img()
__UpperCAmelCase : int = image_processor(images=UpperCamelCase_ , return_tensors="np")
__UpperCAmelCase : Dict = model(**UpperCamelCase_)
# verify the logits
__UpperCAmelCase : Dict = (1, 1000)
self.assertEqual(outputs.logits.shape , UpperCamelCase_)
__UpperCAmelCase : Any = jnp.array([-0.4180, -1.5051, -3.4836])
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , UpperCamelCase_ , atol=1e-4))
| 77 | 1 |
"""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__ ( __magic_name__ , __magic_name__ ):
lowercase_ = "maskformer-swin"
lowercase_ = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : List[str] , UpperCamelCase_ : Dict=224 , UpperCamelCase_ : Any=4 , UpperCamelCase_ : Tuple=3 , UpperCamelCase_ : Any=96 , UpperCamelCase_ : Dict=[2, 2, 6, 2] , UpperCamelCase_ : Tuple=[3, 6, 12, 24] , UpperCamelCase_ : int=7 , UpperCamelCase_ : List[str]=4.0 , UpperCamelCase_ : int=True , UpperCamelCase_ : int=0.0 , UpperCamelCase_ : List[Any]=0.0 , UpperCamelCase_ : str=0.1 , UpperCamelCase_ : Tuple="gelu" , UpperCamelCase_ : List[str]=False , UpperCamelCase_ : Optional[Any]=0.02 , UpperCamelCase_ : Tuple=1e-5 , UpperCamelCase_ : int=None , UpperCamelCase_ : Union[str, Any]=None , **UpperCamelCase_ : Dict , ):
"""simple docstring"""
super().__init__(**UpperCamelCase_)
__UpperCAmelCase : int = image_size
__UpperCAmelCase : Optional[int] = patch_size
__UpperCAmelCase : int = num_channels
__UpperCAmelCase : Dict = embed_dim
__UpperCAmelCase : List[Any] = depths
__UpperCAmelCase : Tuple = len(UpperCamelCase_)
__UpperCAmelCase : List[Any] = num_heads
__UpperCAmelCase : List[str] = window_size
__UpperCAmelCase : Optional[int] = mlp_ratio
__UpperCAmelCase : int = qkv_bias
__UpperCAmelCase : Union[str, Any] = hidden_dropout_prob
__UpperCAmelCase : Optional[Any] = attention_probs_dropout_prob
__UpperCAmelCase : Any = drop_path_rate
__UpperCAmelCase : Tuple = hidden_act
__UpperCAmelCase : Optional[int] = use_absolute_embeddings
__UpperCAmelCase : Optional[Any] = layer_norm_eps
__UpperCAmelCase : List[str] = 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 : Union[str, Any] = int(embed_dim * 2 ** (len(UpperCamelCase_) - 1))
__UpperCAmelCase : List[str] = ["stem"] + [F"stage{idx}" for idx in range(1 , len(UpperCamelCase_) + 1)]
__UpperCAmelCase , __UpperCAmelCase : List[Any] = get_aligned_output_features_output_indices(
out_features=UpperCamelCase_ , out_indices=UpperCamelCase_ , stage_names=self.stage_names)
| 77 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
A = """
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results['spearmanr'])
-0.7
>>> print(round(results['spearmanr_pvalue'], 2))
0.19
"""
A = r"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
def a_ ( self : Any):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("float"),
"references": datasets.Value("float"),
}) , reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"] , )
def a_ ( self : Union[str, Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : int=False):
"""simple docstring"""
__UpperCAmelCase : List[str] = spearmanr(UpperCamelCase_ , UpperCamelCase_)
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 77 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
A = None
A = logging.get_logger(__name__)
A = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
A = {
"""vocab_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
),
},
"""tokenizer_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
),
},
}
A = {
"""facebook/nllb-large-en-ro""": 1_024,
"""facebook/nllb-200-distilled-600M""": 1_024,
}
# fmt: off
A = ["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = ["input_ids", "attention_mask"]
lowercase_ = NllbTokenizer
lowercase_ = []
lowercase_ = []
def __init__( self : str , UpperCamelCase_ : Any=None , UpperCamelCase_ : Dict=None , UpperCamelCase_ : List[str]="<s>" , UpperCamelCase_ : int="</s>" , UpperCamelCase_ : Union[str, Any]="</s>" , UpperCamelCase_ : List[Any]="<s>" , UpperCamelCase_ : Any="<unk>" , UpperCamelCase_ : int="<pad>" , UpperCamelCase_ : int="<mask>" , UpperCamelCase_ : Tuple=None , UpperCamelCase_ : List[str]=None , UpperCamelCase_ : Dict=None , UpperCamelCase_ : Tuple=False , **UpperCamelCase_ : List[Any] , ):
"""simple docstring"""
__UpperCAmelCase : int = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else mask_token
__UpperCAmelCase : Tuple = legacy_behaviour
super().__init__(
vocab_file=UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , cls_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , src_lang=UpperCamelCase_ , tgt_lang=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , legacy_behaviour=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : List[str] = vocab_file
__UpperCAmelCase : Union[str, Any] = False if not self.vocab_file else True
__UpperCAmelCase : Union[str, Any] = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens])
self.add_special_tokens({"additional_special_tokens": _additional_special_tokens})
__UpperCAmelCase : Any = {
lang_code: self.convert_tokens_to_ids(UpperCamelCase_) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
__UpperCAmelCase : Optional[Any] = src_lang if src_lang is not None else "eng_Latn"
__UpperCAmelCase : List[str] = self.convert_tokens_to_ids(self._src_lang)
__UpperCAmelCase : Tuple = tgt_lang
self.set_src_lang_special_tokens(self._src_lang)
@property
def a_ ( self : str):
"""simple docstring"""
return self._src_lang
@src_lang.setter
def a_ ( self : List[str] , UpperCamelCase_ : str):
"""simple docstring"""
__UpperCAmelCase : Dict = new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def a_ ( self : List[Any] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def a_ ( self : Any , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : List[Any] = [self.sep_token_id]
__UpperCAmelCase : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]
def a_ ( self : Union[str, Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] , UpperCamelCase_ : Optional[str] , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
__UpperCAmelCase : Any = src_lang
__UpperCAmelCase : Optional[Any] = self(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , return_tensors=UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : List[Any] = self.convert_tokens_to_ids(UpperCamelCase_)
__UpperCAmelCase : str = tgt_lang_id
return inputs
def a_ ( self : List[Any] , UpperCamelCase_ : List[str] , UpperCamelCase_ : str = "eng_Latn" , UpperCamelCase_ : Optional[List[str]] = None , UpperCamelCase_ : str = "fra_Latn" , **UpperCamelCase_ : str , ):
"""simple docstring"""
__UpperCAmelCase : List[Any] = src_lang
__UpperCAmelCase : Dict = tgt_lang
return super().prepare_seqaseq_batch(UpperCamelCase_ , UpperCamelCase_ , **UpperCamelCase_)
def a_ ( self : Any):
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang)
def a_ ( self : List[str]):
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang)
def a_ ( self : str , UpperCamelCase_ : Dict):
"""simple docstring"""
__UpperCAmelCase : List[str] = self.convert_tokens_to_ids(UpperCamelCase_)
if self.legacy_behaviour:
__UpperCAmelCase : List[Any] = []
__UpperCAmelCase : Tuple = [self.eos_token_id, self.cur_lang_code]
else:
__UpperCAmelCase : str = [self.cur_lang_code]
__UpperCAmelCase : int = [self.eos_token_id]
__UpperCAmelCase : str = self.convert_ids_to_tokens(self.prefix_tokens)
__UpperCAmelCase : List[str] = self.convert_ids_to_tokens(self.suffix_tokens)
__UpperCAmelCase : int = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens)) , )
def a_ ( self : Union[str, Any] , UpperCamelCase_ : str):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.convert_tokens_to_ids(UpperCamelCase_)
if self.legacy_behaviour:
__UpperCAmelCase : str = []
__UpperCAmelCase : Union[str, Any] = [self.eos_token_id, self.cur_lang_code]
else:
__UpperCAmelCase : List[str] = [self.cur_lang_code]
__UpperCAmelCase : Any = [self.eos_token_id]
__UpperCAmelCase : List[Any] = self.convert_ids_to_tokens(self.prefix_tokens)
__UpperCAmelCase : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens)
__UpperCAmelCase : Dict = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens)) , )
def a_ ( self : Optional[Any] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer.")
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory.")
return
__UpperCAmelCase : Union[str, Any] = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_):
copyfile(self.vocab_file , UpperCamelCase_)
return (out_vocab_file,)
| 77 |
"""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""": """spiece.model"""}
A = {
"""vocab_file""": {
"""bert_for_seq_generation""": (
"""https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
),
}
}
A = {"""bert_for_seq_generation""": 512}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = []
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : List[str] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : List[str]="<s>" , UpperCamelCase_ : Optional[Any]="</s>" , UpperCamelCase_ : Optional[int]="<unk>" , UpperCamelCase_ : int="<pad>" , UpperCamelCase_ : List[Any]="<::::>" , UpperCamelCase_ : Optional[Dict[str, Any]] = None , **UpperCamelCase_ : List[Any] , ):
"""simple docstring"""
__UpperCAmelCase : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase_ , )
__UpperCAmelCase : Dict = vocab_file
__UpperCAmelCase : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(UpperCamelCase_)
@property
def a_ ( self : List[str]):
"""simple docstring"""
return self.sp_model.get_piece_size()
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : int = {self.convert_ids_to_tokens(UpperCamelCase_): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self : int):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = self.__dict__.copy()
__UpperCAmelCase : List[Any] = None
return state
def __setstate__( self : Optional[Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs"):
__UpperCAmelCase : List[Any] = {}
__UpperCAmelCase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def a_ ( self : Any , UpperCamelCase_ : str):
"""simple docstring"""
return self.sp_model.encode(UpperCamelCase_ , out_type=UpperCamelCase_)
def a_ ( self : Optional[Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
return self.sp_model.piece_to_id(UpperCamelCase_)
def a_ ( self : Tuple , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : int = self.sp_model.IdToPiece(UpperCamelCase_)
return token
def a_ ( self : Dict , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : int = []
__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(UpperCamelCase_) + token
__UpperCAmelCase : List[Any] = []
else:
current_sub_tokens.append(UpperCamelCase_)
out_string += self.sp_model.decode(UpperCamelCase_)
return out_string.strip()
def a_ ( self : List[str] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : Tuple = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , UpperCamelCase_)
elif not os.path.isfile(self.vocab_file):
with open(UpperCamelCase_ , "wb") as fi:
__UpperCAmelCase : List[str] = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase_)
return (out_vocab_file,)
| 77 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
A = """Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""
def _UpperCamelCase ( ) -> int:
"""simple docstring"""
__UpperCAmelCase : int = _ask_options(
"In which compute environment are you running?" , ["This machine", "AWS (Amazon SageMaker)"] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
__UpperCAmelCase : str = get_sagemaker_input()
else:
__UpperCAmelCase : List[Any] = get_cluster_input()
return config
def _UpperCamelCase ( UpperCamelCase=None ) -> Tuple:
"""simple docstring"""
if subparsers is not None:
__UpperCAmelCase : Tuple = subparsers.add_parser("config" , description=UpperCamelCase )
else:
__UpperCAmelCase : int = argparse.ArgumentParser("Accelerate config command" , description=UpperCamelCase )
parser.add_argument(
"--config_file" , default=UpperCamelCase , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=UpperCamelCase )
return parser
def _UpperCamelCase ( UpperCamelCase ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : int = get_user_input()
if args.config_file is not None:
__UpperCAmelCase : Tuple = args.config_file
else:
if not os.path.isdir(UpperCamelCase ):
os.makedirs(UpperCamelCase )
__UpperCAmelCase : Optional[int] = default_yaml_config_file
if config_file.endswith(".json" ):
config.to_json_file(UpperCamelCase )
else:
config.to_yaml_file(UpperCamelCase )
print(f"accelerate configuration saved at {config_file}" )
def _UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = config_command_parser()
__UpperCAmelCase : Union[str, Any] = parser.parse_args()
config_command(UpperCamelCase )
if __name__ == "__main__":
main()
| 77 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
A = """true"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=82 , UpperCamelCase=16 ) -> Tuple:
"""simple docstring"""
set_seed(42 )
__UpperCAmelCase : Dict = RegressionModel()
__UpperCAmelCase : Optional[Any] = deepcopy(UpperCamelCase )
__UpperCAmelCase : Any = RegressionDataset(length=UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = DataLoader(UpperCamelCase , batch_size=UpperCamelCase )
model.to(accelerator.device )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = accelerator.prepare(UpperCamelCase , UpperCamelCase )
return model, ddp_model, dataloader
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=False ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : List[str] = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
__UpperCAmelCase : Dict = load_dataset("glue" , "mrpc" , split="validation" )
def tokenize_function(UpperCamelCase ):
__UpperCAmelCase : Dict = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=UpperCamelCase , max_length=UpperCamelCase )
return outputs
with accelerator.main_process_first():
__UpperCAmelCase : str = dataset.map(
UpperCamelCase , batched=UpperCamelCase , remove_columns=["idx", "sentence1", "sentence2"] , )
__UpperCAmelCase : List[Any] = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(UpperCamelCase ):
if use_longest:
return tokenizer.pad(UpperCamelCase , padding="longest" , return_tensors="pt" )
return tokenizer.pad(UpperCamelCase , padding="max_length" , max_length=128 , return_tensors="pt" )
return DataLoader(UpperCamelCase , shuffle=UpperCamelCase , collate_fn=UpperCamelCase , batch_size=16 )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : List[Any] = Accelerator(dispatch_batches=UpperCamelCase , split_batches=UpperCamelCase )
__UpperCAmelCase : int = get_dataloader(UpperCamelCase , not dispatch_batches )
__UpperCAmelCase : Any = AutoModelForSequenceClassification.from_pretrained(
"hf-internal-testing/mrpc-bert-base-cased" , return_dict=UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Dict = accelerator.prepare(UpperCamelCase , UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase : Dict = []
for batch in dataloader:
__UpperCAmelCase , __UpperCAmelCase : int = batch.values()
with torch.no_grad():
__UpperCAmelCase : int = model(UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : List[str] = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = [], []
for logit, targ in logits_and_targets:
logits.append(UpperCamelCase )
targs.append(UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = torch.cat(UpperCamelCase ), torch.cat(UpperCamelCase )
return logits, targs
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=82 , UpperCamelCase=False , UpperCamelCase=False , UpperCamelCase=16 ) -> int:
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = get_basic_setup(UpperCamelCase , UpperCamelCase , UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = generate_predictions(UpperCamelCase , UpperCamelCase , UpperCamelCase )
assert (
len(UpperCamelCase ) == num_samples
), f"Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(UpperCamelCase )}"
def _UpperCamelCase ( UpperCamelCase = False , UpperCamelCase = False ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : List[str] = evaluate.load("glue" , "mrpc" )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = get_mrpc_setup(UpperCamelCase , UpperCamelCase )
# First do baseline
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Tuple = setup["no"]
model.to(UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(UpperCamelCase )
with torch.inference_mode():
__UpperCAmelCase : List[str] = model(**UpperCamelCase )
__UpperCAmelCase : str = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=UpperCamelCase , references=batch["labels"] )
__UpperCAmelCase : str = metric.compute()
# Then do distributed
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
__UpperCAmelCase : Any = model(**UpperCamelCase )
__UpperCAmelCase : str = outputs.logits.argmax(dim=-1 )
__UpperCAmelCase : Union[str, Any] = batch["labels"]
__UpperCAmelCase , __UpperCAmelCase : Any = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=UpperCamelCase , references=UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"
def _UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
__UpperCAmelCase : Dict = Accelerator(split_batches=UpperCamelCase , dispatch_batches=UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("**Testing gather_for_metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`" )
test_mrpc(UpperCamelCase , UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test torch metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__UpperCAmelCase : Union[str, Any] = Accelerator(split_batches=UpperCamelCase , dispatch_batches=UpperCamelCase )
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99" )
test_torch_metrics(UpperCamelCase , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test last batch is not dropped when perfectly divisible**" )
__UpperCAmelCase : Any = Accelerator()
test_torch_metrics(UpperCamelCase , 512 )
accelerator.state._reset_state()
def _UpperCamelCase ( UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 77 | 1 |
"""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__)
| 77 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in range(UpperCamelCase , UpperCamelCase ):
__UpperCAmelCase : List[Any] = i
__UpperCAmelCase : Any = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
__UpperCAmelCase : Dict = array[temp_index - 1]
temp_index -= 1
__UpperCAmelCase : str = temp_index_value
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> None: # Max Heap
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = index
__UpperCAmelCase : List[str] = 2 * index + 1 # Left Node
__UpperCAmelCase : Union[str, Any] = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
__UpperCAmelCase : Tuple = left_index
if right_index < heap_size and array[largest] < array[right_index]:
__UpperCAmelCase : int = right_index
if largest != index:
__UpperCAmelCase , __UpperCAmelCase : List[str] = array[largest], array[index]
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
__UpperCAmelCase : List[Any] = len(UpperCamelCase )
for i in range(n // 2 , -1 , -1 ):
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
for i in range(n - 1 , 0 , -1 ):
__UpperCAmelCase , __UpperCAmelCase : int = array[0], array[i]
heapify(UpperCamelCase , 0 , UpperCamelCase )
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = low
__UpperCAmelCase : List[str] = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = array[j], array[i]
i += 1
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
if len(UpperCamelCase ) == 0:
return array
__UpperCAmelCase : Optional[int] = 2 * math.ceil(math.loga(len(UpperCamelCase ) ) )
__UpperCAmelCase : List[Any] = 16
return intro_sort(UpperCamelCase , 0 , len(UpperCamelCase ) , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> list:
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(UpperCamelCase )
max_depth -= 1
__UpperCAmelCase : List[Any] = median_of_a(UpperCamelCase , UpperCamelCase , start + ((end - start) // 2) + 1 , end - 1 )
__UpperCAmelCase : Union[str, Any] = partition(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
intro_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Optional[Any] = p
return insertion_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
A = input("""Enter numbers separated by a comma : """).strip()
A = [float(item) for item in user_input.split(""",""")]
print(sort(unsorted))
| 77 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
A = """"""
A = """"""
A = """"""
A = 1 # (0 is vertical, 1 is horizontal)
def _UpperCamelCase ( ) -> None:
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = get_dataset(UpperCamelCase , UpperCamelCase )
print("Processing..." )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : List[Any] = update_image_and_anno(UpperCamelCase , UpperCamelCase , UpperCamelCase )
for index, image in enumerate(UpperCamelCase ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
__UpperCAmelCase : List[Any] = random_chars(32 )
__UpperCAmelCase : List[str] = paths[index].split(os.sep )[-1].rsplit("." , 1 )[0]
__UpperCAmelCase : str = f"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
cva.imwrite(f"/{file_root}.jpg" , UpperCamelCase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f"Success {index+1}/{len(UpperCamelCase )} with {file_name}" )
__UpperCAmelCase : Dict = []
for anno in new_annos[index]:
__UpperCAmelCase : Any = f"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
annos_list.append(UpperCamelCase )
with open(f"/{file_root}.txt" , "w" ) as outfile:
outfile.write("\n".join(line for line in annos_list ) )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> tuple[list, list]:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = []
__UpperCAmelCase : Optional[int] = []
for label_file in glob.glob(os.path.join(UpperCamelCase , "*.txt" ) ):
__UpperCAmelCase : str = label_file.split(os.sep )[-1].rsplit("." , 1 )[0]
with open(UpperCamelCase ) as in_file:
__UpperCAmelCase : int = in_file.readlines()
__UpperCAmelCase : Dict = os.path.join(UpperCamelCase , f"{label_name}.jpg" )
__UpperCAmelCase : int = []
for obj_list in obj_lists:
__UpperCAmelCase : Union[str, Any] = obj_list.rstrip("\n" ).split(" " )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(UpperCamelCase )
labels.append(UpperCamelCase )
return img_paths, labels
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase = 1 ) -> tuple[list, list, list]:
"""simple docstring"""
__UpperCAmelCase : Dict = []
__UpperCAmelCase : Tuple = []
__UpperCAmelCase : Optional[Any] = []
for idx in range(len(UpperCamelCase ) ):
__UpperCAmelCase : Tuple = []
__UpperCAmelCase : Optional[Any] = img_list[idx]
path_list.append(UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = anno_list[idx]
__UpperCAmelCase : List[str] = cva.imread(UpperCamelCase )
if flip_type == 1:
__UpperCAmelCase : List[str] = cva.flip(UpperCamelCase , UpperCamelCase )
for bbox in img_annos:
__UpperCAmelCase : int = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
__UpperCAmelCase : List[str] = cva.flip(UpperCamelCase , UpperCamelCase )
for bbox in img_annos:
__UpperCAmelCase : Optional[int] = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(UpperCamelCase )
new_imgs_list.append(UpperCamelCase )
return new_imgs_list, new_annos_lists, path_list
def _UpperCamelCase ( UpperCamelCase = 32 ) -> str:
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
__UpperCAmelCase : List[str] = ascii_lowercase + digits
return "".join(random.choice(UpperCamelCase ) for _ in range(UpperCamelCase ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 77 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
__UpperCAmelCase : Any = 0
__UpperCAmelCase : Dict = 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Tuple = 0
# compute the shape of the output matrix
__UpperCAmelCase : Optional[int] = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
__UpperCAmelCase : List[str] = np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
__UpperCAmelCase : str = np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
__UpperCAmelCase : int = 0
__UpperCAmelCase : int = 0
return updated_arr
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : List[str] = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Any = 0
# compute the shape of the output matrix
__UpperCAmelCase : Tuple = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
__UpperCAmelCase : str = np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
__UpperCAmelCase : Tuple = int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Optional[Any] = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name="""avgpooling""", verbose=True)
# Loading the image
A = Image.open("""path_to_image""")
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 77 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
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__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = MBartTokenizer
lowercase_ = MBartTokenizerFast
lowercase_ = True
lowercase_ = True
def a_ ( self : str):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__UpperCAmelCase : Any = MBartTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_)
tokenizer.save_pretrained(self.tmpdirname)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Dict = MBartTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = tokenizer.tokenize("This is a test")
self.assertListEqual(UpperCamelCase_ , ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCamelCase_) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__UpperCAmelCase : List[Any] = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
UpperCamelCase_ , [
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 : Any = tokenizer.convert_tokens_to_ids(UpperCamelCase_)
self.assertListEqual(
UpperCamelCase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(UpperCamelCase_)
self.assertListEqual(
UpperCamelCase_ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def a_ ( self : Dict):
"""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 : Dict = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
__UpperCAmelCase : List[str] = self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : int = self.tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : int = tempfile.mkdtemp()
__UpperCAmelCase : Optional[int] = tokenizer_r.save_pretrained(UpperCamelCase_)
__UpperCAmelCase : Any = tokenizer_p.save_pretrained(UpperCamelCase_)
# 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(UpperCamelCase_ , UpperCamelCase_)
# Checks everything loads correctly in the same way
__UpperCAmelCase : int = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : Tuple = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCamelCase_)
# Save tokenizer rust, legacy_format=True
__UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
__UpperCAmelCase : Dict = tokenizer_r.save_pretrained(UpperCamelCase_ , legacy_format=UpperCamelCase_)
__UpperCAmelCase : int = tokenizer_p.save_pretrained(UpperCamelCase_)
# Checks it save with the same files
self.assertSequenceEqual(UpperCamelCase_ , UpperCamelCase_)
# Checks everything loads correctly in the same way
__UpperCAmelCase : int = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
shutil.rmtree(UpperCamelCase_)
# Save tokenizer rust, legacy_format=False
__UpperCAmelCase : Tuple = tempfile.mkdtemp()
__UpperCAmelCase : int = tokenizer_r.save_pretrained(UpperCamelCase_ , legacy_format=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = tokenizer_p.save_pretrained(UpperCamelCase_)
# 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 : Optional[Any] = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : str = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
shutil.rmtree(UpperCamelCase_)
@require_torch
@require_sentencepiece
@require_tokenizers
class a__ ( unittest.TestCase ):
lowercase_ = "facebook/mbart-large-en-ro"
lowercase_ = [
" 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.",
]
lowercase_ = [
"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.",
]
lowercase_ = [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]
@classmethod
def a_ ( cls : int):
"""simple docstring"""
__UpperCAmelCase : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en_XX" , tgt_lang="ro_RO")
__UpperCAmelCase : Union[str, Any] = 1
return cls
def a_ ( self : List[Any]):
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] , 250001)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] , 250004)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] , 250020)
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase_)
def a_ ( self : Optional[int]):
"""simple docstring"""
self.assertIn(UpperCamelCase_ , self.tokenizer.all_special_ids)
__UpperCAmelCase : Union[str, Any] = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
__UpperCAmelCase : Optional[Any] = self.tokenizer.decode(UpperCamelCase_ , skip_special_tokens=UpperCamelCase_)
__UpperCAmelCase : int = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , UpperCamelCase_)
self.assertNotIn(self.tokenizer.eos_token , UpperCamelCase_)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , UpperCamelCase_)
__UpperCAmelCase : Tuple = 10
__UpperCAmelCase : List[Any] = self.tokenizer(UpperCamelCase_ , max_length=UpperCamelCase_ , truncation=UpperCamelCase_).input_ids[0]
self.assertEqual(ids[-2] , 2)
self.assertEqual(ids[-1] , UpperCamelCase_)
self.assertEqual(len(UpperCamelCase_) , UpperCamelCase_)
def a_ ( self : Any):
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]) , [250026, 250001])
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = tempfile.mkdtemp()
__UpperCAmelCase : Union[str, Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCamelCase_)
__UpperCAmelCase : List[Any] = MBartTokenizer.from_pretrained(UpperCamelCase_)
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , UpperCamelCase_)
@require_torch
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCamelCase_ , 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][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=len(self.expected_src_tokens) , return_tensors="pt" , )
__UpperCAmelCase : Tuple = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id)
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_)
self.assertEqual((2, 14) , batch.input_ids.shape)
self.assertEqual((2, 14) , batch.attention_mask.shape)
__UpperCAmelCase : List[str] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase_)
self.assertEqual(2 , batch.decoder_input_ids[0, -1]) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [])
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE])
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : List[str] = self.tokenizer(self.src_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=3 , return_tensors="pt")
__UpperCAmelCase : Any = self.tokenizer(
text_target=self.tgt_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=10 , return_tensors="pt")
__UpperCAmelCase : int = targets["input_ids"]
__UpperCAmelCase : Any = shift_tokens_right(UpperCamelCase_ , self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1] , 3)
self.assertEqual(batch.decoder_input_ids.shape[1] , 10)
@require_torch
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : int = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="ar_AR")
self.assertEqual(
nested_simplify(UpperCamelCase_) , {
# A, test, EOS, en_XX
"input_ids": [[62, 3034, 2, 250004]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 250001,
} , )
| 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A = None
A = logging.get_logger(__name__)
A = """▁"""
A = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
A = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
A = {
"""google/pegasus-xsum""": 512,
}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = PegasusTokenizer
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : str , UpperCamelCase_ : str=None , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : int="<pad>" , UpperCamelCase_ : Optional[Any]="</s>" , UpperCamelCase_ : Any="<unk>" , UpperCamelCase_ : Tuple="<mask_2>" , UpperCamelCase_ : Any="<mask_1>" , UpperCamelCase_ : Tuple=None , UpperCamelCase_ : str=103 , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = offset
if additional_special_tokens is not None:
if not isinstance(UpperCamelCase_ , UpperCamelCase_):
raise TypeError(
F"additional_special_tokens should be of type {type(UpperCamelCase_)}, but is"
F" {type(UpperCamelCase_)}")
__UpperCAmelCase : Any = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F"<unk_{i}>" for i in range(len(UpperCamelCase_) , self.offset - 1)
]
if len(set(UpperCamelCase_)) != len(UpperCamelCase_):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.")
__UpperCAmelCase : str = additional_special_tokens_extended
else:
__UpperCAmelCase : Tuple = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F"<unk_{i}>" for i in range(2 , self.offset)]
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , pad_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , mask_token_sent=UpperCamelCase_ , offset=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Optional[int] = vocab_file
__UpperCAmelCase : List[str] = False if not self.vocab_file else True
def a_ ( self : Union[str, Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : int = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
F" {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}")
return [1 if x in all_special_ids else 0 for x in seq]
def a_ ( self : Union[str, Any] , UpperCamelCase_ : List , UpperCamelCase_ : Optional[List] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(UpperCamelCase_)
elif token_ids_a is None:
return self._special_token_mask(UpperCamelCase_) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a) + [1]
def a_ ( self : int , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[Any]=None):
"""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 a_ ( self : Union[str, Any] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer.")
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : List[str] = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_):
copyfile(self.vocab_file , UpperCamelCase_)
return (out_vocab_file,)
| 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
return number | (1 << position)
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
return number & ~(1 << position)
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
return number ^ (1 << position)
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> bool:
"""simple docstring"""
return ((number >> position) & 1) == 1
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 77 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[str]:
"""simple docstring"""
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
__UpperCAmelCase : Optional[Any] = TapasConfig.from_json_file(UpperCamelCase )
# set absolute/relative position embeddings parameter
__UpperCAmelCase : Optional[Any] = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
__UpperCAmelCase : List[str] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "WTQ":
# run_task_main.py hparams
__UpperCAmelCase : Tuple = 4
__UpperCAmelCase : Any = True
# hparam_utils.py hparams
__UpperCAmelCase : Union[str, Any] = 0.664694
__UpperCAmelCase : Union[str, Any] = 0.207951
__UpperCAmelCase : int = 0.121194
__UpperCAmelCase : Optional[int] = True
__UpperCAmelCase : List[str] = True
__UpperCAmelCase : Union[str, Any] = False
__UpperCAmelCase : List[str] = 0.0352513
__UpperCAmelCase : Optional[int] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
__UpperCAmelCase : int = 4
__UpperCAmelCase : Optional[int] = False
# hparam_utils.py hparams
__UpperCAmelCase : int = 36.4519
__UpperCAmelCase : str = 0.903421
__UpperCAmelCase : Dict = 222.088
__UpperCAmelCase : Dict = True
__UpperCAmelCase : Union[str, Any] = True
__UpperCAmelCase : Tuple = True
__UpperCAmelCase : Any = 0.763141
__UpperCAmelCase : Optional[Any] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "TABFACT":
__UpperCAmelCase : Union[str, Any] = TapasForSequenceClassification(config=UpperCamelCase )
elif task == "MLM":
__UpperCAmelCase : Tuple = TapasForMaskedLM(config=UpperCamelCase )
elif task == "INTERMEDIATE_PRETRAINING":
__UpperCAmelCase : List[str] = TapasModel(config=UpperCamelCase )
else:
raise ValueError(f"Task {task} not supported." )
print(f"Building PyTorch model from configuration: {config}" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(UpperCamelCase , UpperCamelCase , UpperCamelCase )
# Save pytorch-model (weights and configuration)
print(f"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(UpperCamelCase )
# Save tokenizer files
print(f"Save tokenizer files to {pytorch_dump_path}" )
__UpperCAmelCase : str = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt" , model_max_length=512 )
tokenizer.save_pretrained(UpperCamelCase )
print("Used relative position embeddings:" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
)
parser.add_argument(
"""--reset_position_index_per_cell""",
default=False,
action="""store_true""",
help="""Whether to use relative position embeddings or not. Defaults to True.""",
)
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--tapas_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained TAPAS model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 77 | 1 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
A = logging.get_logger(__name__)
A = """https://openaipublic.azureedge.net/jukebox/models/"""
A = {
"""jukebox-1b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""1b_lyrics/prior_level_2.pth.tar""",
],
"""jukebox-5b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""5b_lyrics/prior_level_2.pth.tar""",
],
}
def _UpperCamelCase ( UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
if key.endswith(".model.1.bias" ) and len(key.split("." ) ) > 10:
__UpperCAmelCase : str = key.replace(".model.1.bias" , ".conv1d_1.bias" )
elif key.endswith(".model.1.weight" ) and len(key.split("." ) ) > 10:
__UpperCAmelCase : Tuple = key.replace(".model.1.weight" , ".conv1d_1.weight" )
elif key.endswith(".model.3.bias" ) and len(key.split("." ) ) > 10:
__UpperCAmelCase : List[Any] = key.replace(".model.3.bias" , ".conv1d_2.bias" )
elif key.endswith(".model.3.weight" ) and len(key.split("." ) ) > 10:
__UpperCAmelCase : List[Any] = key.replace(".model.3.weight" , ".conv1d_2.weight" )
if "conditioner_blocks.0." in key:
__UpperCAmelCase : Any = key.replace("conditioner_blocks.0" , "conditioner_blocks" )
if "prime_prior" in key:
__UpperCAmelCase : Dict = key.replace("prime_prior" , "encoder" )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
__UpperCAmelCase : Dict = key.replace(".emb." , "." )
if key.endswith("k" ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(".k" , ".codebook" )
if "y_emb." in key:
return key.replace("y_emb." , "metadata_embedding." )
if "x_emb.emb." in key:
__UpperCAmelCase : str = key.replace("0.x_emb.emb" , "embed_tokens" )
if "prime_state_ln" in key:
return key.replace("prime_state_ln" , "encoder.final_layer_norm" )
if ".ln" in key:
return key.replace(".ln" , ".layer_norm" )
if "_ln" in key:
return key.replace("_ln" , "_layer_norm" )
if "prime_state_proj" in key:
return key.replace("prime_state_proj" , "encoder.proj_in" )
if "prime_x_out" in key:
return key.replace("prime_x_out" , "encoder.lm_head" )
if "prior.x_out" in key:
return key.replace("x_out" , "fc_proj_out" )
if "x_emb" in key:
return key.replace("x_emb" , "embed_tokens" )
return key
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = {}
import re
__UpperCAmelCase : Tuple = re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
__UpperCAmelCase : Union[str, Any] = re.compile(
R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
__UpperCAmelCase : Optional[Any] = re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
__UpperCAmelCase : int = re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
__UpperCAmelCase : Optional[int] = re.compile(
R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
__UpperCAmelCase : Union[str, Any] = re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
__UpperCAmelCase : Any = re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)" )
__UpperCAmelCase : List[Any] = re.compile(
R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
__UpperCAmelCase : Any = re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)" )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(UpperCamelCase ):
__UpperCAmelCase : str = re_encoder_block_conv_in.match(UpperCamelCase )
__UpperCAmelCase : Dict = regex_match.groups()
__UpperCAmelCase : List[str] = int(groups[2] ) * 2 + int(groups[3] )
__UpperCAmelCase : List[str] = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
__UpperCAmelCase : Optional[int] = re_encoder_block_conv_in.sub(UpperCamelCase , UpperCamelCase )
elif re_encoder_block_resnet.fullmatch(UpperCamelCase ):
__UpperCAmelCase : Tuple = re_encoder_block_resnet.match(UpperCamelCase )
__UpperCAmelCase : Tuple = regex_match.groups()
__UpperCAmelCase : List[Any] = int(groups[2] ) * 2 + int(groups[3] )
__UpperCAmelCase : List[str] = {"1": 1, "3": 2}[groups[-2]]
__UpperCAmelCase : Union[str, Any] = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
__UpperCAmelCase : Dict = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
__UpperCAmelCase : Optional[int] = prefix + resnet_block
__UpperCAmelCase : Union[str, Any] = re_encoder_block_resnet.sub(UpperCamelCase , UpperCamelCase )
elif re_encoder_block_proj_out.fullmatch(UpperCamelCase ):
__UpperCAmelCase : Optional[int] = re_encoder_block_proj_out.match(UpperCamelCase )
__UpperCAmelCase : Optional[Any] = regex_match.groups()
__UpperCAmelCase : Tuple = f"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
__UpperCAmelCase : Dict = re_encoder_block_proj_out.sub(UpperCamelCase , UpperCamelCase )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(UpperCamelCase ):
__UpperCAmelCase : Tuple = re_decoder_block_conv_out.match(UpperCamelCase )
__UpperCAmelCase : Dict = regex_match.groups()
__UpperCAmelCase : List[str] = int(groups[2] ) * 2 + int(groups[3] ) - 2
__UpperCAmelCase : Tuple = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
__UpperCAmelCase : Any = re_decoder_block_conv_out.sub(UpperCamelCase , UpperCamelCase )
elif re_decoder_block_resnet.fullmatch(UpperCamelCase ):
__UpperCAmelCase : Optional[Any] = re_decoder_block_resnet.match(UpperCamelCase )
__UpperCAmelCase : Optional[Any] = regex_match.groups()
__UpperCAmelCase : Union[str, Any] = int(groups[2] ) * 2 + int(groups[3] ) - 2
__UpperCAmelCase : Optional[int] = {"1": 1, "3": 2}[groups[-2]]
__UpperCAmelCase : Union[str, Any] = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
__UpperCAmelCase : Optional[int] = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
__UpperCAmelCase : Optional[Any] = prefix + resnet_block
__UpperCAmelCase : Union[str, Any] = re_decoder_block_resnet.sub(UpperCamelCase , UpperCamelCase )
elif re_decoder_block_proj_in.fullmatch(UpperCamelCase ):
__UpperCAmelCase : Union[str, Any] = re_decoder_block_proj_in.match(UpperCamelCase )
__UpperCAmelCase : List[Any] = regex_match.groups()
__UpperCAmelCase : Optional[int] = f"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
__UpperCAmelCase : Union[str, Any] = re_decoder_block_proj_in.sub(UpperCamelCase , UpperCamelCase )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(UpperCamelCase ):
__UpperCAmelCase : str = re_prior_cond_conv_out.match(UpperCamelCase )
__UpperCAmelCase : List[Any] = regex_match.groups()
__UpperCAmelCase : Optional[Any] = int(groups[1] ) * 2 + int(groups[2] ) - 2
__UpperCAmelCase : Optional[Any] = f"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
__UpperCAmelCase : Union[str, Any] = re_prior_cond_conv_out.sub(UpperCamelCase , UpperCamelCase )
elif re_prior_cond_resnet.fullmatch(UpperCamelCase ):
__UpperCAmelCase : Optional[Any] = re_prior_cond_resnet.match(UpperCamelCase )
__UpperCAmelCase : List[Any] = regex_match.groups()
__UpperCAmelCase : List[Any] = int(groups[1] ) * 2 + int(groups[2] ) - 2
__UpperCAmelCase : Optional[int] = {"1": 1, "3": 2}[groups[-2]]
__UpperCAmelCase : List[Any] = f"conditioner_blocks.upsampler.upsample_block.{block_index}."
__UpperCAmelCase : List[Any] = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
__UpperCAmelCase : List[Any] = prefix + resnet_block
__UpperCAmelCase : List[str] = re_prior_cond_resnet.sub(UpperCamelCase , UpperCamelCase )
elif re_prior_cond_proj_in.fullmatch(UpperCamelCase ):
__UpperCAmelCase : Any = re_prior_cond_proj_in.match(UpperCamelCase )
__UpperCAmelCase : Dict = regex_match.groups()
__UpperCAmelCase : Tuple = f"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
__UpperCAmelCase : Dict = re_prior_cond_proj_in.sub(UpperCamelCase , UpperCamelCase )
# keep original key
else:
__UpperCAmelCase : int = original_key
__UpperCAmelCase : List[str] = replace_key(UpperCamelCase )
if f"{key_prefix}.{key}" not in model_state_dict or key is None:
print(f"failed converting {original_key} to {key}, does not match" )
# handle missmatched shape
elif value.shape != model_state_dict[f"{key_prefix}.{key}"].shape:
__UpperCAmelCase : Optional[Any] = model_state_dict[f"{key_prefix}.{key}"]
print(f"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match" )
__UpperCAmelCase : Dict = original_key
__UpperCAmelCase : str = original_key
__UpperCAmelCase : Dict = value
return new_dict
@torch.no_grad()
def _UpperCamelCase ( UpperCamelCase=None , UpperCamelCase=None ) -> Optional[int]:
"""simple docstring"""
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(f"{pytorch_dump_folder_path}/{file.split('/' )[-1]}" ):
__UpperCAmelCase : Optional[int] = requests.get(f"{PREFIX}{file}" , allow_redirects=UpperCamelCase )
os.makedirs(f"{pytorch_dump_folder_path}/" , exist_ok=UpperCamelCase )
open(f"{pytorch_dump_folder_path}/{file.split('/' )[-1]}" , "wb" ).write(r.content )
__UpperCAmelCase : List[Any] = MODEL_MAPPING[model_name.split("/" )[-1]]
__UpperCAmelCase : Optional[Any] = JukeboxConfig.from_pretrained(UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = JukeboxModel(UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = []
__UpperCAmelCase : Tuple = {}
for i, dict_name in enumerate(UpperCamelCase ):
__UpperCAmelCase : Optional[int] = torch.load(f"{pytorch_dump_folder_path}/{dict_name.split('/' )[-1]}" )["model"]
__UpperCAmelCase : List[Any] = {}
for k in old_dic.keys():
if k.endswith(".b" ):
__UpperCAmelCase : int = old_dic[k]
elif k.endswith(".w" ):
__UpperCAmelCase : Optional[Any] = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
__UpperCAmelCase : Union[str, Any] = old_dic[k]
else:
__UpperCAmelCase : Any = old_dic[k]
__UpperCAmelCase : Union[str, Any] = "vqvae" if i == 0 else f"priors.{3 - i}"
__UpperCAmelCase : List[str] = fix_jukebox_keys(UpperCamelCase , model.state_dict() , UpperCamelCase , UpperCamelCase )
weight_dict.append(UpperCamelCase )
__UpperCAmelCase : Optional[Any] = weight_dict.pop(0 )
model.vqvae.load_state_dict(UpperCamelCase )
for i in range(len(UpperCamelCase ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(UpperCamelCase ).mkdir(exist_ok=UpperCamelCase )
with open(f"{pytorch_dump_folder_path}/mapping.json" , "w" ) as txtfile:
json.dump(UpperCamelCase , UpperCamelCase )
print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(UpperCamelCase )
return weight_dict
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""jukebox-5b-lyrics""",
type=str,
help="""Name of the model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""jukebox-5b-lyrics-converted""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
A = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 77 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = "cpu" , UpperCamelCase = None ) -> None:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = torch.load(UpperCamelCase , map_location=UpperCamelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(UpperCamelCase , torch.Tensor ):
raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin" )
__UpperCAmelCase : Optional[Any] = v.half()
if save_path is None: # overwrite src_path
__UpperCAmelCase : str = src_path
torch.save(UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 77 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : List[Any] = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase ) -> Tuple:
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : List[Any] = emb.weight.shape
__UpperCAmelCase : Optional[Any] = nn.Linear(UpperCamelCase , UpperCamelCase , bias=UpperCamelCase )
__UpperCAmelCase : Optional[Any] = emb.weight.data
return lin_layer
def _UpperCamelCase ( UpperCamelCase ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : Any = torch.load(UpperCamelCase , map_location="cpu" )
__UpperCAmelCase : Union[str, Any] = mam_aaa["args"] or mam_aaa["cfg"]["model"]
__UpperCAmelCase : Optional[Any] = mam_aaa["model"]
remove_ignore_keys_(UpperCamelCase )
__UpperCAmelCase : int = state_dict["encoder.embed_tokens.weight"].shape[0]
__UpperCAmelCase : int = MaMaaaConfig(
vocab_size=UpperCamelCase , max_position_embeddings=1024 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="relu" , )
__UpperCAmelCase : Optional[int] = state_dict["decoder.embed_tokens.weight"]
__UpperCAmelCase : List[str] = MaMaaaForConditionalGeneration(UpperCamelCase )
model.model.load_state_dict(UpperCamelCase , strict=UpperCamelCase )
__UpperCAmelCase : Any = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
A = parser.parse_args()
A = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 77 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = df.shape[:1][0]
# If you're using some other dataset input the target column
A = df.iloc[:, 1:2]
A = actual_data.values.reshape(len_data, 1)
A = MinMaxScaler().fit_transform(actual_data)
A = 10
A = 5
A = 20
A = len_data - periods * look_back
A = actual_data[:division]
A = actual_data[division - look_back :]
A , A = [], []
A , A = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
A = np.array(train_x)
A = np.array(test_x)
A = np.array([list(i.ravel()) for i in train_y])
A = np.array([list(i.ravel()) for i in test_y])
A = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="""mean_squared_error""", optimizer="""adam""")
A = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
A = model.predict(x_test)
| 77 | 1 |
"""simple docstring"""
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
__UpperCAmelCase : Any = multiprocessing.Manager()
__UpperCAmelCase : Dict = manager.list()
__UpperCAmelCase : Union[str, Any] = multiprocessing.Process(target=UpperCamelCase , args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append("timed out" )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Any:
"""simple docstring"""
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
__UpperCAmelCase : Dict = shutil.rmtree
__UpperCAmelCase : Optional[Any] = os.rmdir
__UpperCAmelCase : int = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
__UpperCAmelCase : Any = {}
with swallow_io():
with time_limit(UpperCamelCase ):
exec(UpperCamelCase , UpperCamelCase )
result.append("passed" )
except TimeoutException:
result.append("timed out" )
except BaseException as e:
result.append(f"failed: {e}" )
# Needed for cleaning up.
__UpperCAmelCase : Union[str, Any] = rmtree
__UpperCAmelCase : Union[str, Any] = rmdir
__UpperCAmelCase : int = chdir
@contextlib.contextmanager
def _UpperCamelCase ( UpperCamelCase ) -> str:
"""simple docstring"""
def signal_handler(UpperCamelCase , UpperCamelCase ):
raise TimeoutException("Timed out!" )
signal.setitimer(signal.ITIMER_REAL , UpperCamelCase )
signal.signal(signal.SIGALRM , UpperCamelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def _UpperCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
__UpperCAmelCase : List[Any] = WriteOnlyStringIO()
with contextlib.redirect_stdout(UpperCamelCase ):
with contextlib.redirect_stderr(UpperCamelCase ):
with redirect_stdin(UpperCamelCase ):
yield
@contextlib.contextmanager
def _UpperCamelCase ( ) -> int:
"""simple docstring"""
with tempfile.TemporaryDirectory() as dirname:
with chdir(UpperCamelCase ):
yield dirname
class a__ ( __magic_name__ ):
pass
class a__ ( io.StringIO ):
def a_ ( self : Union[str, Any] , *UpperCamelCase_ : List[str] , **UpperCamelCase_ : List[str]):
"""simple docstring"""
raise OSError
def a_ ( self : str , *UpperCamelCase_ : Tuple , **UpperCamelCase_ : List[Any]):
"""simple docstring"""
raise OSError
def a_ ( self : Any , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
raise OSError
def a_ ( self : Optional[int] , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Tuple):
"""simple docstring"""
return False
class a__ ( contextlib._RedirectStream ): # type: ignore
lowercase_ = "stdin"
@contextlib.contextmanager
def _UpperCamelCase ( UpperCamelCase ) -> Dict:
"""simple docstring"""
if root == ".":
yield
return
__UpperCAmelCase : Tuple = os.getcwd()
os.chdir(UpperCamelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase=None ) -> int:
"""simple docstring"""
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS , (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA , (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK , (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
__UpperCAmelCase : Any = None
__UpperCAmelCase : int = None
import os
__UpperCAmelCase : Tuple = "1"
__UpperCAmelCase : List[Any] = None
__UpperCAmelCase : List[Any] = None
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : Union[str, Any] = None
__UpperCAmelCase : Tuple = None
__UpperCAmelCase : List[str] = None
__UpperCAmelCase : Any = None
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : Tuple = None
__UpperCAmelCase : Any = None
__UpperCAmelCase : List[str] = None
__UpperCAmelCase : Optional[Any] = None
__UpperCAmelCase : Union[str, Any] = None
__UpperCAmelCase : Optional[Any] = None
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : Union[str, Any] = None
__UpperCAmelCase : Any = None
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : Optional[Any] = None
__UpperCAmelCase : Union[str, Any] = None
__UpperCAmelCase : str = None
__UpperCAmelCase : List[str] = None
__UpperCAmelCase : Union[str, Any] = None
__UpperCAmelCase : Tuple = None
__UpperCAmelCase : Dict = None
__UpperCAmelCase : int = None
__UpperCAmelCase : Dict = None
import shutil
__UpperCAmelCase : str = None
__UpperCAmelCase : Union[str, Any] = None
__UpperCAmelCase : List[str] = None
import subprocess
__UpperCAmelCase : Optional[int] = None # type: ignore
__UpperCAmelCase : str = None
import sys
__UpperCAmelCase : Optional[Any] = None
__UpperCAmelCase : List[Any] = None
__UpperCAmelCase : Dict = None
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : Optional[int] = None
| 77 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
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__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = MBartTokenizer
lowercase_ = MBartTokenizerFast
lowercase_ = True
lowercase_ = True
def a_ ( self : str):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__UpperCAmelCase : Any = MBartTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_)
tokenizer.save_pretrained(self.tmpdirname)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Dict = MBartTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = tokenizer.tokenize("This is a test")
self.assertListEqual(UpperCamelCase_ , ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCamelCase_) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__UpperCAmelCase : List[Any] = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
UpperCamelCase_ , [
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 : Any = tokenizer.convert_tokens_to_ids(UpperCamelCase_)
self.assertListEqual(
UpperCamelCase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(UpperCamelCase_)
self.assertListEqual(
UpperCamelCase_ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def a_ ( self : Dict):
"""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 : Dict = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
__UpperCAmelCase : List[str] = self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : int = self.tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : int = tempfile.mkdtemp()
__UpperCAmelCase : Optional[int] = tokenizer_r.save_pretrained(UpperCamelCase_)
__UpperCAmelCase : Any = tokenizer_p.save_pretrained(UpperCamelCase_)
# 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(UpperCamelCase_ , UpperCamelCase_)
# Checks everything loads correctly in the same way
__UpperCAmelCase : int = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : Tuple = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCamelCase_)
# Save tokenizer rust, legacy_format=True
__UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
__UpperCAmelCase : Dict = tokenizer_r.save_pretrained(UpperCamelCase_ , legacy_format=UpperCamelCase_)
__UpperCAmelCase : int = tokenizer_p.save_pretrained(UpperCamelCase_)
# Checks it save with the same files
self.assertSequenceEqual(UpperCamelCase_ , UpperCamelCase_)
# Checks everything loads correctly in the same way
__UpperCAmelCase : int = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
shutil.rmtree(UpperCamelCase_)
# Save tokenizer rust, legacy_format=False
__UpperCAmelCase : Tuple = tempfile.mkdtemp()
__UpperCAmelCase : int = tokenizer_r.save_pretrained(UpperCamelCase_ , legacy_format=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = tokenizer_p.save_pretrained(UpperCamelCase_)
# 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 : Optional[Any] = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : str = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
shutil.rmtree(UpperCamelCase_)
@require_torch
@require_sentencepiece
@require_tokenizers
class a__ ( unittest.TestCase ):
lowercase_ = "facebook/mbart-large-en-ro"
lowercase_ = [
" 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.",
]
lowercase_ = [
"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.",
]
lowercase_ = [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]
@classmethod
def a_ ( cls : int):
"""simple docstring"""
__UpperCAmelCase : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en_XX" , tgt_lang="ro_RO")
__UpperCAmelCase : Union[str, Any] = 1
return cls
def a_ ( self : List[Any]):
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] , 250001)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] , 250004)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] , 250020)
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase_)
def a_ ( self : Optional[int]):
"""simple docstring"""
self.assertIn(UpperCamelCase_ , self.tokenizer.all_special_ids)
__UpperCAmelCase : Union[str, Any] = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
__UpperCAmelCase : Optional[Any] = self.tokenizer.decode(UpperCamelCase_ , skip_special_tokens=UpperCamelCase_)
__UpperCAmelCase : int = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , UpperCamelCase_)
self.assertNotIn(self.tokenizer.eos_token , UpperCamelCase_)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , UpperCamelCase_)
__UpperCAmelCase : Tuple = 10
__UpperCAmelCase : List[Any] = self.tokenizer(UpperCamelCase_ , max_length=UpperCamelCase_ , truncation=UpperCamelCase_).input_ids[0]
self.assertEqual(ids[-2] , 2)
self.assertEqual(ids[-1] , UpperCamelCase_)
self.assertEqual(len(UpperCamelCase_) , UpperCamelCase_)
def a_ ( self : Any):
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]) , [250026, 250001])
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = tempfile.mkdtemp()
__UpperCAmelCase : Union[str, Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCamelCase_)
__UpperCAmelCase : List[Any] = MBartTokenizer.from_pretrained(UpperCamelCase_)
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , UpperCamelCase_)
@require_torch
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCamelCase_ , 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][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=len(self.expected_src_tokens) , return_tensors="pt" , )
__UpperCAmelCase : Tuple = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id)
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_)
self.assertEqual((2, 14) , batch.input_ids.shape)
self.assertEqual((2, 14) , batch.attention_mask.shape)
__UpperCAmelCase : List[str] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase_)
self.assertEqual(2 , batch.decoder_input_ids[0, -1]) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [])
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE])
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : List[str] = self.tokenizer(self.src_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=3 , return_tensors="pt")
__UpperCAmelCase : Any = self.tokenizer(
text_target=self.tgt_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=10 , return_tensors="pt")
__UpperCAmelCase : int = targets["input_ids"]
__UpperCAmelCase : Any = shift_tokens_right(UpperCamelCase_ , self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1] , 3)
self.assertEqual(batch.decoder_input_ids.shape[1] , 10)
@require_torch
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : int = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="ar_AR")
self.assertEqual(
nested_simplify(UpperCamelCase_) , {
# A, test, EOS, en_XX
"input_ids": [[62, 3034, 2, 250004]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 250001,
} , )
| 77 | 1 |
"""simple docstring"""
from __future__ import annotations
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[str]: # noqa: E741
"""simple docstring"""
while r - l > 1:
__UpperCAmelCase : Dict = (l + r) // 2
if v[m] >= key:
__UpperCAmelCase : Optional[int] = m
else:
__UpperCAmelCase : List[str] = m # noqa: E741
return r
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
if len(UpperCamelCase ) == 0:
return 0
__UpperCAmelCase : Optional[int] = [0] * len(UpperCamelCase )
__UpperCAmelCase : str = 1
__UpperCAmelCase : List[Any] = v[0]
for i in range(1 , len(UpperCamelCase ) ):
if v[i] < tail[0]:
__UpperCAmelCase : Any = v[i]
elif v[i] > tail[length - 1]:
__UpperCAmelCase : Optional[Any] = v[i]
length += 1
else:
__UpperCAmelCase : int = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 77 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
class a__ :
def __init__( self : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = None
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.head
while temp is not None:
print(temp.data , end=" ")
__UpperCAmelCase : Tuple = temp.next
print()
def a_ ( self : int , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : List[str] = Node(UpperCamelCase_)
__UpperCAmelCase : str = self.head
__UpperCAmelCase : Optional[int] = new_node
def a_ ( self : str , UpperCamelCase_ : List[Any] , UpperCamelCase_ : str):
"""simple docstring"""
if node_data_a == node_data_a:
return
else:
__UpperCAmelCase : int = self.head
while node_a is not None and node_a.data != node_data_a:
__UpperCAmelCase : Tuple = node_a.next
__UpperCAmelCase : List[Any] = self.head
while node_a is not None and node_a.data != node_data_a:
__UpperCAmelCase : Optional[Any] = node_a.next
if node_a is None or node_a is None:
return
__UpperCAmelCase , __UpperCAmelCase : Any = 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()
| 77 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = 100 , ) -> float:
"""simple docstring"""
__UpperCAmelCase : Optional[int] = x_start
__UpperCAmelCase : List[str] = fnc(UpperCamelCase )
__UpperCAmelCase : Tuple = 0.0
for _ in range(UpperCamelCase ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
__UpperCAmelCase : int = (x_end - x_start) / steps + xa
__UpperCAmelCase : Tuple = fnc(UpperCamelCase )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
__UpperCAmelCase : List[Any] = xa
__UpperCAmelCase : Dict = fxa
return area
if __name__ == "__main__":
def _UpperCamelCase ( UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
return x**3 + x**2
print("""f(x) = x^3 + x^2""")
print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
A = 10
while i <= 100_000:
print(f'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
i *= 10
| 77 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
A = """
Human: <<task>>
Assistant: """
A = """huggingface-tools/default-prompts"""
A = {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase="run" ) -> List[str]:
"""simple docstring"""
if prompt_or_repo_id is None:
__UpperCAmelCase : Optional[int] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , UpperCamelCase ) is not None:
return prompt_or_repo_id
__UpperCAmelCase : str = cached_file(
UpperCamelCase , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(UpperCamelCase , "r" , encoding="utf-8" ) as f:
return f.read()
| 77 | 1 |
"""simple docstring"""
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
AlbertConfig,
BartConfig,
BertConfig,
CamembertConfig,
CTRLConfig,
DistilBertConfig,
DPRConfig,
ElectraConfig,
FlaubertConfig,
GPTaConfig,
LayoutLMConfig,
LxmertConfig,
OpenAIGPTConfig,
RobertaConfig,
TaConfig,
TFAlbertForPreTraining,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFCamembertForMaskedLM,
TFCTRLLMHeadModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
TFElectraForPreTraining,
TFFlaubertWithLMHeadModel,
TFGPTaLMHeadModel,
TFLayoutLMForMaskedLM,
TFLxmertForPreTraining,
TFLxmertVisualFeatureEncoder,
TFOpenAIGPTLMHeadModel,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFTaForConditionalGeneration,
TFTransfoXLLMHeadModel,
TFWavaVecaModel,
TFXLMRobertaForMaskedLM,
TFXLMWithLMHeadModel,
TFXLNetLMHeadModel,
TransfoXLConfig,
WavaVecaConfig,
WavaVecaModel,
XLMConfig,
XLMRobertaConfig,
XLNetConfig,
is_torch_available,
load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging
if is_torch_available():
import numpy as np
import torch
from . import (
AlbertForPreTraining,
BartForConditionalGeneration,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
CamembertForMaskedLM,
CTRLLMHeadModel,
DistilBertForMaskedLM,
DistilBertForQuestionAnswering,
DPRContextEncoder,
DPRQuestionEncoder,
DPRReader,
ElectraForPreTraining,
FlaubertWithLMHeadModel,
GPTaLMHeadModel,
LayoutLMForMaskedLM,
LxmertForPreTraining,
LxmertVisualFeatureEncoder,
OpenAIGPTLMHeadModel,
RobertaForMaskedLM,
RobertaForSequenceClassification,
TaForConditionalGeneration,
TransfoXLLMHeadModel,
XLMRobertaForMaskedLM,
XLMWithLMHeadModel,
XLNetLMHeadModel,
)
logging.set_verbosity_info()
A = {
"""bart""": (
BartConfig,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
BartForConditionalGeneration,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
),
"""bert""": (
BertConfig,
TFBertForPreTraining,
BertForPreTraining,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""bert-large-uncased-whole-word-masking-finetuned-squad""": (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""bert-large-cased-whole-word-masking-finetuned-squad""": (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""bert-base-cased-finetuned-mrpc""": (
BertConfig,
TFBertForSequenceClassification,
BertForSequenceClassification,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""dpr""": (
DPRConfig,
TFDPRQuestionEncoder,
TFDPRContextEncoder,
TFDPRReader,
DPRQuestionEncoder,
DPRContextEncoder,
DPRReader,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
),
"""gpt2""": (
GPTaConfig,
TFGPTaLMHeadModel,
GPTaLMHeadModel,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""xlnet""": (
XLNetConfig,
TFXLNetLMHeadModel,
XLNetLMHeadModel,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""xlm""": (
XLMConfig,
TFXLMWithLMHeadModel,
XLMWithLMHeadModel,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""xlm-roberta""": (
XLMRobertaConfig,
TFXLMRobertaForMaskedLM,
XLMRobertaForMaskedLM,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""transfo-xl""": (
TransfoXLConfig,
TFTransfoXLLMHeadModel,
TransfoXLLMHeadModel,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""openai-gpt""": (
OpenAIGPTConfig,
TFOpenAIGPTLMHeadModel,
OpenAIGPTLMHeadModel,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""roberta""": (
RobertaConfig,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
RobertaForMaskedLM,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""layoutlm""": (
LayoutLMConfig,
TFLayoutLMForMaskedLM,
LayoutLMForMaskedLM,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
),
"""roberta-large-mnli""": (
RobertaConfig,
TFRobertaForSequenceClassification,
RobertaForSequenceClassification,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""camembert""": (
CamembertConfig,
TFCamembertForMaskedLM,
CamembertForMaskedLM,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""flaubert""": (
FlaubertConfig,
TFFlaubertWithLMHeadModel,
FlaubertWithLMHeadModel,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""distilbert""": (
DistilBertConfig,
TFDistilBertForMaskedLM,
DistilBertForMaskedLM,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""distilbert-base-distilled-squad""": (
DistilBertConfig,
TFDistilBertForQuestionAnswering,
DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""lxmert""": (
LxmertConfig,
TFLxmertForPreTraining,
LxmertForPreTraining,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""lxmert-visual-feature-encoder""": (
LxmertConfig,
TFLxmertVisualFeatureEncoder,
LxmertVisualFeatureEncoder,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""ctrl""": (
CTRLConfig,
TFCTRLLMHeadModel,
CTRLLMHeadModel,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""albert""": (
AlbertConfig,
TFAlbertForPreTraining,
AlbertForPreTraining,
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""t5""": (
TaConfig,
TFTaForConditionalGeneration,
TaForConditionalGeneration,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""electra""": (
ElectraConfig,
TFElectraForPreTraining,
ElectraForPreTraining,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"""wav2vec2""": (
WavaVecaConfig,
TFWavaVecaModel,
WavaVecaModel,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
}
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase=False , UpperCamelCase=True ) -> Optional[Any]:
"""simple docstring"""
if model_type not in MODEL_CLASSES:
raise ValueError(f"Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}." )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = MODEL_CLASSES[model_type]
# Initialise TF model
if config_file in aws_config_map:
__UpperCAmelCase : List[str] = cached_file(UpperCamelCase , UpperCamelCase , force_download=not use_cached_models )
__UpperCAmelCase : Tuple = config_class.from_json_file(UpperCamelCase )
__UpperCAmelCase : Optional[int] = True
__UpperCAmelCase : str = True
print(f"Building TensorFlow model from configuration: {config}" )
__UpperCAmelCase : Union[str, Any] = model_class(UpperCamelCase )
# Load weights from tf checkpoint
if pytorch_checkpoint_path in aws_config_map.keys():
__UpperCAmelCase : int = cached_file(
UpperCamelCase , UpperCamelCase , force_download=not use_cached_models )
# Load PyTorch checkpoint in tf2 model:
__UpperCAmelCase : Union[str, Any] = load_pytorch_checkpoint_in_tfa_model(UpperCamelCase , UpperCamelCase )
if compare_with_pt_model:
__UpperCAmelCase : int = tf_model(tf_model.dummy_inputs , training=UpperCamelCase ) # build the network
__UpperCAmelCase : str = torch.load(UpperCamelCase , map_location="cpu" )
__UpperCAmelCase : List[Any] = pt_model_class.from_pretrained(
pretrained_model_name_or_path=UpperCamelCase , config=UpperCamelCase , state_dict=UpperCamelCase )
with torch.no_grad():
__UpperCAmelCase : Dict = pt_model(**pt_model.dummy_inputs )
__UpperCAmelCase : Optional[int] = pto[0].numpy()
__UpperCAmelCase : List[str] = tfo[0].numpy()
__UpperCAmelCase : List[Any] = np.amax(np.abs(np_pt - np_tf ) )
print(f"Max absolute difference between models outputs {diff}" )
assert diff <= 2e-2, f"Error, model absolute difference is >2e-2: {diff}"
# Save pytorch-model
print(f"Save TensorFlow model to {tf_dump_path}" )
tf_model.save_weights(UpperCamelCase , save_format="h5" )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase=None , UpperCamelCase=None , UpperCamelCase=False , UpperCamelCase=False , UpperCamelCase=False , UpperCamelCase=False , ) -> Any:
"""simple docstring"""
if args_model_type is None:
__UpperCAmelCase : Dict = list(MODEL_CLASSES.keys() )
else:
__UpperCAmelCase : List[Any] = [args_model_type]
for j, model_type in enumerate(UpperCamelCase , start=1 ):
print("=" * 100 )
print(f" Converting model type {j}/{len(UpperCamelCase )}: {model_type}" )
print("=" * 100 )
if model_type not in MODEL_CLASSES:
raise ValueError(f"Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}." )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : List[Any] = MODEL_CLASSES[model_type]
if model_shortcut_names_or_path is None:
__UpperCAmelCase : List[Any] = list(aws_model_maps.keys() )
if config_shortcut_names_or_path is None:
__UpperCAmelCase : int = model_shortcut_names_or_path
for i, (model_shortcut_name, config_shortcut_name) in enumerate(
zip(UpperCamelCase , UpperCamelCase ) , start=1 ):
print("-" * 100 )
if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
if not only_convert_finetuned_models:
print(f" Skipping finetuned checkpoint {model_shortcut_name}" )
continue
__UpperCAmelCase : Optional[Any] = model_shortcut_name
elif only_convert_finetuned_models:
print(f" Skipping not finetuned checkpoint {model_shortcut_name}" )
continue
print(
f" Converting checkpoint {i}/{len(UpperCamelCase )}: {model_shortcut_name} - model_type {model_type}" )
print("-" * 100 )
if config_shortcut_name in aws_config_map:
__UpperCAmelCase : Dict = cached_file(UpperCamelCase , UpperCamelCase , force_download=not use_cached_models )
else:
__UpperCAmelCase : Optional[Any] = config_shortcut_name
if model_shortcut_name in aws_model_maps:
__UpperCAmelCase : int = cached_file(UpperCamelCase , UpperCamelCase , force_download=not use_cached_models )
else:
__UpperCAmelCase : List[str] = model_shortcut_name
if os.path.isfile(UpperCamelCase ):
__UpperCAmelCase : Tuple = "converted_model"
convert_pt_checkpoint_to_tf(
model_type=UpperCamelCase , pytorch_checkpoint_path=UpperCamelCase , config_file=UpperCamelCase , tf_dump_path=os.path.join(UpperCamelCase , model_shortcut_name + "-tf_model.h5" ) , compare_with_pt_model=UpperCamelCase , )
if remove_cached_files:
os.remove(UpperCamelCase )
os.remove(UpperCamelCase )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
)
parser.add_argument(
"""--model_type""",
default=None,
type=str,
help=(
f'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
"""convert all the models from AWS."""
),
)
parser.add_argument(
"""--pytorch_checkpoint_path""",
default=None,
type=str,
help=(
"""Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
"""If not given, will download and convert all the checkpoints from AWS."""
),
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
help=(
"""The config json file corresponding to the pre-trained model. \n"""
"""This specifies the model architecture. If not given and """
"""--pytorch_checkpoint_path is not given or is a shortcut name """
"""use the configuration associated to the shortcut name on the AWS"""
),
)
parser.add_argument(
"""--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
)
parser.add_argument(
"""--use_cached_models""",
action="""store_true""",
help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
)
parser.add_argument(
"""--remove_cached_files""",
action="""store_true""",
help="""Remove pytorch models after conversion (save memory when converting in batches).""",
)
parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
A = parser.parse_args()
# if args.pytorch_checkpoint_path is not None:
# convert_pt_checkpoint_to_tf(args.model_type.lower(),
# args.pytorch_checkpoint_path,
# args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
# args.tf_dump_path,
# compare_with_pt_model=args.compare_with_pt_model,
# use_cached_models=args.use_cached_models)
# else:
convert_all_pt_checkpoints_to_tf(
args.model_type.lower() if args.model_type is not None else None,
args.tf_dump_path,
model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
if args.pytorch_checkpoint_path is not None
else None,
config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
compare_with_pt_model=args.compare_with_pt_model,
use_cached_models=args.use_cached_models,
remove_cached_files=args.remove_cached_files,
only_convert_finetuned_models=args.only_convert_finetuned_models,
)
| 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ErnieForCausalLM""",
"""ErnieForMaskedLM""",
"""ErnieForMultipleChoice""",
"""ErnieForNextSentencePrediction""",
"""ErnieForPreTraining""",
"""ErnieForQuestionAnswering""",
"""ErnieForSequenceClassification""",
"""ErnieForTokenClassification""",
"""ErnieModel""",
"""ErniePreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 | 1 |
"""simple docstring"""
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class a__ ( unittest.TestCase ):
def __init__( self : Tuple , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Tuple = parent
def a_ ( self : Dict):
"""simple docstring"""
return {}
def _UpperCamelCase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : List[Any] = "<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR=\"FFFFFF\">\n <HR>\n <a href=\"http://google.com\">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style=\"color:#0000FF\">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>"
__UpperCAmelCase : List[str] = "\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n "
return [html_string_a, html_string_a]
@require_bsa
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = MarkupLMFeatureExtractor if is_bsa_available() else None
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : List[str] = MarkupLMFeatureExtractionTester(self)
@property
def a_ ( self : Tuple):
"""simple docstring"""
return self.feature_extract_tester.prepare_feat_extract_dict()
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.feature_extraction_class()
# Test not batched input
__UpperCAmelCase : Tuple = get_html_strings()[0]
__UpperCAmelCase : Tuple = feature_extractor(UpperCamelCase_)
# fmt: off
__UpperCAmelCase : Dict = [["sample document", "Goog", "This is one header", "This is a another Header", "Travel from", "SFO to JFK", "on May 2, 2015 at 2:00 pm. For details go to confirm.com", "Traveler", "name", "is", "John Doe"]]
__UpperCAmelCase : List[Any] = [["/html/head/title", "/html/body/a", "/html/body/h1", "/html/body/h2", "/html/body/p", "/html/body/p/p/b[1]", "/html/body/p/p/b[2]/i", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/b", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/p"]]
# fmt: on
self.assertEqual(encoding.nodes , UpperCamelCase_)
self.assertEqual(encoding.xpaths , UpperCamelCase_)
# Test batched
__UpperCAmelCase : Optional[int] = get_html_strings()
__UpperCAmelCase : str = feature_extractor(UpperCamelCase_)
# fmt: off
__UpperCAmelCase : Optional[int] = expected_nodes + [["My First Heading", "My first paragraph."]]
__UpperCAmelCase : int = expected_xpaths + [["/html/body/h1", "/html/body/p"]]
self.assertEqual(len(encoding.nodes) , 2)
self.assertEqual(len(encoding.xpaths) , 2)
self.assertEqual(encoding.nodes , UpperCamelCase_)
self.assertEqual(encoding.xpaths , UpperCamelCase_)
| 77 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ( nn.Module ):
def __init__( self : Union[str, Any]):
"""simple docstring"""
super().__init__()
__UpperCAmelCase : Optional[int] = nn.Linear(3 , 4)
__UpperCAmelCase : str = nn.BatchNormad(4)
__UpperCAmelCase : int = nn.Linear(4 , 5)
def a_ ( self : str , UpperCamelCase_ : List[str]):
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(UpperCamelCase_)))
class a__ ( unittest.TestCase ):
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = ModelForTest()
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , model.state_dict())
__UpperCAmelCase : Union[str, Any] = os.path.join(UpperCamelCase_ , "index.json")
self.assertTrue(os.path.isfile(UpperCamelCase_))
# TODO: add tests on what is inside the index
for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
__UpperCAmelCase : Optional[int] = os.path.join(UpperCamelCase_ , F"{key}.dat")
self.assertTrue(os.path.isfile(UpperCamelCase_))
# TODO: add tests on the fact weights are properly loaded
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : int = [torch.floataa, torch.floataa, torch.bfloataa]
for dtype in dtypes:
__UpperCAmelCase : List[Any] = torch.randn(2 , 3 , dtype=UpperCamelCase_)
with TemporaryDirectory() as tmp_dir:
__UpperCAmelCase : Tuple = offload_weight(UpperCamelCase_ , "weight" , UpperCamelCase_ , {})
__UpperCAmelCase : Dict = os.path.join(UpperCamelCase_ , "weight.dat")
self.assertTrue(os.path.isfile(UpperCamelCase_))
self.assertDictEqual(UpperCamelCase_ , {"weight": {"shape": [2, 3], "dtype": str(UpperCamelCase_).split(".")[1]}})
__UpperCAmelCase : Optional[Any] = load_offloaded_weight(UpperCamelCase_ , index["weight"])
self.assertTrue(torch.equal(UpperCamelCase_ , UpperCamelCase_))
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = ModelForTest()
__UpperCAmelCase : Optional[int] = model.state_dict()
__UpperCAmelCase : List[str] = {k: v for k, v in state_dict.items() if "linear2" not in k}
__UpperCAmelCase : Optional[int] = {k: v for k, v in state_dict.items() if "linear2" in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : List[str] = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
__UpperCAmelCase : Optional[int] = {k: v for k, v in state_dict.items() if "weight" in k}
__UpperCAmelCase : Optional[Any] = {k: v for k, v in state_dict.items() if "weight" not in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
# Duplicates are removed
__UpperCAmelCase : str = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Any = {"a.1": 0, "a.10": 1, "a.2": 2}
__UpperCAmelCase : Union[str, Any] = extract_submodules_state_dict(UpperCamelCase_ , ["a.1", "a.2"])
self.assertDictEqual(UpperCamelCase_ , {"a.1": 0, "a.2": 2})
__UpperCAmelCase : int = {"a.1.a": 0, "a.10.a": 1, "a.2.a": 2}
__UpperCAmelCase : int = extract_submodules_state_dict(UpperCamelCase_ , ["a.1", "a.2"])
self.assertDictEqual(UpperCamelCase_ , {"a.1.a": 0, "a.2.a": 2})
| 77 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
A = None
A = logging.get_logger(__name__)
A = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
A = {
"""vocab_file""": {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
},
"""tokenizer_file""": {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
},
}
A = {
"""camembert-base""": 512,
}
A = """▁"""
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = ["input_ids", "attention_mask"]
lowercase_ = CamembertTokenizer
def __init__( self : Optional[int] , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : Dict=None , UpperCamelCase_ : int="<s>" , UpperCamelCase_ : List[Any]="</s>" , UpperCamelCase_ : Union[str, Any]="</s>" , UpperCamelCase_ : Any="<s>" , UpperCamelCase_ : Optional[int]="<unk>" , UpperCamelCase_ : List[Any]="<pad>" , UpperCamelCase_ : Union[str, Any]="<mask>" , UpperCamelCase_ : Any=["<s>NOTUSED", "</s>NOTUSED"] , **UpperCamelCase_ : Tuple , ):
"""simple docstring"""
__UpperCAmelCase : Any = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else mask_token
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , cls_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : List[str] = vocab_file
__UpperCAmelCase : Union[str, Any] = False if not self.vocab_file else True
def a_ ( self : List[str] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__UpperCAmelCase : List[Any] = [self.cls_token_id]
__UpperCAmelCase : List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def a_ ( self : Any , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Dict = [self.sep_token_id]
__UpperCAmelCase : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]
def a_ ( self : Optional[Any] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer.")
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : int = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_):
copyfile(self.vocab_file , UpperCamelCase_)
return (out_vocab_file,)
| 77 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelCase : Union[str, Any] = n - k
# Calculate C(n,k)
for i in range(UpperCamelCase ):
result *= n - i
result //= i + 1
return result
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
return binomial_coefficient(2 * node_count , UpperCamelCase ) // (node_count + 1)
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
if n < 0:
raise ValueError("factorial() not defined for negative values" )
__UpperCAmelCase : Optional[Any] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
return catalan_number(UpperCamelCase ) * factorial(UpperCamelCase )
if __name__ == "__main__":
A = int(input("""Enter the number of nodes: """).strip() or 0)
if node_count <= 0:
raise ValueError("""We need some nodes to work with.""")
print(
f'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
f'''binary trees and {catalan_number(node_count)} binary search trees.'''
)
| 77 | 1 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""")
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase = 1_6000 ) -> List[Any]:
"""simple docstring"""
__UpperCAmelCase : int = int(round(sample_rate * max_length ) )
if len(UpperCamelCase ) <= sample_length:
return wav
__UpperCAmelCase : int = randint(0 , len(UpperCamelCase ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class a__ :
lowercase_ = field(default=__magic_name__ , metadata={"help": "Name of a dataset from the datasets package"} )
lowercase_ = field(
default=__magic_name__ , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
lowercase_ = field(
default=__magic_name__ , metadata={"help": "A file containing the training audio paths and labels."} )
lowercase_ = field(
default=__magic_name__ , metadata={"help": "A file containing the validation audio paths and labels."} )
lowercase_ = field(
default="train" , metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
} , )
lowercase_ = field(
default="validation" , metadata={
"help": (
"The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
)
} , )
lowercase_ = field(
default="audio" , metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"} , )
lowercase_ = field(
default="label" , metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"} )
lowercase_ = field(
default=__magic_name__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
lowercase_ = field(
default=__magic_name__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
lowercase_ = field(
default=2_0 , metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."} , )
@dataclass
class a__ :
lowercase_ = field(
default="facebook/wav2vec2-base" , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} , )
lowercase_ = field(
default=__magic_name__ , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
lowercase_ = field(
default=__magic_name__ , metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"} )
lowercase_ = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
lowercase_ = field(
default=__magic_name__ , metadata={"help": "Name or path of preprocessor config."} )
lowercase_ = field(
default=__magic_name__ , metadata={"help": "Whether to freeze the feature encoder layers of the model."} )
lowercase_ = field(
default=__magic_name__ , metadata={"help": "Whether to generate an attention mask in the feature extractor."} )
lowercase_ = field(
default=__magic_name__ , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
lowercase_ = field(
default=__magic_name__ , metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
lowercase_ = field(
default=__magic_name__ , metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."} , )
def a_ ( self : Optional[int]):
"""simple docstring"""
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"The argument `--freeze_feature_extractor` is deprecated and "
"will be removed in a future version. Use `--freeze_feature_encoder`"
"instead. Setting `freeze_feature_encoder==True`." , UpperCamelCase_ , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"The argument `--freeze_feature_extractor` is deprecated and "
"should not be used in combination with `--freeze_feature_encoder`."
"Only make use of `--freeze_feature_encoder`.")
def _UpperCamelCase ( ) -> Any:
"""simple docstring"""
# 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 : Tuple = 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 : Any = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : int = 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_audio_classification" , UpperCamelCase , UpperCamelCase )
# 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 : List[str] = training_args.get_process_log_level()
logger.setLevel(UpperCamelCase )
transformers.utils.logging.set_verbosity(UpperCamelCase )
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}" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
__UpperCAmelCase : str = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__UpperCAmelCase : Dict = 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 train from scratch." )
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." )
# Initialize our dataset and prepare it for the audio classification task.
__UpperCAmelCase : Optional[int] = DatasetDict()
__UpperCAmelCase : Union[str, Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
__UpperCAmelCase : Tuple = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
"Make sure to set `--audio_column_name` to the correct audio column - one of "
f"{', '.join(raw_datasets['train'].column_names )}." )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. "
"Make sure to set `--label_column_name` to the correct text column - one of "
f"{', '.join(raw_datasets['train'].column_names )}." )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
__UpperCAmelCase : Optional[Any] = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
__UpperCAmelCase : List[str] = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
__UpperCAmelCase : Tuple = feature_extractor.model_input_names[0]
def train_transforms(UpperCamelCase ):
__UpperCAmelCase : Optional[int] = []
for audio in batch[data_args.audio_column_name]:
__UpperCAmelCase : int = random_subsample(
audio["array"] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(UpperCamelCase )
__UpperCAmelCase : Any = feature_extractor(UpperCamelCase , sampling_rate=feature_extractor.sampling_rate )
__UpperCAmelCase : Any = {model_input_name: inputs.get(UpperCamelCase )}
__UpperCAmelCase : int = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(UpperCamelCase ):
__UpperCAmelCase : Any = [audio["array"] for audio in batch[data_args.audio_column_name]]
__UpperCAmelCase : List[Any] = feature_extractor(UpperCamelCase , sampling_rate=feature_extractor.sampling_rate )
__UpperCAmelCase : Union[str, Any] = {model_input_name: inputs.get(UpperCamelCase )}
__UpperCAmelCase : List[Any] = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
__UpperCAmelCase : Tuple = raw_datasets["train"].features[data_args.label_column_name].names
__UpperCAmelCase , __UpperCAmelCase : List[Any] = {}, {}
for i, label in enumerate(UpperCamelCase ):
__UpperCAmelCase : List[str] = str(UpperCamelCase )
__UpperCAmelCase : str = label
# Load the accuracy metric from the datasets package
__UpperCAmelCase : Union[str, Any] = evaluate.load("accuracy" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(UpperCamelCase ):
__UpperCAmelCase : str = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=UpperCamelCase , references=eval_pred.label_ids )
__UpperCAmelCase : Dict = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(UpperCamelCase ) , labelaid=UpperCamelCase , idalabel=UpperCamelCase , finetuning_task="audio-classification" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__UpperCAmelCase : Tuple = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=UpperCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
__UpperCAmelCase : Any = (
raw_datasets["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(UpperCamelCase , output_all_columns=UpperCamelCase )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
__UpperCAmelCase : Union[str, Any] = (
raw_datasets["eval"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(UpperCamelCase , output_all_columns=UpperCamelCase )
# Initialize our trainer
__UpperCAmelCase : Optional[int] = Trainer(
model=UpperCamelCase , args=UpperCamelCase , train_dataset=raw_datasets["train"] if training_args.do_train else None , eval_dataset=raw_datasets["eval"] if training_args.do_eval else None , compute_metrics=UpperCamelCase , tokenizer=UpperCamelCase , )
# Training
if training_args.do_train:
__UpperCAmelCase : List[Any] = None
if training_args.resume_from_checkpoint is not None:
__UpperCAmelCase : Any = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__UpperCAmelCase : Tuple = last_checkpoint
__UpperCAmelCase : Optional[Any] = trainer.train(resume_from_checkpoint=UpperCamelCase )
trainer.save_model()
trainer.log_metrics("train" , train_result.metrics )
trainer.save_metrics("train" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
__UpperCAmelCase : Union[str, Any] = trainer.evaluate()
trainer.log_metrics("eval" , UpperCamelCase )
trainer.save_metrics("eval" , UpperCamelCase )
# Write model card and (optionally) push to hub
__UpperCAmelCase : Any = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "audio-classification",
"dataset": data_args.dataset_name,
"tags": ["audio-classification"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**UpperCamelCase )
else:
trainer.create_model_card(**UpperCamelCase )
if __name__ == "__main__":
main()
| 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
"""processing_trocr""": ["""TrOCRProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TrOCRForCausalLM""",
"""TrOCRPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 | 1 |
"""simple docstring"""
import sys
def _UpperCamelCase ( UpperCamelCase ) -> Tuple:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = len(UpperCamelCase )
__UpperCAmelCase : str = [[0 for x in range(UpperCamelCase )] for x in range(UpperCamelCase )]
__UpperCAmelCase : Tuple = [[0 for x in range(UpperCamelCase )] for x in range(UpperCamelCase )]
for chain_length in range(2 , UpperCamelCase ):
for a in range(1 , n - chain_length + 1 ):
__UpperCAmelCase : Optional[Any] = a + chain_length - 1
__UpperCAmelCase : List[str] = sys.maxsize
for c in range(UpperCamelCase , UpperCamelCase ):
__UpperCAmelCase : Optional[int] = (
matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
)
if cost < matrix[a][b]:
__UpperCAmelCase : Any = cost
__UpperCAmelCase : List[Any] = c
return matrix, sol
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
if i == j:
print("A" + str(UpperCamelCase ) , end=" " )
else:
print("(" , end=" " )
print_optiomal_solution(UpperCamelCase , UpperCamelCase , optimal_solution[i][j] )
print_optiomal_solution(UpperCamelCase , optimal_solution[i][j] + 1 , UpperCamelCase )
print(")" , end=" " )
def _UpperCamelCase ( ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : List[str] = [30, 35, 15, 5, 10, 20, 25]
__UpperCAmelCase : Any = len(UpperCamelCase )
# Size of matrix created from above array will be
# 30*35 35*15 15*5 5*10 10*20 20*25
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = matrix_chain_order(UpperCamelCase )
print("No. of Operation required: " + str(matrix[1][n - 1] ) )
print_optiomal_solution(UpperCamelCase , 1 , n - 1 )
if __name__ == "__main__":
main()
| 77 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self : Optional[Any] , UpperCamelCase_ : int , UpperCamelCase_ : Any=13 , UpperCamelCase_ : Optional[int]=3 , UpperCamelCase_ : int=224 , UpperCamelCase_ : int=30 , UpperCamelCase_ : str=400 , UpperCamelCase_ : List[Any]=True , UpperCamelCase_ : Optional[Any]=None , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Optional[int]=[0.5, 0.5, 0.5] , UpperCamelCase_ : Optional[Any]=[0.5, 0.5, 0.5] , ):
"""simple docstring"""
__UpperCAmelCase : Tuple = size if size is not None else {"height": 18, "width": 18}
__UpperCAmelCase : List[Any] = parent
__UpperCAmelCase : Tuple = batch_size
__UpperCAmelCase : Tuple = num_channels
__UpperCAmelCase : List[Any] = image_size
__UpperCAmelCase : str = min_resolution
__UpperCAmelCase : Tuple = max_resolution
__UpperCAmelCase : Optional[Any] = do_resize
__UpperCAmelCase : Any = size
__UpperCAmelCase : Any = do_normalize
__UpperCAmelCase : Any = image_mean
__UpperCAmelCase : Optional[Any] = image_std
def a_ ( self : str):
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = ViTImageProcessor if is_vision_available() else None
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = EfficientFormerImageProcessorTester(self)
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
return self.image_proc_tester.prepare_image_processor_dict()
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(UpperCamelCase_ , "image_mean"))
self.assertTrue(hasattr(UpperCamelCase_ , "image_std"))
self.assertTrue(hasattr(UpperCamelCase_ , "do_normalize"))
self.assertTrue(hasattr(UpperCamelCase_ , "do_resize"))
self.assertTrue(hasattr(UpperCamelCase_ , "size"))
def a_ ( self : Dict):
"""simple docstring"""
pass
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
__UpperCAmelCase : str = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , Image.Image)
# Test not batched input
__UpperCAmelCase : Optional[int] = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Optional[int] = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : int = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
__UpperCAmelCase : Union[str, Any] = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_ , numpify=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , np.ndarray)
# Test not batched input
__UpperCAmelCase : Tuple = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Any = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
__UpperCAmelCase : Any = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_ , torchify=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , torch.Tensor)
# Test not batched input
__UpperCAmelCase : Optional[Any] = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Optional[int] = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
| 77 | 1 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase = 100 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Any = sum(i * i for i in range(1 , n + 1 ) )
__UpperCAmelCase : Union[str, Any] = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 77 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": from_to(0.76455, 1.30795),
"""cubicfoot""": from_to(0.028, 35.3147),
"""cup""": from_to(0.000236588, 4226.75),
}
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> float:
"""simple docstring"""
if from_type not in METRIC_CONVERSION:
raise ValueError(
f"Invalid 'from_type' value: {from_type!r} Supported values are:\n"
+ ", ".join(UpperCamelCase ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
f"Invalid 'to_type' value: {to_type!r}. Supported values are:\n"
+ ", ".join(UpperCamelCase ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 77 | 1 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
A = get_tests_dir("""fixtures""")
class a__ ( unittest.TestCase ):
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Tuple = mock.Mock()
__UpperCAmelCase : str = 500
__UpperCAmelCase : Optional[Any] = {}
__UpperCAmelCase : int = HTTPError
__UpperCAmelCase : Dict = {}
# Download this model to make sure it's in the cache.
__UpperCAmelCase : Tuple = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=UpperCamelCase_) as mock_head:
__UpperCAmelCase : int = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# This check we did call the fake head request
mock_head.assert_called()
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Dict = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json")
def a_ ( self : int):
"""simple docstring"""
with self.assertRaises(UpperCamelCase_):
# config is in subfolder, the following should not work without specifying the subfolder
__UpperCAmelCase : Tuple = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")
__UpperCAmelCase : Tuple = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants" , subfolder="feature_extractor")
self.assertIsNotNone(UpperCamelCase_)
@is_staging_test
class a__ ( unittest.TestCase ):
@classmethod
def a_ ( cls : List[str]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = TOKEN
HfFolder.save_token(UpperCamelCase_)
@classmethod
def a_ ( cls : int):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id="test-image-processor")
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-image-processor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-image-processor")
except HTTPError:
pass
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : int = ViTImageProcessor.from_pretrained(UpperCamelCase_)
image_processor.push_to_hub("test-image-processor" , use_auth_token=self._token)
__UpperCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_))
# Reset repo
delete_repo(token=self._token , repo_id="test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
UpperCamelCase_ , repo_id="test-image-processor" , push_to_hub=UpperCamelCase_ , use_auth_token=self._token)
__UpperCAmelCase : Dict = ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_))
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Any = ViTImageProcessor.from_pretrained(UpperCamelCase_)
image_processor.push_to_hub("valid_org/test-image-processor" , use_auth_token=self._token)
__UpperCAmelCase : Union[str, Any] = ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_))
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
UpperCamelCase_ , repo_id="valid_org/test-image-processor-org" , push_to_hub=UpperCamelCase_ , use_auth_token=self._token)
__UpperCAmelCase : Union[str, Any] = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
for k, v in image_processor.__dict__.items():
self.assertEqual(UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_))
def a_ ( self : Optional[int]):
"""simple docstring"""
CustomImageProcessor.register_for_auto_class()
__UpperCAmelCase : int = CustomImageProcessor.from_pretrained(UpperCamelCase_)
image_processor.push_to_hub("test-dynamic-image-processor" , use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"} , )
__UpperCAmelCase : Any = AutoImageProcessor.from_pretrained(
F"{USER}/test-dynamic-image-processor" , trust_remote_code=UpperCamelCase_)
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , "CustomImageProcessor")
| 77 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = ShapEPipeline
lowercase_ = ["prompt"]
lowercase_ = ["prompt"]
lowercase_ = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
lowercase_ = False
@property
def a_ ( self : Optional[int]):
"""simple docstring"""
return 32
@property
def a_ ( self : Any):
"""simple docstring"""
return 32
@property
def a_ ( self : int):
"""simple docstring"""
return self.time_input_dim * 4
@property
def a_ ( self : List[Any]):
"""simple docstring"""
return 8
@property
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def a_ ( self : List[str]):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(UpperCamelCase_)
@property
def a_ ( self : Any):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Union[str, Any] = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
__UpperCAmelCase : Dict = PriorTransformer(**UpperCamelCase_)
return model
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Tuple = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
__UpperCAmelCase : List[Any] = ShapERenderer(**UpperCamelCase_)
return model
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.dummy_prior
__UpperCAmelCase : str = self.dummy_text_encoder
__UpperCAmelCase : int = self.dummy_tokenizer
__UpperCAmelCase : int = self.dummy_renderer
__UpperCAmelCase : Tuple = HeunDiscreteScheduler(
beta_schedule="exp" , num_train_timesteps=1024 , prediction_type="sample" , use_karras_sigmas=UpperCamelCase_ , clip_sample=UpperCamelCase_ , clip_sample_range=1.0 , )
__UpperCAmelCase : str = {
"prior": prior,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"renderer": renderer,
"scheduler": scheduler,
}
return components
def a_ ( self : Optional[int] , UpperCamelCase_ : Dict , UpperCamelCase_ : Any=0):
"""simple docstring"""
if str(UpperCamelCase_).startswith("mps"):
__UpperCAmelCase : List[Any] = torch.manual_seed(UpperCamelCase_)
else:
__UpperCAmelCase : str = torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)
__UpperCAmelCase : List[Any] = {
"prompt": "horse",
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "np",
}
return inputs
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : str = "cpu"
__UpperCAmelCase : Union[str, Any] = self.get_dummy_components()
__UpperCAmelCase : Union[str, Any] = self.pipeline_class(**UpperCamelCase_)
__UpperCAmelCase : Any = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = pipe(**self.get_dummy_inputs(UpperCamelCase_))
__UpperCAmelCase : Union[str, Any] = output.images[0]
__UpperCAmelCase : str = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__UpperCAmelCase : Union[str, Any] = np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def a_ ( self : Tuple):
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2])
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = torch_device == "cpu"
__UpperCAmelCase : Optional[Any] = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=UpperCamelCase_ , relax_max_difference=UpperCamelCase_ , )
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.get_dummy_components()
__UpperCAmelCase : List[str] = self.pipeline_class(**UpperCamelCase_)
__UpperCAmelCase : int = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = 1
__UpperCAmelCase : Any = 2
__UpperCAmelCase : Optional[Any] = self.get_dummy_inputs(UpperCamelCase_)
for key in inputs.keys():
if key in self.batch_params:
__UpperCAmelCase : List[Any] = batch_size * [inputs[key]]
__UpperCAmelCase : List[Any] = pipe(**UpperCamelCase_ , num_images_per_prompt=UpperCamelCase_)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def a_ ( self : List[str]):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Dict = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_np_out.npy")
__UpperCAmelCase : Optional[Any] = ShapEPipeline.from_pretrained("openai/shap-e")
__UpperCAmelCase : Any = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Dict = torch.Generator(device=UpperCamelCase_).manual_seed(0)
__UpperCAmelCase : int = pipe(
"a shark" , generator=UpperCamelCase_ , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type="np" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(UpperCamelCase_ , UpperCamelCase_)
| 77 | 1 |
"""simple docstring"""
from __future__ import annotations
A = []
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> bool:
"""simple docstring"""
for i in range(len(UpperCamelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(UpperCamelCase ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(UpperCamelCase , -1 , -1 ) , range(UpperCamelCase , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(UpperCamelCase , -1 , -1 ) , range(UpperCamelCase , len(UpperCamelCase ) ) ):
if board[i][j] == 1:
return False
return True
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> bool:
"""simple docstring"""
if row >= len(UpperCamelCase ):
solution.append(UpperCamelCase )
printboard(UpperCamelCase )
print()
return True
for i in range(len(UpperCamelCase ) ):
if is_safe(UpperCamelCase , UpperCamelCase , UpperCamelCase ):
__UpperCAmelCase : Any = 1
solve(UpperCamelCase , row + 1 )
__UpperCAmelCase : Dict = 0
return False
def _UpperCamelCase ( UpperCamelCase ) -> None:
"""simple docstring"""
for i in range(len(UpperCamelCase ) ):
for j in range(len(UpperCamelCase ) ):
if board[i][j] == 1:
print("Q" , end=" " )
else:
print("." , end=" " )
print()
# n=int(input("The no. of queens"))
A = 8
A = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("""The total no. of solutions are :""", len(solution))
| 77 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_features", "is_longer"]
def __init__( self : List[str] , UpperCamelCase_ : Dict=64 , UpperCamelCase_ : Tuple=48000 , UpperCamelCase_ : List[Any]=480 , UpperCamelCase_ : List[str]=10 , UpperCamelCase_ : str=1024 , UpperCamelCase_ : List[str]=0.0 , UpperCamelCase_ : Optional[int]=False , UpperCamelCase_ : float = 0 , UpperCamelCase_ : float = 14000 , UpperCamelCase_ : int = None , UpperCamelCase_ : str = "fusion" , UpperCamelCase_ : str = "repeatpad" , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
feature_size=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , padding_value=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Union[str, Any] = top_db
__UpperCAmelCase : Optional[Any] = truncation
__UpperCAmelCase : str = padding
__UpperCAmelCase : int = fft_window_size
__UpperCAmelCase : str = (fft_window_size >> 1) + 1
__UpperCAmelCase : List[Any] = hop_length
__UpperCAmelCase : Optional[Any] = max_length_s
__UpperCAmelCase : Tuple = max_length_s * sampling_rate
__UpperCAmelCase : str = sampling_rate
__UpperCAmelCase : int = frequency_min
__UpperCAmelCase : Optional[Any] = frequency_max
__UpperCAmelCase : Any = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCamelCase_ , min_frequency=UpperCamelCase_ , max_frequency=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , norm=UpperCamelCase_ , mel_scale="htk" , )
__UpperCAmelCase : Any = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCamelCase_ , min_frequency=UpperCamelCase_ , max_frequency=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , norm="slaney" , mel_scale="slaney" , )
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Dict = copy.deepcopy(self.__dict__)
__UpperCAmelCase : str = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def a_ ( self : int , UpperCamelCase_ : np.array , UpperCamelCase_ : Optional[np.array] = None):
"""simple docstring"""
__UpperCAmelCase : List[Any] = spectrogram(
UpperCamelCase_ , window_function(self.fft_window_size , "hann") , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=UpperCamelCase_ , log_mel="dB" , )
return log_mel_spectrogram.T
def a_ ( self : List[Any] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = np.array_split(list(range(0 , total_frames - chunk_frames + 1)) , 3)
if len(ranges[1]) == 0:
# if the audio is too short, we just use the first chunk
__UpperCAmelCase : str = [0]
if len(ranges[2]) == 0:
# if the audio is too short, we just use the first chunk
__UpperCAmelCase : Dict = [0]
# randomly choose index for each part
__UpperCAmelCase : Dict = np.random.choice(ranges[0])
__UpperCAmelCase : List[str] = np.random.choice(ranges[1])
__UpperCAmelCase : List[Any] = np.random.choice(ranges[2])
__UpperCAmelCase : List[Any] = mel[idx_front : idx_front + chunk_frames, :]
__UpperCAmelCase : List[str] = mel[idx_middle : idx_middle + chunk_frames, :]
__UpperCAmelCase : List[str] = mel[idx_back : idx_back + chunk_frames, :]
__UpperCAmelCase : Tuple = torch.tensor(mel[None, None, :])
__UpperCAmelCase : Union[str, Any] = torch.nn.functional.interpolate(
UpperCamelCase_ , size=[chunk_frames, 64] , mode="bilinear" , align_corners=UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = mel_shrink[0][0].numpy()
__UpperCAmelCase : Optional[int] = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0)
return mel_fusion
def a_ ( self : Optional[Any] , UpperCamelCase_ : np.array , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
__UpperCAmelCase : List[str] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
__UpperCAmelCase : List[Any] = len(UpperCamelCase_) - max_length
__UpperCAmelCase : int = np.random.randint(0 , overflow + 1)
__UpperCAmelCase : Union[str, Any] = waveform[idx : idx + max_length]
__UpperCAmelCase : Union[str, Any] = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters_slaney)[None, :]
elif truncation == "fusion":
__UpperCAmelCase : Any = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters)
__UpperCAmelCase : Dict = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
__UpperCAmelCase : Tuple = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
__UpperCAmelCase : List[str] = np.stack([mel, mel, mel, mel] , axis=0)
__UpperCAmelCase : Any = False
else:
__UpperCAmelCase : List[str] = self._random_mel_fusion(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = True
else:
raise NotImplementedError(F"data_truncating {truncation} not implemented")
else:
__UpperCAmelCase : Optional[Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
__UpperCAmelCase : Tuple = int(max_length / len(UpperCamelCase_))
__UpperCAmelCase : List[str] = np.stack(np.tile(UpperCamelCase_ , n_repeat + 1))[:max_length]
if padding == "repeatpad":
__UpperCAmelCase : Union[str, Any] = int(max_length / len(UpperCamelCase_))
__UpperCAmelCase : Optional[Any] = np.stack(np.tile(UpperCamelCase_ , UpperCamelCase_))
__UpperCAmelCase : int = np.pad(UpperCamelCase_ , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0)
if truncation == "fusion":
__UpperCAmelCase : Any = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters)
__UpperCAmelCase : List[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0)
else:
__UpperCAmelCase : Optional[int] = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters_slaney)[None, :]
return input_mel, longer
def __call__( self : Dict , UpperCamelCase_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , UpperCamelCase_ : str = None , UpperCamelCase_ : Optional[str] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[Union[str, TensorType]] = None , **UpperCamelCase_ : Any , ):
"""simple docstring"""
__UpperCAmelCase : int = truncation if truncation is not None else self.truncation
__UpperCAmelCase : Optional[Any] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
F" was sampled with {self.sampling_rate} and not {sampling_rate}.")
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug.")
__UpperCAmelCase : List[str] = isinstance(UpperCamelCase_ , np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(F"Only mono-channel audio is supported for input to {self}")
__UpperCAmelCase : str = is_batched_numpy or (
isinstance(UpperCamelCase_ , (list, tuple)) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)))
)
if is_batched:
__UpperCAmelCase : Dict = [np.asarray(UpperCamelCase_ , dtype=np.floataa) for speech in raw_speech]
elif not is_batched and not isinstance(UpperCamelCase_ , np.ndarray):
__UpperCAmelCase : Tuple = np.asarray(UpperCamelCase_ , dtype=np.floataa)
elif isinstance(UpperCamelCase_ , np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
__UpperCAmelCase : Optional[int] = raw_speech.astype(np.floataa)
# always return batch
if not is_batched:
__UpperCAmelCase : int = [np.asarray(UpperCamelCase_)]
# convert to mel spectrogram, truncate and pad if needed.
__UpperCAmelCase : Optional[int] = [
self._get_input_mel(UpperCamelCase_ , max_length if max_length else self.nb_max_samples , UpperCamelCase_ , UpperCamelCase_)
for waveform in raw_speech
]
__UpperCAmelCase : Tuple = []
__UpperCAmelCase : List[Any] = []
for mel, longer in padded_inputs:
input_mel.append(UpperCamelCase_)
is_longer.append(UpperCamelCase_)
if truncation == "fusion" and sum(UpperCamelCase_) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
__UpperCAmelCase : Any = np.random.randint(0 , len(UpperCamelCase_))
__UpperCAmelCase : Optional[int] = True
if isinstance(input_mel[0] , UpperCamelCase_):
__UpperCAmelCase : Tuple = [np.asarray(UpperCamelCase_ , dtype=np.floataa) for feature in input_mel]
# is_longer is a list of bool
__UpperCAmelCase : List[str] = [[longer] for longer in is_longer]
__UpperCAmelCase : Optional[int] = {"input_features": input_mel, "is_longer": is_longer}
__UpperCAmelCase : Optional[int] = BatchFeature(UpperCamelCase_)
if return_tensors is not None:
__UpperCAmelCase : Any = input_features.convert_to_tensors(UpperCamelCase_)
return input_features
| 77 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
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__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = KandinskyVaaInpaintPipeline
lowercase_ = ["image_embeds", "negative_image_embeds", "image", "mask_image"]
lowercase_ = [
"image_embeds",
"negative_image_embeds",
"image",
"mask_image",
]
lowercase_ = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
lowercase_ = False
@property
def a_ ( self : Dict):
"""simple docstring"""
return 32
@property
def a_ ( self : str):
"""simple docstring"""
return 32
@property
def a_ ( self : Any):
"""simple docstring"""
return self.time_input_dim
@property
def a_ ( self : Any):
"""simple docstring"""
return self.time_input_dim * 4
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
return 100
@property
def a_ ( self : Any):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Dict = {
"in_channels": 9,
# 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(**UpperCamelCase_)
return model
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def a_ ( self : str):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : int = VQModel(**self.dummy_movq_kwargs)
return model
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : int = self.dummy_unet
__UpperCAmelCase : Tuple = self.dummy_movq
__UpperCAmelCase : Any = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule="linear" , beta_start=0.00085 , beta_end=0.012 , clip_sample=UpperCamelCase_ , set_alpha_to_one=UpperCamelCase_ , steps_offset=1 , prediction_type="epsilon" , thresholding=UpperCamelCase_ , )
__UpperCAmelCase : List[str] = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def a_ ( self : str , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Dict=0):
"""simple docstring"""
__UpperCAmelCase : str = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(UpperCamelCase_)).to(UpperCamelCase_)
__UpperCAmelCase : Tuple = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1)).to(
UpperCamelCase_)
# create init_image
__UpperCAmelCase : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(UpperCamelCase_)).to(UpperCamelCase_)
__UpperCAmelCase : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1)[0]
__UpperCAmelCase : Union[str, Any] = Image.fromarray(np.uinta(UpperCamelCase_)).convert("RGB").resize((256, 256))
# create mask
__UpperCAmelCase : Any = np.ones((64, 64) , dtype=np.floataa)
__UpperCAmelCase : str = 0
if str(UpperCamelCase_).startswith("mps"):
__UpperCAmelCase : int = torch.manual_seed(UpperCamelCase_)
else:
__UpperCAmelCase : List[Any] = torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)
__UpperCAmelCase : List[str] = {
"image": init_image,
"mask_image": mask,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 64,
"width": 64,
"num_inference_steps": 2,
"guidance_scale": 4.0,
"output_type": "np",
}
return inputs
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Dict = "cpu"
__UpperCAmelCase : Optional[Any] = self.get_dummy_components()
__UpperCAmelCase : int = self.pipeline_class(**UpperCamelCase_)
__UpperCAmelCase : List[str] = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Any = pipe(**self.get_dummy_inputs(UpperCamelCase_))
__UpperCAmelCase : Any = output.images
__UpperCAmelCase : int = pipe(
**self.get_dummy_inputs(UpperCamelCase_) , return_dict=UpperCamelCase_ , )[0]
__UpperCAmelCase : int = image[0, -3:, -3:, -1]
__UpperCAmelCase : Dict = image_from_tuple[0, -3:, -3:, -1]
print(F"image.shape {image.shape}")
assert image.shape == (1, 64, 64, 3)
__UpperCAmelCase : Union[str, Any] = np.array(
[0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848])
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
def a_ ( self : List[Any]):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def a_ ( self : Tuple):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy")
__UpperCAmelCase : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png")
__UpperCAmelCase : Union[str, Any] = np.ones((768, 768) , dtype=np.floataa)
__UpperCAmelCase : Union[str, Any] = 0
__UpperCAmelCase : Optional[int] = "a hat"
__UpperCAmelCase : Any = KandinskyVaaPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior" , torch_dtype=torch.floataa)
pipe_prior.to(UpperCamelCase_)
__UpperCAmelCase : Optional[int] = KandinskyVaaInpaintPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder-inpaint" , torch_dtype=torch.floataa)
__UpperCAmelCase : str = pipeline.to(UpperCamelCase_)
pipeline.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = torch.Generator(device="cpu").manual_seed(0)
__UpperCAmelCase , __UpperCAmelCase : Dict = pipe_prior(
UpperCamelCase_ , generator=UpperCamelCase_ , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
__UpperCAmelCase : Optional[Any] = pipeline(
image=UpperCamelCase_ , mask_image=UpperCamelCase_ , image_embeds=UpperCamelCase_ , negative_image_embeds=UpperCamelCase_ , generator=UpperCamelCase_ , num_inference_steps=100 , height=768 , width=768 , output_type="np" , )
__UpperCAmelCase : str = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(UpperCamelCase_ , UpperCamelCase_)
| 77 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : Optional[Any] , UpperCamelCase_ : List[Any]="</s>" , UpperCamelCase_ : Tuple="<unk>" , UpperCamelCase_ : List[str]="<pad>" , UpperCamelCase_ : Union[str, Any]=125 , UpperCamelCase_ : Dict=None , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
if extra_ids > 0 and additional_special_tokens is None:
__UpperCAmelCase : int = [F"<extra_id_{i}>" for i in range(UpperCamelCase_)]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__UpperCAmelCase : Dict = len(set(filter(lambda UpperCamelCase_: bool("extra_id" in str(UpperCamelCase_)) , UpperCamelCase_)))
if extra_tokens != extra_ids:
raise ValueError(
F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
" extra_ids tokens")
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else pad_token
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else eos_token
__UpperCAmelCase : Optional[int] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else unk_token
super().__init__(
eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , extra_ids=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : List[str] = extra_ids
__UpperCAmelCase : int = 2**8 # utf is 8 bits
# define special tokens dict
__UpperCAmelCase : Dict[int, str] = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
__UpperCAmelCase : Any = len(self.special_tokens_encoder)
__UpperCAmelCase : List[Any] = len(UpperCamelCase_)
for i, token in enumerate(UpperCamelCase_):
__UpperCAmelCase : Union[str, Any] = self.vocab_size + i - n
__UpperCAmelCase : Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def a_ ( self : List[Any]):
"""simple docstring"""
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def a_ ( self : List[str] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase_ , token_ids_a=UpperCamelCase_ , already_has_special_tokens=UpperCamelCase_)
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(UpperCamelCase_)) + [1]
return ([0] * len(UpperCamelCase_)) + [1] + ([0] * len(UpperCamelCase_)) + [1]
def a_ ( self : Optional[Any] , UpperCamelCase_ : List[int]):
"""simple docstring"""
if len(UpperCamelCase_) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
" eos tokens being added.")
return token_ids
else:
return token_ids + [self.eos_token_id]
def a_ ( self : Dict , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Dict = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos) * [0]
return len(token_ids_a + eos + token_ids_a + eos) * [0]
def a_ ( self : Optional[int] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self._add_eos_if_not_present(UpperCamelCase_)
if token_ids_a is None:
return token_ids_a
else:
__UpperCAmelCase : List[Any] = self._add_eos_if_not_present(UpperCamelCase_)
return token_ids_a + token_ids_a
def a_ ( self : List[str] , UpperCamelCase_ : str):
"""simple docstring"""
__UpperCAmelCase : Any = [chr(UpperCamelCase_) for i in text.encode("utf-8")]
return tokens
def a_ ( self : Tuple , UpperCamelCase_ : List[Any]):
"""simple docstring"""
if token in self.special_tokens_encoder:
__UpperCAmelCase : Any = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
__UpperCAmelCase : int = self.added_tokens_encoder[token]
elif len(UpperCamelCase_) != 1:
__UpperCAmelCase : Optional[Any] = self.unk_token_id
else:
__UpperCAmelCase : Any = ord(UpperCamelCase_) + self._num_special_tokens
return token_id
def a_ ( self : Any , UpperCamelCase_ : List[str]):
"""simple docstring"""
if index in self.special_tokens_decoder:
__UpperCAmelCase : Any = self.special_tokens_decoder[index]
else:
__UpperCAmelCase : List[str] = chr(index - self._num_special_tokens)
return token
def a_ ( self : Dict , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : str = b""
for token in tokens:
if token in self.special_tokens_decoder:
__UpperCAmelCase : Tuple = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.added_tokens_decoder:
__UpperCAmelCase : Any = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.special_tokens_encoder:
__UpperCAmelCase : Optional[int] = token.encode("utf-8")
elif token in self.added_tokens_encoder:
__UpperCAmelCase : Optional[Any] = token.encode("utf-8")
else:
__UpperCAmelCase : Any = bytes([ord(UpperCamelCase_)])
bstring += tok_string
__UpperCAmelCase : List[Any] = bstring.decode("utf-8" , errors="ignore")
return string
def a_ ( self : Optional[int] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
return ()
| 77 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class a__ ( unittest.TestCase ):
lowercase_ = inspect.getfile(accelerate.test_utils )
lowercase_ = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_cli.py"] )
lowercase_ = ["accelerate", "launch"]
lowercase_ = Path.home() / ".cache/huggingface/accelerate"
lowercase_ = "default_config.yaml"
lowercase_ = config_folder / config_file
lowercase_ = config_folder / "_default_config.yaml"
lowercase_ = Path("tests/test_configs" )
@classmethod
def a_ ( cls : Dict):
"""simple docstring"""
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path)
@classmethod
def a_ ( cls : List[str]):
"""simple docstring"""
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path)
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy())
def a_ ( self : str):
"""simple docstring"""
for config in sorted(self.test_config_path.glob("**/*.yaml")):
with self.subTest(config_file=UpperCamelCase_):
execute_subprocess_async(
self.base_cmd + ["--config_file", str(UpperCamelCase_), self.test_file_path] , env=os.environ.copy())
def a_ ( self : int):
"""simple docstring"""
execute_subprocess_async(["accelerate", "test"] , env=os.environ.copy())
class a__ ( unittest.TestCase ):
lowercase_ = "test-tpu"
lowercase_ = "us-central1-a"
lowercase_ = "ls"
lowercase_ = ["accelerate", "tpu-config"]
lowercase_ = "cd /usr/share"
lowercase_ = "tests/test_samples/test_command_file.sh"
lowercase_ = "Running gcloud compute tpus tpu-vm ssh"
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = run_command(
self.cmd
+ ["--command", self.command, "--tpu_zone", self.tpu_zone, "--tpu_name", self.tpu_name, "--debug"] , return_stdout=UpperCamelCase_ , )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all" , UpperCamelCase_ , )
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/0_12_0.yaml",
"--command",
self.command,
"--tpu_zone",
self.tpu_zone,
"--tpu_name",
self.tpu_name,
"--debug",
] , return_stdout=UpperCamelCase_ , )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all" , UpperCamelCase_ , )
def a_ ( self : str):
"""simple docstring"""
__UpperCAmelCase : List[str] = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--debug"] , return_stdout=UpperCamelCase_)
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all" , UpperCamelCase_ , )
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : List[Any] = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--command", self.command, "--debug"] , return_stdout=UpperCamelCase_ , )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all" , UpperCamelCase_ , )
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/latest.yaml",
"--command",
self.command,
"--command",
"echo \"Hello World\"",
"--debug",
] , return_stdout=UpperCamelCase_ , )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all" , UpperCamelCase_ , )
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = run_command(
self.cmd
+ ["--config_file", "tests/test_configs/latest.yaml", "--command_file", self.command_file, "--debug"] , return_stdout=UpperCamelCase_ , )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all" , UpperCamelCase_ , )
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : str = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/0_12_0.yaml",
"--command_file",
self.command_file,
"--tpu_zone",
self.tpu_zone,
"--tpu_name",
self.tpu_name,
"--debug",
] , return_stdout=UpperCamelCase_ , )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all" , UpperCamelCase_ , )
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Any = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--install_accelerate", "--debug"] , return_stdout=UpperCamelCase_ , )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all" , UpperCamelCase_ , )
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Dict = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/latest.yaml",
"--install_accelerate",
"--accelerate_version",
"12.0.0",
"--debug",
] , return_stdout=UpperCamelCase_ , )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all" , UpperCamelCase_ , )
| 77 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self : List[Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple=3 , UpperCamelCase_ : Optional[int]=32 , UpperCamelCase_ : Dict=3 , UpperCamelCase_ : List[str]=10 , UpperCamelCase_ : str=[10, 20, 30, 40] , UpperCamelCase_ : Tuple=[1, 1, 2, 1] , UpperCamelCase_ : str=True , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Dict="relu" , UpperCamelCase_ : str=3 , UpperCamelCase_ : int=None , ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = parent
__UpperCAmelCase : List[str] = batch_size
__UpperCAmelCase : List[str] = image_size
__UpperCAmelCase : Tuple = num_channels
__UpperCAmelCase : Union[str, Any] = embeddings_size
__UpperCAmelCase : Dict = hidden_sizes
__UpperCAmelCase : Dict = depths
__UpperCAmelCase : Tuple = is_training
__UpperCAmelCase : List[Any] = use_labels
__UpperCAmelCase : Optional[int] = hidden_act
__UpperCAmelCase : str = num_labels
__UpperCAmelCase : Optional[int] = scope
__UpperCAmelCase : Dict = len(UpperCamelCase_)
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__UpperCAmelCase : Dict = self.get_config()
return config, pixel_values
def a_ ( self : Dict):
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def a_ ( self : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = FlaxRegNetModel(config=UpperCamelCase_)
__UpperCAmelCase : Dict = model(UpperCamelCase_)
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def a_ ( self : Any , UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.num_labels
__UpperCAmelCase : Tuple = FlaxRegNetForImageClassification(config=UpperCamelCase_)
__UpperCAmelCase : str = model(UpperCamelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Any = self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase : Tuple = config_and_inputs
__UpperCAmelCase : Optional[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
lowercase_ = False
lowercase_ = False
lowercase_ = False
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = FlaxRegNetModelTester(self)
__UpperCAmelCase : str = ConfigTester(self , config_class=UpperCamelCase_ , has_text_modality=UpperCamelCase_)
def a_ ( self : Dict):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def a_ ( self : Tuple):
"""simple docstring"""
return
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_)
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_)
@unittest.skip(reason="RegNet does not use inputs_embeds")
def a_ ( self : Union[str, Any]):
"""simple docstring"""
pass
@unittest.skip(reason="RegNet does not support input and output embeddings")
def a_ ( self : Optional[int]):
"""simple docstring"""
pass
def a_ ( self : str):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : int = model_class(UpperCamelCase_)
__UpperCAmelCase : Optional[int] = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase : Any = [*signature.parameters.keys()]
__UpperCAmelCase : Dict = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCamelCase_)
def a_ ( self : int):
"""simple docstring"""
def check_hidden_states_output(UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Union[str, Any]):
__UpperCAmelCase : Union[str, Any] = model_class(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_))
__UpperCAmelCase : List[str] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__UpperCAmelCase : str = self.model_tester.num_stages
self.assertEqual(len(UpperCamelCase_) , expected_num_stages + 1)
__UpperCAmelCase , __UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : List[str] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase : Optional[int] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
def a_ ( self : Tuple):
"""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 : List[Any] = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Optional[int] = model_class(UpperCamelCase_)
@jax.jit
def model_jitted(UpperCamelCase_ : int , **UpperCamelCase_ : Optional[int]):
return model(pixel_values=UpperCamelCase_ , **UpperCamelCase_)
with self.subTest("JIT Enabled"):
__UpperCAmelCase : Optional[Any] = model_jitted(**UpperCamelCase_).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
__UpperCAmelCase : Dict = model_jitted(**UpperCamelCase_).to_tuple()
self.assertEqual(len(UpperCamelCase_) , len(UpperCamelCase_))
for jitted_output, output in zip(UpperCamelCase_ , UpperCamelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCamelCase ( ) -> Any:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_flax
class a__ ( unittest.TestCase ):
@cached_property
def a_ ( self : Optional[int]):
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/regnet-y-040") if is_vision_available() else None
@slow
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Any = FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")
__UpperCAmelCase : Dict = self.default_image_processor
__UpperCAmelCase : str = prepare_img()
__UpperCAmelCase : int = image_processor(images=UpperCamelCase_ , return_tensors="np")
__UpperCAmelCase : Dict = model(**UpperCamelCase_)
# verify the logits
__UpperCAmelCase : Dict = (1, 1000)
self.assertEqual(outputs.logits.shape , UpperCamelCase_)
__UpperCAmelCase : Any = jnp.array([-0.4180, -1.5051, -3.4836])
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , UpperCamelCase_ , atol=1e-4))
| 77 | 1 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[str]:
"""simple docstring"""
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
__UpperCAmelCase : Optional[Any] = TapasConfig.from_json_file(UpperCamelCase )
# set absolute/relative position embeddings parameter
__UpperCAmelCase : Optional[Any] = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
__UpperCAmelCase : List[str] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "WTQ":
# run_task_main.py hparams
__UpperCAmelCase : Tuple = 4
__UpperCAmelCase : Any = True
# hparam_utils.py hparams
__UpperCAmelCase : Union[str, Any] = 0.664694
__UpperCAmelCase : Union[str, Any] = 0.207951
__UpperCAmelCase : int = 0.121194
__UpperCAmelCase : Optional[int] = True
__UpperCAmelCase : List[str] = True
__UpperCAmelCase : Union[str, Any] = False
__UpperCAmelCase : List[str] = 0.0352513
__UpperCAmelCase : Optional[int] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
__UpperCAmelCase : int = 4
__UpperCAmelCase : Optional[int] = False
# hparam_utils.py hparams
__UpperCAmelCase : int = 36.4519
__UpperCAmelCase : str = 0.903421
__UpperCAmelCase : Dict = 222.088
__UpperCAmelCase : Dict = True
__UpperCAmelCase : Union[str, Any] = True
__UpperCAmelCase : Tuple = True
__UpperCAmelCase : Any = 0.763141
__UpperCAmelCase : Optional[Any] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "TABFACT":
__UpperCAmelCase : Union[str, Any] = TapasForSequenceClassification(config=UpperCamelCase )
elif task == "MLM":
__UpperCAmelCase : Tuple = TapasForMaskedLM(config=UpperCamelCase )
elif task == "INTERMEDIATE_PRETRAINING":
__UpperCAmelCase : List[str] = TapasModel(config=UpperCamelCase )
else:
raise ValueError(f"Task {task} not supported." )
print(f"Building PyTorch model from configuration: {config}" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(UpperCamelCase , UpperCamelCase , UpperCamelCase )
# Save pytorch-model (weights and configuration)
print(f"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(UpperCamelCase )
# Save tokenizer files
print(f"Save tokenizer files to {pytorch_dump_path}" )
__UpperCAmelCase : str = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt" , model_max_length=512 )
tokenizer.save_pretrained(UpperCamelCase )
print("Used relative position embeddings:" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
)
parser.add_argument(
"""--reset_position_index_per_cell""",
default=False,
action="""store_true""",
help="""Whether to use relative position embeddings or not. Defaults to True.""",
)
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--tapas_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained TAPAS model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 77 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
A = """
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results['spearmanr'])
-0.7
>>> print(round(results['spearmanr_pvalue'], 2))
0.19
"""
A = r"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
def a_ ( self : Any):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("float"),
"references": datasets.Value("float"),
}) , reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"] , )
def a_ ( self : Union[str, Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : int=False):
"""simple docstring"""
__UpperCAmelCase : List[str] = spearmanr(UpperCamelCase_ , UpperCamelCase_)
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 77 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
from .pipeline_kandinsky import KandinskyPipeline
from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
from .text_encoder import MultilingualCLIP
| 77 |
"""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""": """spiece.model"""}
A = {
"""vocab_file""": {
"""bert_for_seq_generation""": (
"""https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
),
}
}
A = {"""bert_for_seq_generation""": 512}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = []
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : List[str] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : List[str]="<s>" , UpperCamelCase_ : Optional[Any]="</s>" , UpperCamelCase_ : Optional[int]="<unk>" , UpperCamelCase_ : int="<pad>" , UpperCamelCase_ : List[Any]="<::::>" , UpperCamelCase_ : Optional[Dict[str, Any]] = None , **UpperCamelCase_ : List[Any] , ):
"""simple docstring"""
__UpperCAmelCase : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase_ , )
__UpperCAmelCase : Dict = vocab_file
__UpperCAmelCase : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(UpperCamelCase_)
@property
def a_ ( self : List[str]):
"""simple docstring"""
return self.sp_model.get_piece_size()
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : int = {self.convert_ids_to_tokens(UpperCamelCase_): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self : int):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = self.__dict__.copy()
__UpperCAmelCase : List[Any] = None
return state
def __setstate__( self : Optional[Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs"):
__UpperCAmelCase : List[Any] = {}
__UpperCAmelCase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def a_ ( self : Any , UpperCamelCase_ : str):
"""simple docstring"""
return self.sp_model.encode(UpperCamelCase_ , out_type=UpperCamelCase_)
def a_ ( self : Optional[Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
return self.sp_model.piece_to_id(UpperCamelCase_)
def a_ ( self : Tuple , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : int = self.sp_model.IdToPiece(UpperCamelCase_)
return token
def a_ ( self : Dict , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : int = []
__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(UpperCamelCase_) + token
__UpperCAmelCase : List[Any] = []
else:
current_sub_tokens.append(UpperCamelCase_)
out_string += self.sp_model.decode(UpperCamelCase_)
return out_string.strip()
def a_ ( self : List[str] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : Tuple = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , UpperCamelCase_)
elif not os.path.isfile(self.vocab_file):
with open(UpperCamelCase_ , "wb") as fi:
__UpperCAmelCase : List[str] = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase_)
return (out_vocab_file,)
| 77 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
A = logging.get_logger(__name__)
A = {"""vocab_file""": """vocab.txt"""}
A = {
"""vocab_file""": {
"""YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt""",
"""YituTech/conv-bert-medium-small""": (
"""https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"""
),
"""YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt""",
}
}
A = {
"""YituTech/conv-bert-base""": 512,
"""YituTech/conv-bert-medium-small""": 512,
"""YituTech/conv-bert-small""": 512,
}
A = {
"""YituTech/conv-bert-base""": {"""do_lower_case""": True},
"""YituTech/conv-bert-medium-small""": {"""do_lower_case""": True},
"""YituTech/conv-bert-small""": {"""do_lower_case""": True},
}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_INIT_CONFIGURATION
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = ConvBertTokenizer
def __init__( self : Union[str, Any] , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : Tuple=None , UpperCamelCase_ : Optional[Any]=True , UpperCamelCase_ : Optional[int]="[UNK]" , UpperCamelCase_ : str="[SEP]" , UpperCamelCase_ : Dict="[PAD]" , UpperCamelCase_ : Tuple="[CLS]" , UpperCamelCase_ : Dict="[MASK]" , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Optional[Any]=None , **UpperCamelCase_ : Any , ):
"""simple docstring"""
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , do_lower_case=UpperCamelCase_ , unk_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , cls_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , tokenize_chinese_chars=UpperCamelCase_ , strip_accents=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : List[str] = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get("lowercase" , UpperCamelCase_) != do_lower_case
or normalizer_state.get("strip_accents" , UpperCamelCase_) != strip_accents
or normalizer_state.get("handle_chinese_chars" , UpperCamelCase_) != tokenize_chinese_chars
):
__UpperCAmelCase : Tuple = getattr(UpperCamelCase_ , normalizer_state.pop("type"))
__UpperCAmelCase : int = do_lower_case
__UpperCAmelCase : Any = strip_accents
__UpperCAmelCase : List[str] = tokenize_chinese_chars
__UpperCAmelCase : Any = normalizer_class(**UpperCamelCase_)
__UpperCAmelCase : List[str] = do_lower_case
def a_ ( self : Optional[int] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[int]=None):
"""simple docstring"""
__UpperCAmelCase : List[str] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def a_ ( self : int , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : List[str] = [self.sep_token_id]
__UpperCAmelCase : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]
def a_ ( self : Tuple , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self._tokenizer.model.save(UpperCamelCase_ , name=UpperCamelCase_)
return tuple(UpperCamelCase_)
| 77 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
A = """true"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=82 , UpperCamelCase=16 ) -> Tuple:
"""simple docstring"""
set_seed(42 )
__UpperCAmelCase : Dict = RegressionModel()
__UpperCAmelCase : Optional[Any] = deepcopy(UpperCamelCase )
__UpperCAmelCase : Any = RegressionDataset(length=UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = DataLoader(UpperCamelCase , batch_size=UpperCamelCase )
model.to(accelerator.device )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = accelerator.prepare(UpperCamelCase , UpperCamelCase )
return model, ddp_model, dataloader
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=False ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : List[str] = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
__UpperCAmelCase : Dict = load_dataset("glue" , "mrpc" , split="validation" )
def tokenize_function(UpperCamelCase ):
__UpperCAmelCase : Dict = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=UpperCamelCase , max_length=UpperCamelCase )
return outputs
with accelerator.main_process_first():
__UpperCAmelCase : str = dataset.map(
UpperCamelCase , batched=UpperCamelCase , remove_columns=["idx", "sentence1", "sentence2"] , )
__UpperCAmelCase : List[Any] = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(UpperCamelCase ):
if use_longest:
return tokenizer.pad(UpperCamelCase , padding="longest" , return_tensors="pt" )
return tokenizer.pad(UpperCamelCase , padding="max_length" , max_length=128 , return_tensors="pt" )
return DataLoader(UpperCamelCase , shuffle=UpperCamelCase , collate_fn=UpperCamelCase , batch_size=16 )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : List[Any] = Accelerator(dispatch_batches=UpperCamelCase , split_batches=UpperCamelCase )
__UpperCAmelCase : int = get_dataloader(UpperCamelCase , not dispatch_batches )
__UpperCAmelCase : Any = AutoModelForSequenceClassification.from_pretrained(
"hf-internal-testing/mrpc-bert-base-cased" , return_dict=UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Dict = accelerator.prepare(UpperCamelCase , UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase : Dict = []
for batch in dataloader:
__UpperCAmelCase , __UpperCAmelCase : int = batch.values()
with torch.no_grad():
__UpperCAmelCase : int = model(UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : List[str] = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = [], []
for logit, targ in logits_and_targets:
logits.append(UpperCamelCase )
targs.append(UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = torch.cat(UpperCamelCase ), torch.cat(UpperCamelCase )
return logits, targs
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=82 , UpperCamelCase=False , UpperCamelCase=False , UpperCamelCase=16 ) -> int:
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = get_basic_setup(UpperCamelCase , UpperCamelCase , UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = generate_predictions(UpperCamelCase , UpperCamelCase , UpperCamelCase )
assert (
len(UpperCamelCase ) == num_samples
), f"Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(UpperCamelCase )}"
def _UpperCamelCase ( UpperCamelCase = False , UpperCamelCase = False ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : List[str] = evaluate.load("glue" , "mrpc" )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = get_mrpc_setup(UpperCamelCase , UpperCamelCase )
# First do baseline
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Tuple = setup["no"]
model.to(UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(UpperCamelCase )
with torch.inference_mode():
__UpperCAmelCase : List[str] = model(**UpperCamelCase )
__UpperCAmelCase : str = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=UpperCamelCase , references=batch["labels"] )
__UpperCAmelCase : str = metric.compute()
# Then do distributed
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
__UpperCAmelCase : Any = model(**UpperCamelCase )
__UpperCAmelCase : str = outputs.logits.argmax(dim=-1 )
__UpperCAmelCase : Union[str, Any] = batch["labels"]
__UpperCAmelCase , __UpperCAmelCase : Any = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=UpperCamelCase , references=UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"
def _UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
__UpperCAmelCase : Dict = Accelerator(split_batches=UpperCamelCase , dispatch_batches=UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("**Testing gather_for_metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`" )
test_mrpc(UpperCamelCase , UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test torch metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__UpperCAmelCase : Union[str, Any] = Accelerator(split_batches=UpperCamelCase , dispatch_batches=UpperCamelCase )
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99" )
test_torch_metrics(UpperCamelCase , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test last batch is not dropped when perfectly divisible**" )
__UpperCAmelCase : Any = Accelerator()
test_torch_metrics(UpperCamelCase , 512 )
accelerator.state._reset_state()
def _UpperCamelCase ( UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 77 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
A = logging.get_logger(__name__)
A = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
A = {
"""vocab_file""": {
"""bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt""",
"""bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt""",
"""bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/vocab.txt""",
"""bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/vocab.txt""",
"""bert-base-multilingual-uncased""": (
"""https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"""
),
"""bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt""",
"""bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt""",
"""bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt""",
"""bert-large-uncased-whole-word-masking""": (
"""https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"""
),
"""bert-large-cased-whole-word-masking""": (
"""https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"""
),
"""bert-large-uncased-whole-word-masking-finetuned-squad""": (
"""https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"""
),
"""bert-large-cased-whole-word-masking-finetuned-squad""": (
"""https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"""
),
"""bert-base-cased-finetuned-mrpc""": (
"""https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"""
),
"""bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt""",
"""bert-base-german-dbmdz-uncased""": (
"""https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"""
),
"""TurkuNLP/bert-base-finnish-cased-v1""": (
"""https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"""
),
"""TurkuNLP/bert-base-finnish-uncased-v1""": (
"""https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"""
),
"""wietsedv/bert-base-dutch-cased""": (
"""https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json""",
"""bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json""",
"""bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json""",
"""bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json""",
"""bert-base-multilingual-uncased""": (
"""https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"""
),
"""bert-base-multilingual-cased""": (
"""https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"""
),
"""bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json""",
"""bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json""",
"""bert-large-uncased-whole-word-masking""": (
"""https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"""
),
"""bert-large-cased-whole-word-masking""": (
"""https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"""
),
"""bert-large-uncased-whole-word-masking-finetuned-squad""": (
"""https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"""
),
"""bert-large-cased-whole-word-masking-finetuned-squad""": (
"""https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"""
),
"""bert-base-cased-finetuned-mrpc""": (
"""https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"""
),
"""bert-base-german-dbmdz-cased""": (
"""https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"""
),
"""bert-base-german-dbmdz-uncased""": (
"""https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"""
),
"""TurkuNLP/bert-base-finnish-cased-v1""": (
"""https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"""
),
"""TurkuNLP/bert-base-finnish-uncased-v1""": (
"""https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"""
),
"""wietsedv/bert-base-dutch-cased""": (
"""https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"""
),
},
}
A = {
"""bert-base-uncased""": 512,
"""bert-large-uncased""": 512,
"""bert-base-cased""": 512,
"""bert-large-cased""": 512,
"""bert-base-multilingual-uncased""": 512,
"""bert-base-multilingual-cased""": 512,
"""bert-base-chinese""": 512,
"""bert-base-german-cased""": 512,
"""bert-large-uncased-whole-word-masking""": 512,
"""bert-large-cased-whole-word-masking""": 512,
"""bert-large-uncased-whole-word-masking-finetuned-squad""": 512,
"""bert-large-cased-whole-word-masking-finetuned-squad""": 512,
"""bert-base-cased-finetuned-mrpc""": 512,
"""bert-base-german-dbmdz-cased""": 512,
"""bert-base-german-dbmdz-uncased""": 512,
"""TurkuNLP/bert-base-finnish-cased-v1""": 512,
"""TurkuNLP/bert-base-finnish-uncased-v1""": 512,
"""wietsedv/bert-base-dutch-cased""": 512,
}
A = {
"""bert-base-uncased""": {"""do_lower_case""": True},
"""bert-large-uncased""": {"""do_lower_case""": True},
"""bert-base-cased""": {"""do_lower_case""": False},
"""bert-large-cased""": {"""do_lower_case""": False},
"""bert-base-multilingual-uncased""": {"""do_lower_case""": True},
"""bert-base-multilingual-cased""": {"""do_lower_case""": False},
"""bert-base-chinese""": {"""do_lower_case""": False},
"""bert-base-german-cased""": {"""do_lower_case""": False},
"""bert-large-uncased-whole-word-masking""": {"""do_lower_case""": True},
"""bert-large-cased-whole-word-masking""": {"""do_lower_case""": False},
"""bert-large-uncased-whole-word-masking-finetuned-squad""": {"""do_lower_case""": True},
"""bert-large-cased-whole-word-masking-finetuned-squad""": {"""do_lower_case""": False},
"""bert-base-cased-finetuned-mrpc""": {"""do_lower_case""": False},
"""bert-base-german-dbmdz-cased""": {"""do_lower_case""": False},
"""bert-base-german-dbmdz-uncased""": {"""do_lower_case""": True},
"""TurkuNLP/bert-base-finnish-cased-v1""": {"""do_lower_case""": False},
"""TurkuNLP/bert-base-finnish-uncased-v1""": {"""do_lower_case""": True},
"""wietsedv/bert-base-dutch-cased""": {"""do_lower_case""": False},
}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_INIT_CONFIGURATION
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = BertTokenizer
def __init__( self : Any , UpperCamelCase_ : List[Any]=None , UpperCamelCase_ : List[str]=None , UpperCamelCase_ : Tuple=True , UpperCamelCase_ : Union[str, Any]="[UNK]" , UpperCamelCase_ : Union[str, Any]="[SEP]" , UpperCamelCase_ : List[Any]="[PAD]" , UpperCamelCase_ : List[Any]="[CLS]" , UpperCamelCase_ : Tuple="[MASK]" , UpperCamelCase_ : Union[str, Any]=True , UpperCamelCase_ : Optional[int]=None , **UpperCamelCase_ : Optional[int] , ):
"""simple docstring"""
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , do_lower_case=UpperCamelCase_ , unk_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , cls_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , tokenize_chinese_chars=UpperCamelCase_ , strip_accents=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Union[str, Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get("lowercase" , UpperCamelCase_) != do_lower_case
or normalizer_state.get("strip_accents" , UpperCamelCase_) != strip_accents
or normalizer_state.get("handle_chinese_chars" , UpperCamelCase_) != tokenize_chinese_chars
):
__UpperCAmelCase : Dict = getattr(UpperCamelCase_ , normalizer_state.pop("type"))
__UpperCAmelCase : Any = do_lower_case
__UpperCAmelCase : List[str] = strip_accents
__UpperCAmelCase : Any = tokenize_chinese_chars
__UpperCAmelCase : Union[str, Any] = normalizer_class(**UpperCamelCase_)
__UpperCAmelCase : List[Any] = do_lower_case
def a_ ( self : Optional[int] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[Any]=None):
"""simple docstring"""
__UpperCAmelCase : int = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def a_ ( self : Any , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = [self.sep_token_id]
__UpperCAmelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]
def a_ ( self : str , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
__UpperCAmelCase : int = self._tokenizer.model.save(UpperCamelCase_ , name=UpperCamelCase_)
return tuple(UpperCamelCase_)
| 77 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in range(UpperCamelCase , UpperCamelCase ):
__UpperCAmelCase : List[Any] = i
__UpperCAmelCase : Any = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
__UpperCAmelCase : Dict = array[temp_index - 1]
temp_index -= 1
__UpperCAmelCase : str = temp_index_value
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> None: # Max Heap
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = index
__UpperCAmelCase : List[str] = 2 * index + 1 # Left Node
__UpperCAmelCase : Union[str, Any] = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
__UpperCAmelCase : Tuple = left_index
if right_index < heap_size and array[largest] < array[right_index]:
__UpperCAmelCase : int = right_index
if largest != index:
__UpperCAmelCase , __UpperCAmelCase : List[str] = array[largest], array[index]
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
__UpperCAmelCase : List[Any] = len(UpperCamelCase )
for i in range(n // 2 , -1 , -1 ):
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
for i in range(n - 1 , 0 , -1 ):
__UpperCAmelCase , __UpperCAmelCase : int = array[0], array[i]
heapify(UpperCamelCase , 0 , UpperCamelCase )
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = low
__UpperCAmelCase : List[str] = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = array[j], array[i]
i += 1
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
if len(UpperCamelCase ) == 0:
return array
__UpperCAmelCase : Optional[int] = 2 * math.ceil(math.loga(len(UpperCamelCase ) ) )
__UpperCAmelCase : List[Any] = 16
return intro_sort(UpperCamelCase , 0 , len(UpperCamelCase ) , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> list:
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(UpperCamelCase )
max_depth -= 1
__UpperCAmelCase : List[Any] = median_of_a(UpperCamelCase , UpperCamelCase , start + ((end - start) // 2) + 1 , end - 1 )
__UpperCAmelCase : Union[str, Any] = partition(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
intro_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Optional[Any] = p
return insertion_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
A = input("""Enter numbers separated by a comma : """).strip()
A = [float(item) for item in user_input.split(""",""")]
print(sort(unsorted))
| 77 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class a__ ( unittest.TestCase ):
def a_ ( self : Tuple , UpperCamelCase_ : int , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = jnp.ones((batch_size, length)) / length
return scores
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = None
__UpperCAmelCase : int = 20
__UpperCAmelCase : int = self._get_uniform_logits(batch_size=2 , length=UpperCamelCase_)
# tweak scores to not be uniform anymore
__UpperCAmelCase : List[Any] = scores.at[1, 5].set((1 / length) + 0.1) # peak, 1st batch
__UpperCAmelCase : int = scores.at[1, 10].set((1 / length) - 0.4) # valley, 1st batch
# compute softmax
__UpperCAmelCase : List[str] = jax.nn.softmax(UpperCamelCase_ , axis=-1)
__UpperCAmelCase : str = FlaxTemperatureLogitsWarper(temperature=0.5)
__UpperCAmelCase : Tuple = FlaxTemperatureLogitsWarper(temperature=1.3)
__UpperCAmelCase : Union[str, Any] = jax.nn.softmax(temp_dist_warper_sharper(UpperCamelCase_ , scores.copy() , cur_len=UpperCamelCase_) , axis=-1)
__UpperCAmelCase : List[Any] = jax.nn.softmax(temp_dist_warper_smoother(UpperCamelCase_ , scores.copy() , cur_len=UpperCamelCase_) , axis=-1)
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1e-3))
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1e-3))
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max())
self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min())
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max())
self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min())
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : int = None
__UpperCAmelCase : Tuple = 10
__UpperCAmelCase : Any = 2
# create ramp distribution
__UpperCAmelCase : Tuple = np.broadcast_to(np.arange(UpperCamelCase_)[None, :] , (batch_size, vocab_size)).copy()
__UpperCAmelCase : Dict = ramp_logits[1:, : vocab_size // 2] + vocab_size
__UpperCAmelCase : str = FlaxTopKLogitsWarper(3)
__UpperCAmelCase : Union[str, Any] = top_k_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0]).tolist() , 7 * [True] + 3 * [False])
self.assertListEqual(jnp.isinf(scores[1]).tolist() , 2 * [True] + 3 * [False] + 5 * [True])
# check special case
__UpperCAmelCase : Tuple = 5
__UpperCAmelCase : List[Any] = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3)
__UpperCAmelCase : str = np.broadcast_to(np.arange(UpperCamelCase_)[None, :] , (batch_size, length)).copy()
__UpperCAmelCase : str = top_k_warp_safety_check(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1).tolist() , [2, 2])
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Any = None
__UpperCAmelCase : Optional[int] = 10
__UpperCAmelCase : Dict = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
__UpperCAmelCase : List[Any] = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))
__UpperCAmelCase : Optional[Any] = FlaxTopPLogitsWarper(0.8)
__UpperCAmelCase : int = np.exp(top_p_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_))
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
__UpperCAmelCase : Optional[Any] = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
self.assertTrue(np.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1e-3))
# check edge cases with negative and extreme logits
__UpperCAmelCase : Optional[Any] = np.broadcast_to(np.arange(UpperCamelCase_)[None, :] , (batch_size, vocab_size)).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
__UpperCAmelCase : int = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
__UpperCAmelCase : Optional[int] = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0)
__UpperCAmelCase : int = top_p_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1).tolist() , [3, 2])
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : int = 20
__UpperCAmelCase : Optional[Any] = 4
__UpperCAmelCase : Union[str, Any] = 0
__UpperCAmelCase : int = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=UpperCamelCase_)
# check that min length is applied at length 5
__UpperCAmelCase : List[Any] = ids_tensor((batch_size, 20) , vocab_size=20)
__UpperCAmelCase : int = 5
__UpperCAmelCase : Any = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : int = min_dist_processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float("inf")])
# check that min length is not applied anymore at length 15
__UpperCAmelCase : List[str] = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Dict = 15
__UpperCAmelCase : Optional[int] = min_dist_processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
self.assertFalse(jnp.isinf(UpperCamelCase_).any())
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = 20
__UpperCAmelCase : Optional[int] = 4
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Any = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase_)
# check that all scores are -inf except the bos_token_id score
__UpperCAmelCase : Optional[Any] = ids_tensor((batch_size, 1) , vocab_size=20)
__UpperCAmelCase : Union[str, Any] = 1
__UpperCAmelCase : List[Any] = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : List[str] = logits_processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]).all())
self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0]) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
__UpperCAmelCase : List[Any] = 3
__UpperCAmelCase : str = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : List[str] = logits_processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
self.assertFalse(jnp.isinf(UpperCamelCase_).any())
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : List[Any] = 20
__UpperCAmelCase : List[str] = 4
__UpperCAmelCase : Optional[Any] = 0
__UpperCAmelCase : List[Any] = 5
__UpperCAmelCase : Optional[Any] = FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase_ , eos_token_id=UpperCamelCase_)
# check that all scores are -inf except the eos_token_id when max_length is reached
__UpperCAmelCase : Optional[Any] = ids_tensor((batch_size, 4) , vocab_size=20)
__UpperCAmelCase : Optional[int] = 4
__UpperCAmelCase : int = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : int = logits_processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]).all())
self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0]) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
__UpperCAmelCase : Dict = 3
__UpperCAmelCase : Optional[Any] = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Dict = logits_processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
self.assertFalse(jnp.isinf(UpperCamelCase_).any())
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : int = 4
__UpperCAmelCase : Optional[Any] = 10
__UpperCAmelCase : Any = 15
__UpperCAmelCase : List[str] = 2
__UpperCAmelCase : Union[str, Any] = 1
__UpperCAmelCase : Tuple = 15
# dummy input_ids and scores
__UpperCAmelCase : Dict = ids_tensor((batch_size, sequence_length) , UpperCamelCase_)
__UpperCAmelCase : Tuple = input_ids.copy()
__UpperCAmelCase : Dict = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = scores.copy()
# instantiate all dist processors
__UpperCAmelCase : Union[str, Any] = FlaxTemperatureLogitsWarper(temperature=0.5)
__UpperCAmelCase : List[str] = FlaxTopKLogitsWarper(3)
__UpperCAmelCase : str = FlaxTopPLogitsWarper(0.8)
# instantiate all logits processors
__UpperCAmelCase : List[Any] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=UpperCamelCase_)
__UpperCAmelCase : str = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase_)
__UpperCAmelCase : List[Any] = FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase_ , eos_token_id=UpperCamelCase_)
__UpperCAmelCase : Any = 10
# no processor list
__UpperCAmelCase : List[str] = temp_dist_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = top_k_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = top_p_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
__UpperCAmelCase : int = min_dist_proc(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = bos_dist_proc(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = eos_dist_proc(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
# with processor list
__UpperCAmelCase : str = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
__UpperCAmelCase : Optional[int] = processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
# scores should be equal
self.assertTrue(jnp.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1e-3))
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist())
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Tuple = 4
__UpperCAmelCase : str = 10
__UpperCAmelCase : Optional[int] = 15
__UpperCAmelCase : int = 2
__UpperCAmelCase : List[str] = 1
__UpperCAmelCase : Dict = 15
# dummy input_ids and scores
__UpperCAmelCase : Any = ids_tensor((batch_size, sequence_length) , UpperCamelCase_)
__UpperCAmelCase : Tuple = input_ids.copy()
__UpperCAmelCase : str = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : int = scores.copy()
# instantiate all dist processors
__UpperCAmelCase : Union[str, Any] = FlaxTemperatureLogitsWarper(temperature=0.5)
__UpperCAmelCase : Optional[int] = FlaxTopKLogitsWarper(3)
__UpperCAmelCase : str = FlaxTopPLogitsWarper(0.8)
# instantiate all logits processors
__UpperCAmelCase : List[str] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase_)
__UpperCAmelCase : Tuple = FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase_ , eos_token_id=UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = 10
# no processor list
def run_no_processor_list(UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Dict):
__UpperCAmelCase : Any = temp_dist_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
__UpperCAmelCase : List[str] = top_k_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
__UpperCAmelCase : int = top_p_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
__UpperCAmelCase : int = min_dist_proc(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
__UpperCAmelCase : int = bos_dist_proc(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
__UpperCAmelCase : Any = eos_dist_proc(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
return scores
# with processor list
def run_processor_list(UpperCamelCase_ : Dict , UpperCamelCase_ : List[str] , UpperCamelCase_ : Dict):
__UpperCAmelCase : List[Any] = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
__UpperCAmelCase : Optional[Any] = processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_)
return scores
__UpperCAmelCase : str = jax.jit(UpperCamelCase_)
__UpperCAmelCase : List[str] = jax.jit(UpperCamelCase_)
__UpperCAmelCase : List[Any] = jitted_run_no_processor_list(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Dict = jitted_run_processor_list(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
# scores should be equal
self.assertTrue(jnp.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1e-3))
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist())
| 77 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
__UpperCAmelCase : Any = 0
__UpperCAmelCase : Dict = 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Tuple = 0
# compute the shape of the output matrix
__UpperCAmelCase : Optional[int] = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
__UpperCAmelCase : List[str] = np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
__UpperCAmelCase : str = np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
__UpperCAmelCase : int = 0
__UpperCAmelCase : int = 0
return updated_arr
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : List[str] = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Any = 0
# compute the shape of the output matrix
__UpperCAmelCase : Tuple = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
__UpperCAmelCase : str = np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
__UpperCAmelCase : Tuple = int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Optional[Any] = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name="""avgpooling""", verbose=True)
# Loading the image
A = Image.open("""path_to_image""")
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 77 | 1 |
"""simple docstring"""
A = 0 # The first color of the flag.
A = 1 # The second color of the flag.
A = 2 # The third color of the flag.
A = (red, white, blue)
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
if not sequence:
return []
if len(UpperCamelCase ) == 1:
return list(UpperCamelCase )
__UpperCAmelCase : int = 0
__UpperCAmelCase : str = len(UpperCamelCase ) - 1
__UpperCAmelCase : Optional[Any] = 0
while mid <= high:
if sequence[mid] == colors[0]:
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
__UpperCAmelCase , __UpperCAmelCase : List[str] = sequence[high], sequence[mid]
high -= 1
else:
__UpperCAmelCase : Optional[int] = f"The elements inside the sequence must contains only {colors} values"
raise ValueError(UpperCamelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
A = input("""Enter numbers separated by commas:\n""").strip()
A = [int(item.strip()) for item in user_input.split(""",""")]
print(f'''{dutch_national_flag_sort(unsorted)}''')
| 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A = None
A = logging.get_logger(__name__)
A = """▁"""
A = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
A = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
A = {
"""google/pegasus-xsum""": 512,
}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = PegasusTokenizer
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : str , UpperCamelCase_ : str=None , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : int="<pad>" , UpperCamelCase_ : Optional[Any]="</s>" , UpperCamelCase_ : Any="<unk>" , UpperCamelCase_ : Tuple="<mask_2>" , UpperCamelCase_ : Any="<mask_1>" , UpperCamelCase_ : Tuple=None , UpperCamelCase_ : str=103 , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = offset
if additional_special_tokens is not None:
if not isinstance(UpperCamelCase_ , UpperCamelCase_):
raise TypeError(
F"additional_special_tokens should be of type {type(UpperCamelCase_)}, but is"
F" {type(UpperCamelCase_)}")
__UpperCAmelCase : Any = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F"<unk_{i}>" for i in range(len(UpperCamelCase_) , self.offset - 1)
]
if len(set(UpperCamelCase_)) != len(UpperCamelCase_):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.")
__UpperCAmelCase : str = additional_special_tokens_extended
else:
__UpperCAmelCase : Tuple = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F"<unk_{i}>" for i in range(2 , self.offset)]
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , pad_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , mask_token_sent=UpperCamelCase_ , offset=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Optional[int] = vocab_file
__UpperCAmelCase : List[str] = False if not self.vocab_file else True
def a_ ( self : Union[str, Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : int = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
F" {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}")
return [1 if x in all_special_ids else 0 for x in seq]
def a_ ( self : Union[str, Any] , UpperCamelCase_ : List , UpperCamelCase_ : Optional[List] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(UpperCamelCase_)
elif token_ids_a is None:
return self._special_token_mask(UpperCamelCase_) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a) + [1]
def a_ ( self : int , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[Any]=None):
"""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 a_ ( self : Union[str, Any] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer.")
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : List[str] = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_):
copyfile(self.vocab_file , UpperCamelCase_)
return (out_vocab_file,)
| 77 | 1 |
"""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 ):
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : int = tf.convert_to_tensor(
[
[
8.2220991, # 3rd highest value; idx. 0
-0.5620044,
5.23229752,
4.0386393,
-6.8798378,
-0.54785802,
-3.2012153,
2.92777176,
1.88171953,
7.35341276, # 5th highest value; idx. 9
8.43207833, # 2nd highest value; idx. 10
-9.85711836,
-5.96209236,
-1.13039161,
-7.1115294,
-0.8369633,
-5.3186408,
7.06427407,
0.81369344,
-0.82023817,
-5.9179796,
0.58813443,
-6.99778438,
4.71551189,
-0.18771637,
7.44020759, # 4th highest value; idx. 25
9.38450987, # 1st highest value; idx. 26
2.12662941,
-9.32562038,
2.35652522,
], # cummulative prob of 5 highest values <= 0.6
[
0.58425518,
4.53139238,
-5.57510464,
-6.28030699,
-7.19529503,
-4.02122551,
1.39337037,
-6.06707057,
1.59480517,
-9.643119,
0.03907799,
0.67231762,
-8.88206726,
6.27115922, # 4th highest value; idx. 13
2.28520723,
4.82767506,
4.30421368,
8.8275313, # 2nd highest value; idx. 17
5.44029958, # 5th highest value; idx. 18
-4.4735794,
7.38579536, # 3rd highest value; idx. 20
-2.91051663,
2.61946077,
-2.5674762,
-9.48959302,
-4.02922645,
-1.35416918,
9.67702323, # 1st highest value; idx. 27
-5.89478553,
1.85370467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
__UpperCAmelCase : Tuple = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
__UpperCAmelCase : List[str] = tf.convert_to_tensor(
[8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023] , dtype=tf.floataa , ) # expected non filtered values as noted above
__UpperCAmelCase : Any = tf_top_k_top_p_filtering(UpperCamelCase_ , top_k=10 , top_p=0.6 , min_tokens_to_keep=4)
__UpperCAmelCase : List[Any] = output[output != -float("inf")]
__UpperCAmelCase : Tuple = tf.cast(
tf.where(tf.not_equal(UpperCamelCase_ , tf.constant(-float("inf") , dtype=tf.floataa))) , dtype=tf.intaa , )
tf.debugging.assert_near(UpperCamelCase_ , UpperCamelCase_ , rtol=1e-12)
tf.debugging.assert_equal(UpperCamelCase_ , UpperCamelCase_)
@require_tf
class a__ ( unittest.TestCase , __magic_name__ ):
# setting framework_dependent_parameters needs to be gated, just like its contents' imports
if is_tf_available():
lowercase_ = {
"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 a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : int = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
__UpperCAmelCase : str = 2
__UpperCAmelCase : int = 2
class a__ ( tf.Module ):
def __init__( self : List[Any] , UpperCamelCase_ : Tuple):
"""simple docstring"""
super(UpperCamelCase_ , self).__init__()
__UpperCAmelCase : Optional[Any] = 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=UpperCamelCase_ , )
def a_ ( self : Tuple , UpperCamelCase_ : Any , UpperCamelCase_ : List[str]):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = self.model.generate(
input_ids=UpperCamelCase_ , attention_mask=UpperCamelCase_ , max_new_tokens=UpperCamelCase_ , return_dict_in_generate=UpperCamelCase_ , )
return {"sequences": outputs["sequences"]}
__UpperCAmelCase : str = [[2, 0], [102, 103]]
__UpperCAmelCase : str = [[1, 0], [1, 1]]
__UpperCAmelCase : str = DummyModel(model=UpperCamelCase_)
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(UpperCamelCase_ , UpperCamelCase_ , signatures={"serving_default": dummy_model.serving})
__UpperCAmelCase : List[str] = tf.saved_model.load(UpperCamelCase_).signatures["serving_default"]
for batch_size in range(1 , len(UpperCamelCase_) + 1):
__UpperCAmelCase : Optional[int] = {
"input_ids": tf.constant(dummy_input_ids[:batch_size]),
"attention_mask": tf.constant(dummy_attention_masks[:batch_size]),
}
__UpperCAmelCase : str = serving_func(**UpperCamelCase_)["sequences"]
__UpperCAmelCase : str = test_model.generate(**UpperCamelCase_ , max_new_tokens=UpperCamelCase_)
tf.debugging.assert_equal(UpperCamelCase_ , UpperCamelCase_)
@slow
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
__UpperCAmelCase : List[str] = 1
__UpperCAmelCase : List[Any] = 2
class a__ ( tf.Module ):
def __init__( self : Union[str, Any] , UpperCamelCase_ : Any):
"""simple docstring"""
super(UpperCamelCase_ , self).__init__()
__UpperCAmelCase : int = 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=UpperCamelCase_ , )
def a_ ( self : List[str] , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.model.generate(
input_ids=UpperCamelCase_ , attention_mask=UpperCamelCase_ , max_new_tokens=UpperCamelCase_ , return_dict_in_generate=UpperCamelCase_ , )
return {"sequences": outputs["sequences"]}
__UpperCAmelCase : Union[str, Any] = [[2], [102, 103]]
__UpperCAmelCase : str = [[1], [1, 1]]
__UpperCAmelCase : Optional[int] = DummyModel(model=UpperCamelCase_)
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(UpperCamelCase_ , UpperCamelCase_ , signatures={"serving_default": dummy_model.serving})
__UpperCAmelCase : Tuple = tf.saved_model.load(UpperCamelCase_).signatures["serving_default"]
for input_row in range(len(UpperCamelCase_)):
__UpperCAmelCase : Dict = {
"input_ids": tf.constant([dummy_input_ids[input_row]]),
"attention_mask": tf.constant([dummy_attention_masks[input_row]]),
}
__UpperCAmelCase : Dict = serving_func(**UpperCamelCase_)["sequences"]
__UpperCAmelCase : int = test_model.generate(**UpperCamelCase_ , max_new_tokens=UpperCamelCase_)
tf.debugging.assert_equal(UpperCamelCase_ , UpperCamelCase_)
@slow
@require_tensorflow_text
def a_ ( self : 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=UpperCamelCase_)
class a__ ( tf.keras.layers.Layer ):
def __init__( self : Tuple):
"""simple docstring"""
super().__init__()
__UpperCAmelCase : Dict = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(UpperCamelCase_ , "spiece.model") , "rb").read())
__UpperCAmelCase : List[Any] = TFAutoModelForSeqaSeqLM.from_pretrained("hf-internal-testing/tiny-random-t5")
def a_ ( self : List[Any] , UpperCamelCase_ : Tuple , *UpperCamelCase_ : Dict , **UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = self.tokenizer.tokenize(UpperCamelCase_)
__UpperCAmelCase , __UpperCAmelCase : List[str] = text.pad_model_inputs(
UpperCamelCase_ , max_seq_length=64 , pad_value=self.model.config.pad_token_id)
__UpperCAmelCase : int = self.model.generate(input_ids=UpperCamelCase_ , attention_mask=UpperCamelCase_)
return self.tokenizer.detokenize(UpperCamelCase_)
__UpperCAmelCase : Dict = CompleteSentenceTransformer()
__UpperCAmelCase : List[str] = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name="inputs")
__UpperCAmelCase : Optional[Any] = complete_model(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = tf.keras.Model(UpperCamelCase_ , UpperCamelCase_)
keras_model.save(UpperCamelCase_)
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Tuple = {
"do_sample": True,
"num_beams": 1,
"top_p": 0.7,
"top_k": 10,
"temperature": 0.7,
}
__UpperCAmelCase : List[str] = 14
__UpperCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
__UpperCAmelCase : List[str] = "Hello, my dog is cute and"
__UpperCAmelCase : Optional[Any] = tokenizer(UpperCamelCase_ , return_tensors="tf")
__UpperCAmelCase : List[str] = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
__UpperCAmelCase : Optional[int] = 638
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(":/CPU:0"):
tf.random.set_seed(0)
__UpperCAmelCase : List[Any] = model.generate(**UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_)
self.assertTrue(expectation == len(generated_tokens[0]))
__UpperCAmelCase : Dict = [638, 198]
with tf.device(":/CPU:0"):
tf.random.set_seed(0)
__UpperCAmelCase : List[Any] = model.generate(**UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_)
self.assertTrue(expectation == len(generated_tokens[0]))
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : int = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
__UpperCAmelCase : Dict = "Hugging Face is a technology company based in New York and Paris."
__UpperCAmelCase : Tuple = bart_tokenizer(UpperCamelCase_ , return_tensors="tf").input_ids
__UpperCAmelCase : List[str] = TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart")
__UpperCAmelCase : int = bart_model.generate(UpperCamelCase_).numpy()
class a__ ( __magic_name__ ):
def a_ ( self : Any , UpperCamelCase_ : str , UpperCamelCase_ : List[Any]=None , **UpperCamelCase_ : int):
"""simple docstring"""
return super().call(UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : List[Any] = FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart")
__UpperCAmelCase : Union[str, Any] = bart_model.generate(UpperCamelCase_ , foo="bar").numpy()
self.assertTrue(np.array_equal(UpperCamelCase_ , UpperCamelCase_))
class a__ ( bart_model.model.encoder.__class__ ):
def a_ ( self : Dict , UpperCamelCase_ : int , **UpperCamelCase_ : Dict):
"""simple docstring"""
return super().call(UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : Dict = FakeEncoder(bart_model.config , bart_model.model.shared)
__UpperCAmelCase : str = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
__UpperCAmelCase : List[str] = bart_model.generate(UpperCamelCase_).numpy()
with self.assertRaises(UpperCamelCase_):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(UpperCamelCase_ , foo="bar")
| 77 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[str]:
"""simple docstring"""
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
__UpperCAmelCase : Optional[Any] = TapasConfig.from_json_file(UpperCamelCase )
# set absolute/relative position embeddings parameter
__UpperCAmelCase : Optional[Any] = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
__UpperCAmelCase : List[str] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "WTQ":
# run_task_main.py hparams
__UpperCAmelCase : Tuple = 4
__UpperCAmelCase : Any = True
# hparam_utils.py hparams
__UpperCAmelCase : Union[str, Any] = 0.664694
__UpperCAmelCase : Union[str, Any] = 0.207951
__UpperCAmelCase : int = 0.121194
__UpperCAmelCase : Optional[int] = True
__UpperCAmelCase : List[str] = True
__UpperCAmelCase : Union[str, Any] = False
__UpperCAmelCase : List[str] = 0.0352513
__UpperCAmelCase : Optional[int] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
__UpperCAmelCase : int = 4
__UpperCAmelCase : Optional[int] = False
# hparam_utils.py hparams
__UpperCAmelCase : int = 36.4519
__UpperCAmelCase : str = 0.903421
__UpperCAmelCase : Dict = 222.088
__UpperCAmelCase : Dict = True
__UpperCAmelCase : Union[str, Any] = True
__UpperCAmelCase : Tuple = True
__UpperCAmelCase : Any = 0.763141
__UpperCAmelCase : Optional[Any] = TapasForQuestionAnswering(config=UpperCamelCase )
elif task == "TABFACT":
__UpperCAmelCase : Union[str, Any] = TapasForSequenceClassification(config=UpperCamelCase )
elif task == "MLM":
__UpperCAmelCase : Tuple = TapasForMaskedLM(config=UpperCamelCase )
elif task == "INTERMEDIATE_PRETRAINING":
__UpperCAmelCase : List[str] = TapasModel(config=UpperCamelCase )
else:
raise ValueError(f"Task {task} not supported." )
print(f"Building PyTorch model from configuration: {config}" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(UpperCamelCase , UpperCamelCase , UpperCamelCase )
# Save pytorch-model (weights and configuration)
print(f"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(UpperCamelCase )
# Save tokenizer files
print(f"Save tokenizer files to {pytorch_dump_path}" )
__UpperCAmelCase : str = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt" , model_max_length=512 )
tokenizer.save_pretrained(UpperCamelCase )
print("Used relative position embeddings:" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
)
parser.add_argument(
"""--reset_position_index_per_cell""",
default=False,
action="""store_true""",
help="""Whether to use relative position embeddings or not. Defaults to True.""",
)
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--tapas_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained TAPAS model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 77 | 1 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class a__ :
def __init__( self : Optional[Any] , UpperCamelCase_ : List[str]):
"""simple docstring"""
__UpperCAmelCase : Tuple = str(id_)
__UpperCAmelCase : Optional[Any] = None
__UpperCAmelCase : Tuple = None
__UpperCAmelCase : Optional[Any] = []
__UpperCAmelCase : str = {} # {vertex:distance}
def __lt__( self : Optional[int] , UpperCamelCase_ : int):
"""simple docstring"""
return self.key < other.key
def __repr__( self : Tuple):
"""simple docstring"""
return self.id
def a_ ( self : int , UpperCamelCase_ : str):
"""simple docstring"""
self.neighbors.append(UpperCamelCase_)
def a_ ( self : Optional[Any] , UpperCamelCase_ : List[str] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : int = weight
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
# add the neighbors:
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , UpperCamelCase )
graph[b - 1].add_edge(graph[a - 1] , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> list:
"""simple docstring"""
__UpperCAmelCase : Optional[int] = []
for u in graph:
__UpperCAmelCase : Optional[int] = math.inf
__UpperCAmelCase : List[Any] = None
__UpperCAmelCase : int = 0
__UpperCAmelCase : Dict = graph[:]
while q:
__UpperCAmelCase : Optional[int] = min(UpperCamelCase )
q.remove(UpperCamelCase )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
__UpperCAmelCase : Any = u
__UpperCAmelCase : List[Any] = u.edges[v.id]
for i in range(1 , len(UpperCamelCase ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Iterator[tuple]:
"""simple docstring"""
for u in graph:
__UpperCAmelCase : Dict = math.inf
__UpperCAmelCase : Optional[Any] = None
__UpperCAmelCase : Optional[int] = 0
__UpperCAmelCase : Any = list(UpperCamelCase )
hq.heapify(UpperCamelCase )
while h:
__UpperCAmelCase : Any = hq.heappop(UpperCamelCase )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
__UpperCAmelCase : Any = u
__UpperCAmelCase : List[Any] = u.edges[v.id]
hq.heapify(UpperCamelCase )
for i in range(1 , len(UpperCamelCase ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def _UpperCamelCase ( ) -> None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 77 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = "cpu" , UpperCamelCase = None ) -> None:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = torch.load(UpperCamelCase , map_location=UpperCamelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(UpperCamelCase , torch.Tensor ):
raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin" )
__UpperCAmelCase : Optional[Any] = v.half()
if save_path is None: # overwrite src_path
__UpperCAmelCase : str = src_path
torch.save(UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 77 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = ShapEPipeline
lowercase_ = ["prompt"]
lowercase_ = ["prompt"]
lowercase_ = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
lowercase_ = False
@property
def a_ ( self : Optional[int]):
"""simple docstring"""
return 32
@property
def a_ ( self : Any):
"""simple docstring"""
return 32
@property
def a_ ( self : int):
"""simple docstring"""
return self.time_input_dim * 4
@property
def a_ ( self : List[Any]):
"""simple docstring"""
return 8
@property
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def a_ ( self : List[str]):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(UpperCamelCase_)
@property
def a_ ( self : Any):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Union[str, Any] = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
__UpperCAmelCase : Dict = PriorTransformer(**UpperCamelCase_)
return model
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Tuple = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
__UpperCAmelCase : List[Any] = ShapERenderer(**UpperCamelCase_)
return model
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.dummy_prior
__UpperCAmelCase : str = self.dummy_text_encoder
__UpperCAmelCase : int = self.dummy_tokenizer
__UpperCAmelCase : int = self.dummy_renderer
__UpperCAmelCase : Tuple = HeunDiscreteScheduler(
beta_schedule="exp" , num_train_timesteps=1024 , prediction_type="sample" , use_karras_sigmas=UpperCamelCase_ , clip_sample=UpperCamelCase_ , clip_sample_range=1.0 , )
__UpperCAmelCase : str = {
"prior": prior,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"renderer": renderer,
"scheduler": scheduler,
}
return components
def a_ ( self : Optional[int] , UpperCamelCase_ : Dict , UpperCamelCase_ : Any=0):
"""simple docstring"""
if str(UpperCamelCase_).startswith("mps"):
__UpperCAmelCase : List[Any] = torch.manual_seed(UpperCamelCase_)
else:
__UpperCAmelCase : str = torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)
__UpperCAmelCase : List[Any] = {
"prompt": "horse",
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "np",
}
return inputs
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : str = "cpu"
__UpperCAmelCase : Union[str, Any] = self.get_dummy_components()
__UpperCAmelCase : Union[str, Any] = self.pipeline_class(**UpperCamelCase_)
__UpperCAmelCase : Any = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = pipe(**self.get_dummy_inputs(UpperCamelCase_))
__UpperCAmelCase : Union[str, Any] = output.images[0]
__UpperCAmelCase : str = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__UpperCAmelCase : Union[str, Any] = np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def a_ ( self : Tuple):
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2])
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = torch_device == "cpu"
__UpperCAmelCase : Optional[Any] = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=UpperCamelCase_ , relax_max_difference=UpperCamelCase_ , )
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.get_dummy_components()
__UpperCAmelCase : List[str] = self.pipeline_class(**UpperCamelCase_)
__UpperCAmelCase : int = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = 1
__UpperCAmelCase : Any = 2
__UpperCAmelCase : Optional[Any] = self.get_dummy_inputs(UpperCamelCase_)
for key in inputs.keys():
if key in self.batch_params:
__UpperCAmelCase : List[Any] = batch_size * [inputs[key]]
__UpperCAmelCase : List[Any] = pipe(**UpperCamelCase_ , num_images_per_prompt=UpperCamelCase_)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def a_ ( self : List[str]):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Dict = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_np_out.npy")
__UpperCAmelCase : Optional[Any] = ShapEPipeline.from_pretrained("openai/shap-e")
__UpperCAmelCase : Any = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Dict = torch.Generator(device=UpperCamelCase_).manual_seed(0)
__UpperCAmelCase : int = pipe(
"a shark" , generator=UpperCamelCase_ , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type="np" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(UpperCamelCase_ , UpperCamelCase_)
| 77 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A = pd.read_csv("""sample_data.csv""", header=None)
A = df.shape[:1][0]
# If you're using some other dataset input the target column
A = df.iloc[:, 1:2]
A = actual_data.values.reshape(len_data, 1)
A = MinMaxScaler().fit_transform(actual_data)
A = 10
A = 5
A = 20
A = len_data - periods * look_back
A = actual_data[:division]
A = actual_data[division - look_back :]
A , A = [], []
A , A = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
A = np.array(train_x)
A = np.array(test_x)
A = np.array([list(i.ravel()) for i in train_y])
A = np.array([list(i.ravel()) for i in test_y])
A = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="""mean_squared_error""", optimizer="""adam""")
A = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
A = model.predict(x_test)
| 77 | 1 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
A = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def _UpperCamelCase ( ) -> str:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = Github(os.environ["GITHUB_TOKEN"] )
__UpperCAmelCase : Optional[Any] = g.get_repo("huggingface/diffusers" )
__UpperCAmelCase : int = repo.get_issues(state="open" )
for issue in open_issues:
__UpperCAmelCase : Dict = sorted(issue.get_comments() , key=lambda UpperCamelCase : i.created_at , reverse=UpperCamelCase )
__UpperCAmelCase : Optional[Any] = comments[0] if len(UpperCamelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state="closed" )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state="open" )
issue.remove_from_labels("stale" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
"This issue has been automatically marked as stale because it has not had "
"recent activity. If you think this still needs to be addressed "
"please comment on this thread.\n\nPlease note that issues that do not follow the "
"[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
issue.add_to_labels("stale" )
if __name__ == "__main__":
main()
| 77 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
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__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = MBartTokenizer
lowercase_ = MBartTokenizerFast
lowercase_ = True
lowercase_ = True
def a_ ( self : str):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__UpperCAmelCase : Any = MBartTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_)
tokenizer.save_pretrained(self.tmpdirname)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Dict = MBartTokenizer(UpperCamelCase_ , keep_accents=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = tokenizer.tokenize("This is a test")
self.assertListEqual(UpperCamelCase_ , ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCamelCase_) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__UpperCAmelCase : List[Any] = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
UpperCamelCase_ , [
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 : Any = tokenizer.convert_tokens_to_ids(UpperCamelCase_)
self.assertListEqual(
UpperCamelCase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(UpperCamelCase_)
self.assertListEqual(
UpperCamelCase_ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def a_ ( self : Dict):
"""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 : Dict = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
__UpperCAmelCase : List[str] = self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : int = self.tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_)
__UpperCAmelCase : int = tempfile.mkdtemp()
__UpperCAmelCase : Optional[int] = tokenizer_r.save_pretrained(UpperCamelCase_)
__UpperCAmelCase : Any = tokenizer_p.save_pretrained(UpperCamelCase_)
# 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(UpperCamelCase_ , UpperCamelCase_)
# Checks everything loads correctly in the same way
__UpperCAmelCase : int = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : Tuple = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCamelCase_)
# Save tokenizer rust, legacy_format=True
__UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
__UpperCAmelCase : Dict = tokenizer_r.save_pretrained(UpperCamelCase_ , legacy_format=UpperCamelCase_)
__UpperCAmelCase : int = tokenizer_p.save_pretrained(UpperCamelCase_)
# Checks it save with the same files
self.assertSequenceEqual(UpperCamelCase_ , UpperCamelCase_)
# Checks everything loads correctly in the same way
__UpperCAmelCase : int = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
shutil.rmtree(UpperCamelCase_)
# Save tokenizer rust, legacy_format=False
__UpperCAmelCase : Tuple = tempfile.mkdtemp()
__UpperCAmelCase : int = tokenizer_r.save_pretrained(UpperCamelCase_ , legacy_format=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = tokenizer_p.save_pretrained(UpperCamelCase_)
# 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 : Optional[Any] = tokenizer_r.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : str = tokenizer_p.from_pretrained(UpperCamelCase_)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase_ , UpperCamelCase_))
shutil.rmtree(UpperCamelCase_)
@require_torch
@require_sentencepiece
@require_tokenizers
class a__ ( unittest.TestCase ):
lowercase_ = "facebook/mbart-large-en-ro"
lowercase_ = [
" 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.",
]
lowercase_ = [
"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.",
]
lowercase_ = [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]
@classmethod
def a_ ( cls : int):
"""simple docstring"""
__UpperCAmelCase : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en_XX" , tgt_lang="ro_RO")
__UpperCAmelCase : Union[str, Any] = 1
return cls
def a_ ( self : List[Any]):
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] , 250001)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] , 250004)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] , 250020)
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase_)
def a_ ( self : Optional[int]):
"""simple docstring"""
self.assertIn(UpperCamelCase_ , self.tokenizer.all_special_ids)
__UpperCAmelCase : Union[str, Any] = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
__UpperCAmelCase : Optional[Any] = self.tokenizer.decode(UpperCamelCase_ , skip_special_tokens=UpperCamelCase_)
__UpperCAmelCase : int = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , UpperCamelCase_)
self.assertNotIn(self.tokenizer.eos_token , UpperCamelCase_)
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , UpperCamelCase_)
__UpperCAmelCase : Tuple = 10
__UpperCAmelCase : List[Any] = self.tokenizer(UpperCamelCase_ , max_length=UpperCamelCase_ , truncation=UpperCamelCase_).input_ids[0]
self.assertEqual(ids[-2] , 2)
self.assertEqual(ids[-1] , UpperCamelCase_)
self.assertEqual(len(UpperCamelCase_) , UpperCamelCase_)
def a_ ( self : Any):
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]) , [250026, 250001])
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = tempfile.mkdtemp()
__UpperCAmelCase : Union[str, Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCamelCase_)
__UpperCAmelCase : List[Any] = MBartTokenizer.from_pretrained(UpperCamelCase_)
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , UpperCamelCase_)
@require_torch
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCamelCase_ , 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][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=len(self.expected_src_tokens) , return_tensors="pt" , )
__UpperCAmelCase : Tuple = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id)
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_)
self.assertEqual((2, 14) , batch.input_ids.shape)
self.assertEqual((2, 14) , batch.attention_mask.shape)
__UpperCAmelCase : List[str] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase_)
self.assertEqual(2 , batch.decoder_input_ids[0, -1]) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [])
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE])
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : List[str] = self.tokenizer(self.src_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=3 , return_tensors="pt")
__UpperCAmelCase : Any = self.tokenizer(
text_target=self.tgt_text , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=10 , return_tensors="pt")
__UpperCAmelCase : int = targets["input_ids"]
__UpperCAmelCase : Any = shift_tokens_right(UpperCamelCase_ , self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1] , 3)
self.assertEqual(batch.decoder_input_ids.shape[1] , 10)
@require_torch
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : int = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="ar_AR")
self.assertEqual(
nested_simplify(UpperCamelCase_) , {
# A, test, EOS, en_XX
"input_ids": [[62, 3034, 2, 250004]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 250001,
} , )
| 77 | 1 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase=True , UpperCamelCase="pt" ) -> List[Any]:
"""simple docstring"""
__UpperCAmelCase : List[Any] = {"add_prefix_space": True} if isinstance(UpperCamelCase , UpperCamelCase ) and not line.startswith(" " ) else {}
__UpperCAmelCase : str = padding_side
return tokenizer(
[line] , max_length=UpperCamelCase , padding="max_length" if pad_to_max_length else None , truncation=UpperCamelCase , return_tensors=UpperCamelCase , add_special_tokens=UpperCamelCase , **UpperCamelCase , )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase=None , ) -> int:
"""simple docstring"""
__UpperCAmelCase : Any = input_ids.ne(UpperCamelCase ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class a__ ( __magic_name__ ):
def __init__( self : Tuple , UpperCamelCase_ : str , UpperCamelCase_ : List[str] , UpperCamelCase_ : Any , UpperCamelCase_ : Any , UpperCamelCase_ : Union[str, Any]="train" , UpperCamelCase_ : Any=None , UpperCamelCase_ : List[Any]=None , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : Optional[int]="" , ):
"""simple docstring"""
super().__init__()
__UpperCAmelCase : List[Any] = Path(UpperCamelCase_).joinpath(type_path + ".source")
__UpperCAmelCase : List[str] = Path(UpperCamelCase_).joinpath(type_path + ".target")
__UpperCAmelCase : int = self.get_char_lens(self.src_file)
__UpperCAmelCase : Optional[int] = max_source_length
__UpperCAmelCase : Dict = max_target_length
assert min(self.src_lens) > 0, F"found empty line in {self.src_file}"
__UpperCAmelCase : str = tokenizer
__UpperCAmelCase : Optional[int] = prefix
if n_obs is not None:
__UpperCAmelCase : List[str] = self.src_lens[:n_obs]
__UpperCAmelCase : Any = src_lang
__UpperCAmelCase : List[Any] = tgt_lang
def __len__( self : str):
"""simple docstring"""
return len(self.src_lens)
def __getitem__( self : Optional[Any] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : int = index + 1 # linecache starts at 1
__UpperCAmelCase : Any = self.prefix + linecache.getline(str(self.src_file) , UpperCamelCase_).rstrip("\n")
__UpperCAmelCase : Dict = linecache.getline(str(self.tgt_file) , UpperCamelCase_).rstrip("\n")
assert source_line, F"empty source line for index {index}"
assert tgt_line, F"empty tgt line for index {index}"
# Need to add eos token manually for T5
if isinstance(self.tokenizer , UpperCamelCase_):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__UpperCAmelCase : Dict = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , UpperCamelCase_) else self.tokenizer
)
__UpperCAmelCase : Dict = self.tokenizer.generator if isinstance(self.tokenizer , UpperCamelCase_) else self.tokenizer
__UpperCAmelCase : List[str] = encode_line(UpperCamelCase_ , UpperCamelCase_ , self.max_source_length , "right")
__UpperCAmelCase : List[str] = encode_line(UpperCamelCase_ , UpperCamelCase_ , self.max_target_length , "right")
__UpperCAmelCase : str = source_inputs["input_ids"].squeeze()
__UpperCAmelCase : Optional[Any] = target_inputs["input_ids"].squeeze()
__UpperCAmelCase : Any = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def a_ ( UpperCamelCase_ : Tuple):
"""simple docstring"""
return [len(UpperCamelCase_) for x in Path(UpperCamelCase_).open().readlines()]
def a_ ( self : Dict , UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : str = torch.stack([x["input_ids"] for x in batch])
__UpperCAmelCase : List[str] = torch.stack([x["attention_mask"] for x in batch])
__UpperCAmelCase : Dict = torch.stack([x["decoder_input_ids"] for x in batch])
__UpperCAmelCase : int = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , UpperCamelCase_)
else self.tokenizer.pad_token_id
)
__UpperCAmelCase : List[Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , UpperCamelCase_)
else self.tokenizer.pad_token_id
)
__UpperCAmelCase : str = trim_batch(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase , __UpperCAmelCase : Dict = trim_batch(UpperCamelCase_ , UpperCamelCase_ , attention_mask=UpperCamelCase_)
__UpperCAmelCase : str = {
"input_ids": source_ids,
"attention_mask": source_mask,
"decoder_input_ids": y,
}
return batch
A = getLogger(__name__)
def _UpperCamelCase ( UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
return list(itertools.chain.from_iterable(UpperCamelCase ) )
def _UpperCamelCase ( UpperCamelCase ) -> None:
"""simple docstring"""
__UpperCAmelCase : List[Any] = get_git_info()
save_json(UpperCamelCase , os.path.join(UpperCamelCase , "git_log.json" ) )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase=4 , **UpperCamelCase ) -> Dict:
"""simple docstring"""
with open(UpperCamelCase , "w" ) as f:
json.dump(UpperCamelCase , UpperCamelCase , indent=UpperCamelCase , **UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
with open(UpperCamelCase ) as f:
return json.load(UpperCamelCase )
def _UpperCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase : List[str] = git.Repo(search_parent_directories=UpperCamelCase )
__UpperCAmelCase : Any = {
"repo_id": str(UpperCamelCase ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
"hostname": str(socket.gethostname() ),
}
return repo_infos
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> List:
"""simple docstring"""
return list(map(UpperCamelCase , UpperCamelCase ) )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
with open(UpperCamelCase , "wb" ) as f:
return pickle.dump(UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
def remove_articles(UpperCamelCase ):
return re.sub(R"\b(a|an|the)\b" , " " , UpperCamelCase )
def white_space_fix(UpperCamelCase ):
return " ".join(text.split() )
def remove_punc(UpperCamelCase ):
__UpperCAmelCase : Optional[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(UpperCamelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase ) ) ) )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[int] = normalize_answer(UpperCamelCase ).split()
__UpperCAmelCase : Tuple = normalize_answer(UpperCamelCase ).split()
__UpperCAmelCase : Union[str, Any] = Counter(UpperCamelCase ) & Counter(UpperCamelCase )
__UpperCAmelCase : str = sum(common.values() )
if num_same == 0:
return 0
__UpperCAmelCase : Any = 1.0 * num_same / len(UpperCamelCase )
__UpperCAmelCase : List[str] = 1.0 * num_same / len(UpperCamelCase )
__UpperCAmelCase : Tuple = (2 * precision * recall) / (precision + recall)
return fa
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> List[str]:
"""simple docstring"""
return normalize_answer(UpperCamelCase ) == normalize_answer(UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Dict:
"""simple docstring"""
assert len(UpperCamelCase ) == len(UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = 0
for hypo, pred in zip(UpperCamelCase , UpperCamelCase ):
em += exact_match_score(UpperCamelCase , UpperCamelCase )
if len(UpperCamelCase ) > 0:
em /= len(UpperCamelCase )
return {"em": em}
def _UpperCamelCase ( UpperCamelCase ) -> Tuple:
"""simple docstring"""
return model_prefix.startswith("rag" )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__UpperCAmelCase : Optional[int] = "dropout_rate"
for p in extra_params:
if getattr(UpperCamelCase , UpperCamelCase , UpperCamelCase ):
if not hasattr(UpperCamelCase , UpperCamelCase ) and not hasattr(UpperCamelCase , equivalent_param[p] ):
logger.info("config doesn't have a `{}` attribute".format(UpperCamelCase ) )
delattr(UpperCamelCase , UpperCamelCase )
continue
__UpperCAmelCase : List[Any] = p if hasattr(UpperCamelCase , UpperCamelCase ) else equivalent_param[p]
setattr(UpperCamelCase , UpperCamelCase , getattr(UpperCamelCase , UpperCamelCase ) )
delattr(UpperCamelCase , UpperCamelCase )
return hparams, config
| 77 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
class a__ :
def __init__( self : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = None
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.head
while temp is not None:
print(temp.data , end=" ")
__UpperCAmelCase : Tuple = temp.next
print()
def a_ ( self : int , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : List[str] = Node(UpperCamelCase_)
__UpperCAmelCase : str = self.head
__UpperCAmelCase : Optional[int] = new_node
def a_ ( self : str , UpperCamelCase_ : List[Any] , UpperCamelCase_ : str):
"""simple docstring"""
if node_data_a == node_data_a:
return
else:
__UpperCAmelCase : int = self.head
while node_a is not None and node_a.data != node_data_a:
__UpperCAmelCase : Tuple = node_a.next
__UpperCAmelCase : List[Any] = self.head
while node_a is not None and node_a.data != node_data_a:
__UpperCAmelCase : Optional[Any] = node_a.next
if node_a is None or node_a is None:
return
__UpperCAmelCase , __UpperCAmelCase : Any = 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()
| 77 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class a__ ( __magic_name__ ):
lowercase_ = 42
class a__ ( __magic_name__ , __magic_name__ ):
@register_to_config
def __init__( self : Optional[int] , UpperCamelCase_ : int = 32 , UpperCamelCase_ : int = 64 , UpperCamelCase_ : int = 20 , UpperCamelCase_ : int = 768 , UpperCamelCase_ : List[Any]=77 , UpperCamelCase_ : Dict=4 , UpperCamelCase_ : float = 0.0 , UpperCamelCase_ : str = "silu" , UpperCamelCase_ : Optional[str] = None , UpperCamelCase_ : Optional[str] = None , UpperCamelCase_ : Optional[str] = "linear" , UpperCamelCase_ : Optional[str] = "prd" , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[int] = None , ):
"""simple docstring"""
super().__init__()
__UpperCAmelCase : int = num_attention_heads
__UpperCAmelCase : int = attention_head_dim
__UpperCAmelCase : Tuple = num_attention_heads * attention_head_dim
__UpperCAmelCase : List[str] = additional_embeddings
__UpperCAmelCase : int = time_embed_dim or inner_dim
__UpperCAmelCase : List[str] = embedding_proj_dim or embedding_dim
__UpperCAmelCase : Optional[Any] = clip_embed_dim or embedding_dim
__UpperCAmelCase : Tuple = Timesteps(UpperCamelCase_ , UpperCamelCase_ , 0)
__UpperCAmelCase : List[str] = TimestepEmbedding(UpperCamelCase_ , UpperCamelCase_ , out_dim=UpperCamelCase_ , act_fn=UpperCamelCase_)
__UpperCAmelCase : int = nn.Linear(UpperCamelCase_ , UpperCamelCase_)
if embedding_proj_norm_type is None:
__UpperCAmelCase : Union[str, Any] = None
elif embedding_proj_norm_type == "layer":
__UpperCAmelCase : str = nn.LayerNorm(UpperCamelCase_)
else:
raise ValueError(F"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}")
__UpperCAmelCase : List[str] = nn.Linear(UpperCamelCase_ , UpperCamelCase_)
if encoder_hid_proj_type is None:
__UpperCAmelCase : List[str] = None
elif encoder_hid_proj_type == "linear":
__UpperCAmelCase : List[str] = nn.Linear(UpperCamelCase_ , UpperCamelCase_)
else:
raise ValueError(F"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}")
__UpperCAmelCase : List[Any] = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , UpperCamelCase_))
if added_emb_type == "prd":
__UpperCAmelCase : Union[str, Any] = nn.Parameter(torch.zeros(1 , 1 , UpperCamelCase_))
elif added_emb_type is None:
__UpperCAmelCase : int = None
else:
raise ValueError(
F"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`.")
__UpperCAmelCase : Union[str, Any] = nn.ModuleList(
[
BasicTransformerBlock(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , dropout=UpperCamelCase_ , activation_fn="gelu" , attention_bias=UpperCamelCase_ , )
for d in range(UpperCamelCase_)
])
if norm_in_type == "layer":
__UpperCAmelCase : List[Any] = nn.LayerNorm(UpperCamelCase_)
elif norm_in_type is None:
__UpperCAmelCase : Any = None
else:
raise ValueError(F"Unsupported norm_in_type: {norm_in_type}.")
__UpperCAmelCase : List[str] = nn.LayerNorm(UpperCamelCase_)
__UpperCAmelCase : Any = nn.Linear(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Any = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -10000.0)
causal_attention_mask.triu_(1)
__UpperCAmelCase : str = causal_attention_mask[None, ...]
self.register_buffer("causal_attention_mask" , UpperCamelCase_ , persistent=UpperCamelCase_)
__UpperCAmelCase : Tuple = nn.Parameter(torch.zeros(1 , UpperCamelCase_))
__UpperCAmelCase : Any = nn.Parameter(torch.zeros(1 , UpperCamelCase_))
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Dict = {}
def fn_recursive_add_processors(UpperCamelCase_ : str , UpperCamelCase_ : torch.nn.Module , UpperCamelCase_ : Dict[str, AttentionProcessor]):
if hasattr(UpperCamelCase_ , "set_processor"):
__UpperCAmelCase : int = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(F"{name}.{sub_name}" , UpperCamelCase_ , UpperCamelCase_)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
return processors
def a_ ( self : str , UpperCamelCase_ : Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
"""simple docstring"""
__UpperCAmelCase : int = len(self.attn_processors.keys())
if isinstance(UpperCamelCase_ , UpperCamelCase_) and len(UpperCamelCase_) != count:
raise ValueError(
F"A dict of processors was passed, but the number of processors {len(UpperCamelCase_)} does not match the"
F" number of attention layers: {count}. Please make sure to pass {count} processor classes.")
def fn_recursive_attn_processor(UpperCamelCase_ : str , UpperCamelCase_ : torch.nn.Module , UpperCamelCase_ : Any):
if hasattr(UpperCamelCase_ , "set_processor"):
if not isinstance(UpperCamelCase_ , UpperCamelCase_):
module.set_processor(UpperCamelCase_)
else:
module.set_processor(processor.pop(F"{name}.processor"))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(F"{name}.{sub_name}" , UpperCamelCase_ , UpperCamelCase_)
for name, module in self.named_children():
fn_recursive_attn_processor(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
def a_ ( self : str):
"""simple docstring"""
self.set_attn_processor(AttnProcessor())
def a_ ( self : Dict , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Union[torch.Tensor, float, int] , UpperCamelCase_ : torch.FloatTensor , UpperCamelCase_ : Optional[torch.FloatTensor] = None , UpperCamelCase_ : Optional[torch.BoolTensor] = None , UpperCamelCase_ : bool = True , ):
"""simple docstring"""
__UpperCAmelCase : int = hidden_states.shape[0]
__UpperCAmelCase : Union[str, Any] = timestep
if not torch.is_tensor(UpperCamelCase_):
__UpperCAmelCase : Dict = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device)
elif torch.is_tensor(UpperCamelCase_) and len(timesteps.shape) == 0:
__UpperCAmelCase : Union[str, Any] = timesteps[None].to(hidden_states.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__UpperCAmelCase : str = timesteps * torch.ones(UpperCamelCase_ , dtype=timesteps.dtype , device=timesteps.device)
__UpperCAmelCase : Union[str, Any] = self.time_proj(UpperCamelCase_)
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
__UpperCAmelCase : Any = timesteps_projected.to(dtype=self.dtype)
__UpperCAmelCase : Optional[int] = self.time_embedding(UpperCamelCase_)
if self.embedding_proj_norm is not None:
__UpperCAmelCase : List[str] = self.embedding_proj_norm(UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = self.embedding_proj(UpperCamelCase_)
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
__UpperCAmelCase : Union[str, Any] = self.encoder_hidden_states_proj(UpperCamelCase_)
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set")
__UpperCAmelCase : List[Any] = self.proj_in(UpperCamelCase_)
__UpperCAmelCase : str = self.positional_embedding.to(hidden_states.dtype)
__UpperCAmelCase : int = []
__UpperCAmelCase : Union[str, Any] = 0
if encoder_hidden_states is not None:
additional_embeds.append(UpperCamelCase_)
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape) == 2:
__UpperCAmelCase : Union[str, Any] = proj_embeddings[:, None, :]
if len(hidden_states.shape) == 2:
__UpperCAmelCase : List[str] = hidden_states[:, None, :]
__UpperCAmelCase : List[str] = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
__UpperCAmelCase : Union[str, Any] = self.prd_embedding.to(hidden_states.dtype).expand(UpperCamelCase_ , -1 , -1)
additional_embeds.append(UpperCamelCase_)
__UpperCAmelCase : Optional[int] = torch.cat(
UpperCamelCase_ , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
__UpperCAmelCase : List[Any] = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
__UpperCAmelCase : str = F.pad(
UpperCamelCase_ , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
__UpperCAmelCase : Tuple = hidden_states + positional_embeddings
if attention_mask is not None:
__UpperCAmelCase : Dict = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
__UpperCAmelCase : str = F.pad(UpperCamelCase_ , (0, self.additional_embeddings) , value=0.0)
__UpperCAmelCase : List[str] = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)
__UpperCAmelCase : List[Any] = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0)
if self.norm_in is not None:
__UpperCAmelCase : int = self.norm_in(UpperCamelCase_)
for block in self.transformer_blocks:
__UpperCAmelCase : int = block(UpperCamelCase_ , attention_mask=UpperCamelCase_)
__UpperCAmelCase : Any = self.norm_out(UpperCamelCase_)
if self.prd_embedding is not None:
__UpperCAmelCase : Dict = hidden_states[:, -1]
else:
__UpperCAmelCase : str = hidden_states[:, additional_embeddings_len:]
__UpperCAmelCase : List[str] = self.proj_to_clip_embeddings(UpperCamelCase_)
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=UpperCamelCase_)
def a_ ( self : str , UpperCamelCase_ : Dict):
"""simple docstring"""
__UpperCAmelCase : str = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 77 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
A = """
Human: <<task>>
Assistant: """
A = """huggingface-tools/default-prompts"""
A = {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase="run" ) -> List[str]:
"""simple docstring"""
if prompt_or_repo_id is None:
__UpperCAmelCase : Optional[int] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , UpperCamelCase ) is not None:
return prompt_or_repo_id
__UpperCAmelCase : str = cached_file(
UpperCamelCase , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(UpperCamelCase , "r" , encoding="utf-8" ) as f:
return f.read()
| 77 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpeechConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""UniSpeechForCTC""",
"""UniSpeechForPreTraining""",
"""UniSpeechForSequenceClassification""",
"""UniSpeechModel""",
"""UniSpeechPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ErnieForCausalLM""",
"""ErnieForMaskedLM""",
"""ErnieForMultipleChoice""",
"""ErnieForNextSentencePrediction""",
"""ErnieForPreTraining""",
"""ErnieForQuestionAnswering""",
"""ErnieForSequenceClassification""",
"""ErnieForTokenClassification""",
"""ErnieModel""",
"""ErniePreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> bool:
"""simple docstring"""
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
__UpperCAmelCase : List[Any] = 4
__UpperCAmelCase : Union[str, Any] = (1 << p) - 1
for _ in range(p - 2 ):
__UpperCAmelCase : str = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(11))
| 77 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ( nn.Module ):
def __init__( self : Union[str, Any]):
"""simple docstring"""
super().__init__()
__UpperCAmelCase : Optional[int] = nn.Linear(3 , 4)
__UpperCAmelCase : str = nn.BatchNormad(4)
__UpperCAmelCase : int = nn.Linear(4 , 5)
def a_ ( self : str , UpperCamelCase_ : List[str]):
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(UpperCamelCase_)))
class a__ ( unittest.TestCase ):
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = ModelForTest()
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , model.state_dict())
__UpperCAmelCase : Union[str, Any] = os.path.join(UpperCamelCase_ , "index.json")
self.assertTrue(os.path.isfile(UpperCamelCase_))
# TODO: add tests on what is inside the index
for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
__UpperCAmelCase : Optional[int] = os.path.join(UpperCamelCase_ , F"{key}.dat")
self.assertTrue(os.path.isfile(UpperCamelCase_))
# TODO: add tests on the fact weights are properly loaded
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : int = [torch.floataa, torch.floataa, torch.bfloataa]
for dtype in dtypes:
__UpperCAmelCase : List[Any] = torch.randn(2 , 3 , dtype=UpperCamelCase_)
with TemporaryDirectory() as tmp_dir:
__UpperCAmelCase : Tuple = offload_weight(UpperCamelCase_ , "weight" , UpperCamelCase_ , {})
__UpperCAmelCase : Dict = os.path.join(UpperCamelCase_ , "weight.dat")
self.assertTrue(os.path.isfile(UpperCamelCase_))
self.assertDictEqual(UpperCamelCase_ , {"weight": {"shape": [2, 3], "dtype": str(UpperCamelCase_).split(".")[1]}})
__UpperCAmelCase : Optional[Any] = load_offloaded_weight(UpperCamelCase_ , index["weight"])
self.assertTrue(torch.equal(UpperCamelCase_ , UpperCamelCase_))
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = ModelForTest()
__UpperCAmelCase : Optional[int] = model.state_dict()
__UpperCAmelCase : List[str] = {k: v for k, v in state_dict.items() if "linear2" not in k}
__UpperCAmelCase : Optional[int] = {k: v for k, v in state_dict.items() if "linear2" in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : List[str] = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
__UpperCAmelCase : Optional[int] = {k: v for k, v in state_dict.items() if "weight" in k}
__UpperCAmelCase : Optional[Any] = {k: v for k, v in state_dict.items() if "weight" not in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
# Duplicates are removed
__UpperCAmelCase : str = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Any = {"a.1": 0, "a.10": 1, "a.2": 2}
__UpperCAmelCase : Union[str, Any] = extract_submodules_state_dict(UpperCamelCase_ , ["a.1", "a.2"])
self.assertDictEqual(UpperCamelCase_ , {"a.1": 0, "a.2": 2})
__UpperCAmelCase : int = {"a.1.a": 0, "a.10.a": 1, "a.2.a": 2}
__UpperCAmelCase : int = extract_submodules_state_dict(UpperCamelCase_ , ["a.1", "a.2"])
self.assertDictEqual(UpperCamelCase_ , {"a.1.a": 0, "a.2.a": 2})
| 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
__UpperCAmelCase : List[Any] = len(UpperCamelCase )
for _ in range(UpperCamelCase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
A = list(range(10, 0, -1))
print(f'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''')
| 77 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelCase : Union[str, Any] = n - k
# Calculate C(n,k)
for i in range(UpperCamelCase ):
result *= n - i
result //= i + 1
return result
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
return binomial_coefficient(2 * node_count , UpperCamelCase ) // (node_count + 1)
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
if n < 0:
raise ValueError("factorial() not defined for negative values" )
__UpperCAmelCase : Optional[Any] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def _UpperCamelCase ( UpperCamelCase ) -> int:
"""simple docstring"""
return catalan_number(UpperCamelCase ) * factorial(UpperCamelCase )
if __name__ == "__main__":
A = int(input("""Enter the number of nodes: """).strip() or 0)
if node_count <= 0:
raise ValueError("""We need some nodes to work with.""")
print(
f'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
f'''binary trees and {catalan_number(node_count)} binary search trees.'''
)
| 77 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
A = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
A = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> tuple[str, float]:
"""simple docstring"""
__UpperCAmelCase : str = len([g for position, g in enumerate(UpperCamelCase ) if g == main_target[position]] )
return (item, float(UpperCamelCase ))
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> tuple[str, str]:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = random.randint(0 , len(UpperCamelCase ) - 1 )
__UpperCAmelCase : Optional[int] = parent_a[:random_slice] + parent_a[random_slice:]
__UpperCAmelCase : Optional[Any] = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : Dict = list(UpperCamelCase )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
__UpperCAmelCase : Union[str, Any] = random.choice(UpperCamelCase )
return "".join(UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , ) -> list[str]:
"""simple docstring"""
__UpperCAmelCase : List[str] = []
# Generate more children proportionally to the fitness score.
__UpperCAmelCase : int = int(parent_a[1] * 100 ) + 1
__UpperCAmelCase : List[str] = 10 if child_n >= 10 else child_n
for _ in range(UpperCamelCase ):
__UpperCAmelCase : str = population_score[random.randint(0 , UpperCamelCase )][0]
__UpperCAmelCase , __UpperCAmelCase : List[Any] = crossover(parent_a[0] , UpperCamelCase )
# Append new string to the population list.
pop.append(mutate(UpperCamelCase , UpperCamelCase ) )
pop.append(mutate(UpperCamelCase , UpperCamelCase ) )
return pop
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase = True ) -> tuple[int, int, str]:
"""simple docstring"""
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
__UpperCAmelCase : Dict = f"{N_POPULATION} must be bigger than {N_SELECTED}"
raise ValueError(UpperCamelCase )
# Verify that the target contains no genes besides the ones inside genes variable.
__UpperCAmelCase : List[str] = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
__UpperCAmelCase : List[Any] = f"{not_in_genes_list} is not in genes list, evolution cannot converge"
raise ValueError(UpperCamelCase )
# Generate random starting population.
__UpperCAmelCase : Optional[int] = []
for _ in range(UpperCamelCase ):
population.append("".join([random.choice(UpperCamelCase ) for i in range(len(UpperCamelCase ) )] ) )
# Just some logs to know what the algorithms is doing.
__UpperCAmelCase , __UpperCAmelCase : List[str] = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(UpperCamelCase )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
__UpperCAmelCase : Any = [evaluate(UpperCamelCase , UpperCamelCase ) for item in population]
# Check if there is a matching evolution.
__UpperCAmelCase : int = sorted(UpperCamelCase , key=lambda UpperCamelCase : x[1] , reverse=UpperCamelCase )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f"\nGeneration: {generation}"
f"\nTotal Population:{total_population}"
f"\nBest score: {population_score[0][1]}"
f"\nBest string: {population_score[0][0]}" )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
__UpperCAmelCase : int = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(UpperCamelCase )
# Normalize population score to be between 0 and 1.
__UpperCAmelCase : Union[str, Any] = [
(item, score / len(UpperCamelCase )) for item, score in population_score
]
# This is selection
for i in range(UpperCamelCase ):
population.extend(select(population_score[int(UpperCamelCase )] , UpperCamelCase , UpperCamelCase ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(UpperCamelCase ) > N_POPULATION:
break
if __name__ == "__main__":
A = (
"""This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
)
A = list(
""" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
"""nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
)
A , A , A = basic(target_str, genes_list)
print(
f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 77 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
"""processing_trocr""": ["""TrOCRProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TrOCRForCausalLM""",
"""TrOCRPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 | 1 |
"""simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
A = 6378137.0
A = 6356752.314245
A = 6_378_137
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> float:
"""simple docstring"""
__UpperCAmelCase : Dict = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
__UpperCAmelCase : Dict = atan((1 - flattening) * tan(radians(UpperCamelCase ) ) )
__UpperCAmelCase : Optional[int] = atan((1 - flattening) * tan(radians(UpperCamelCase ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
__UpperCAmelCase : List[Any] = haversine_distance(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
__UpperCAmelCase : str = (b_lata + b_lata) / 2
__UpperCAmelCase : Tuple = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
__UpperCAmelCase : Tuple = (sin(UpperCamelCase ) ** 2) * (cos(UpperCamelCase ) ** 2)
__UpperCAmelCase : Optional[Any] = cos(sigma / 2 ) ** 2
__UpperCAmelCase : List[Any] = (sigma - sin(UpperCamelCase )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
__UpperCAmelCase : Union[str, Any] = (cos(UpperCamelCase ) ** 2) * (sin(UpperCamelCase ) ** 2)
__UpperCAmelCase : List[str] = sin(sigma / 2 ) ** 2
__UpperCAmelCase : Union[str, Any] = (sigma + sin(UpperCamelCase )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 77 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self : Optional[Any] , UpperCamelCase_ : int , UpperCamelCase_ : Any=13 , UpperCamelCase_ : Optional[int]=3 , UpperCamelCase_ : int=224 , UpperCamelCase_ : int=30 , UpperCamelCase_ : str=400 , UpperCamelCase_ : List[Any]=True , UpperCamelCase_ : Optional[Any]=None , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Optional[int]=[0.5, 0.5, 0.5] , UpperCamelCase_ : Optional[Any]=[0.5, 0.5, 0.5] , ):
"""simple docstring"""
__UpperCAmelCase : Tuple = size if size is not None else {"height": 18, "width": 18}
__UpperCAmelCase : List[Any] = parent
__UpperCAmelCase : Tuple = batch_size
__UpperCAmelCase : Tuple = num_channels
__UpperCAmelCase : List[Any] = image_size
__UpperCAmelCase : str = min_resolution
__UpperCAmelCase : Tuple = max_resolution
__UpperCAmelCase : Optional[Any] = do_resize
__UpperCAmelCase : Any = size
__UpperCAmelCase : Any = do_normalize
__UpperCAmelCase : Any = image_mean
__UpperCAmelCase : Optional[Any] = image_std
def a_ ( self : str):
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = ViTImageProcessor if is_vision_available() else None
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = EfficientFormerImageProcessorTester(self)
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
return self.image_proc_tester.prepare_image_processor_dict()
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(UpperCamelCase_ , "image_mean"))
self.assertTrue(hasattr(UpperCamelCase_ , "image_std"))
self.assertTrue(hasattr(UpperCamelCase_ , "do_normalize"))
self.assertTrue(hasattr(UpperCamelCase_ , "do_resize"))
self.assertTrue(hasattr(UpperCamelCase_ , "size"))
def a_ ( self : Dict):
"""simple docstring"""
pass
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
__UpperCAmelCase : str = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , Image.Image)
# Test not batched input
__UpperCAmelCase : Optional[int] = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Optional[int] = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : int = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
__UpperCAmelCase : Union[str, Any] = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_ , numpify=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , np.ndarray)
# Test not batched input
__UpperCAmelCase : Tuple = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Any = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
__UpperCAmelCase : Any = prepare_image_inputs(self.image_proc_tester , equal_resolution=UpperCamelCase_ , torchify=UpperCamelCase_)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , torch.Tensor)
# Test not batched input
__UpperCAmelCase : Optional[Any] = image_processor(image_inputs[0] , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
__UpperCAmelCase : Optional[int] = image_processor(UpperCamelCase_ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
| 77 | 1 |
"""simple docstring"""
from __future__ import annotations
A = list[list[int]]
# assigning initial values to the grid
A = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
A = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> bool:
"""simple docstring"""
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def _UpperCamelCase ( UpperCamelCase ) -> tuple[int, int] | None:
"""simple docstring"""
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def _UpperCamelCase ( UpperCamelCase ) -> Matrix | None:
"""simple docstring"""
if location := find_empty_location(UpperCamelCase ):
__UpperCAmelCase , __UpperCAmelCase : Dict = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
__UpperCAmelCase : Dict = digit
if sudoku(UpperCamelCase ) is not None:
return grid
__UpperCAmelCase : Optional[Any] = 0
return None
def _UpperCamelCase ( UpperCamelCase ) -> None:
"""simple docstring"""
for row in grid:
for cell in row:
print(UpperCamelCase , end=" " )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("""\nExample grid:\n""" + """=""" * 20)
print_solution(example_grid)
print("""\nExample grid solution:""")
A = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("""Cannot find a solution.""")
| 77 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": from_to(0.76455, 1.30795),
"""cubicfoot""": from_to(0.028, 35.3147),
"""cup""": from_to(0.000236588, 4226.75),
}
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> float:
"""simple docstring"""
if from_type not in METRIC_CONVERSION:
raise ValueError(
f"Invalid 'from_type' value: {from_type!r} Supported values are:\n"
+ ", ".join(UpperCamelCase ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
f"Invalid 'to_type' value: {to_type!r}. Supported values are:\n"
+ ", ".join(UpperCamelCase ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 77 | 1 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
__UpperCAmelCase : Any = 0
__UpperCAmelCase : Dict = 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Tuple = 0
# compute the shape of the output matrix
__UpperCAmelCase : Optional[int] = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
__UpperCAmelCase : List[str] = np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
__UpperCAmelCase : str = np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
__UpperCAmelCase : int = 0
__UpperCAmelCase : int = 0
return updated_arr
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : List[str] = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Any = 0
# compute the shape of the output matrix
__UpperCAmelCase : Tuple = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
__UpperCAmelCase : str = np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
__UpperCAmelCase : Tuple = int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Optional[Any] = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name="""avgpooling""", verbose=True)
# Loading the image
A = Image.open("""path_to_image""")
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 77 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = ShapEPipeline
lowercase_ = ["prompt"]
lowercase_ = ["prompt"]
lowercase_ = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
lowercase_ = False
@property
def a_ ( self : Optional[int]):
"""simple docstring"""
return 32
@property
def a_ ( self : Any):
"""simple docstring"""
return 32
@property
def a_ ( self : int):
"""simple docstring"""
return self.time_input_dim * 4
@property
def a_ ( self : List[Any]):
"""simple docstring"""
return 8
@property
def a_ ( self : List[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def a_ ( self : List[str]):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(UpperCamelCase_)
@property
def a_ ( self : Any):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Union[str, Any] = {
"num_attention_heads": 2,
"attention_head_dim": 16,
"embedding_dim": self.time_input_dim,
"num_embeddings": 32,
"embedding_proj_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"num_layers": 1,
"clip_embed_dim": self.time_input_dim * 2,
"additional_embeddings": 0,
"time_embed_act_fn": "gelu",
"norm_in_type": "layer",
"encoder_hid_proj_type": None,
"added_emb_type": None,
}
__UpperCAmelCase : Dict = PriorTransformer(**UpperCamelCase_)
return model
@property
def a_ ( self : Union[str, Any]):
"""simple docstring"""
torch.manual_seed(0)
__UpperCAmelCase : Tuple = {
"param_shapes": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"d_latent": self.time_input_dim,
"d_hidden": self.renderer_dim,
"n_output": 12,
"background": (
0.1,
0.1,
0.1,
),
}
__UpperCAmelCase : List[Any] = ShapERenderer(**UpperCamelCase_)
return model
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.dummy_prior
__UpperCAmelCase : str = self.dummy_text_encoder
__UpperCAmelCase : int = self.dummy_tokenizer
__UpperCAmelCase : int = self.dummy_renderer
__UpperCAmelCase : Tuple = HeunDiscreteScheduler(
beta_schedule="exp" , num_train_timesteps=1024 , prediction_type="sample" , use_karras_sigmas=UpperCamelCase_ , clip_sample=UpperCamelCase_ , clip_sample_range=1.0 , )
__UpperCAmelCase : str = {
"prior": prior,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"renderer": renderer,
"scheduler": scheduler,
}
return components
def a_ ( self : Optional[int] , UpperCamelCase_ : Dict , UpperCamelCase_ : Any=0):
"""simple docstring"""
if str(UpperCamelCase_).startswith("mps"):
__UpperCAmelCase : List[Any] = torch.manual_seed(UpperCamelCase_)
else:
__UpperCAmelCase : str = torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)
__UpperCAmelCase : List[Any] = {
"prompt": "horse",
"generator": generator,
"num_inference_steps": 1,
"frame_size": 32,
"output_type": "np",
}
return inputs
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : str = "cpu"
__UpperCAmelCase : Union[str, Any] = self.get_dummy_components()
__UpperCAmelCase : Union[str, Any] = self.pipeline_class(**UpperCamelCase_)
__UpperCAmelCase : Any = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = pipe(**self.get_dummy_inputs(UpperCamelCase_))
__UpperCAmelCase : Union[str, Any] = output.images[0]
__UpperCAmelCase : str = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__UpperCAmelCase : Union[str, Any] = np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def a_ ( self : Tuple):
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2])
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = torch_device == "cpu"
__UpperCAmelCase : Optional[Any] = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=UpperCamelCase_ , relax_max_difference=UpperCamelCase_ , )
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.get_dummy_components()
__UpperCAmelCase : List[str] = self.pipeline_class(**UpperCamelCase_)
__UpperCAmelCase : int = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Optional[int] = 1
__UpperCAmelCase : Any = 2
__UpperCAmelCase : Optional[Any] = self.get_dummy_inputs(UpperCamelCase_)
for key in inputs.keys():
if key in self.batch_params:
__UpperCAmelCase : List[Any] = batch_size * [inputs[key]]
__UpperCAmelCase : List[Any] = pipe(**UpperCamelCase_ , num_images_per_prompt=UpperCamelCase_)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def a_ ( self : List[str]):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : Dict = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/shap_e/test_shap_e_np_out.npy")
__UpperCAmelCase : Optional[Any] = ShapEPipeline.from_pretrained("openai/shap-e")
__UpperCAmelCase : Any = pipe.to(UpperCamelCase_)
pipe.set_progress_bar_config(disable=UpperCamelCase_)
__UpperCAmelCase : Dict = torch.Generator(device=UpperCamelCase_).manual_seed(0)
__UpperCAmelCase : int = pipe(
"a shark" , generator=UpperCamelCase_ , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type="np" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(UpperCamelCase_ , UpperCamelCase_)
| 77 | 1 |
"""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 typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class a__ ( __magic_name__ ):
lowercase_ = "Salesforce/blip-image-captioning-base"
lowercase_ = (
"This is a tool that generates a description of an image. It takes an input named `image` which should be the "
"image to caption, and returns a text that contains the description in English."
)
lowercase_ = "image_captioner"
lowercase_ = AutoModelForVisionaSeq
lowercase_ = ["image"]
lowercase_ = ["text"]
def __init__( self : Any , *UpperCamelCase_ : Optional[int] , **UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
requires_backends(self , ["vision"])
super().__init__(*UpperCamelCase_ , **UpperCamelCase_)
def a_ ( self : List[str] , UpperCamelCase_ : "Image"):
"""simple docstring"""
return self.pre_processor(images=UpperCamelCase_ , return_tensors="pt")
def a_ ( self : Optional[Any] , UpperCamelCase_ : Tuple):
"""simple docstring"""
return self.model.generate(**UpperCamelCase_)
def a_ ( self : Any , UpperCamelCase_ : Tuple):
"""simple docstring"""
return self.pre_processor.batch_decode(UpperCamelCase_ , skip_special_tokens=UpperCamelCase_)[0].strip()
| 77 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_features", "is_longer"]
def __init__( self : List[str] , UpperCamelCase_ : Dict=64 , UpperCamelCase_ : Tuple=48000 , UpperCamelCase_ : List[Any]=480 , UpperCamelCase_ : List[str]=10 , UpperCamelCase_ : str=1024 , UpperCamelCase_ : List[str]=0.0 , UpperCamelCase_ : Optional[int]=False , UpperCamelCase_ : float = 0 , UpperCamelCase_ : float = 14000 , UpperCamelCase_ : int = None , UpperCamelCase_ : str = "fusion" , UpperCamelCase_ : str = "repeatpad" , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
feature_size=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , padding_value=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Union[str, Any] = top_db
__UpperCAmelCase : Optional[Any] = truncation
__UpperCAmelCase : str = padding
__UpperCAmelCase : int = fft_window_size
__UpperCAmelCase : str = (fft_window_size >> 1) + 1
__UpperCAmelCase : List[Any] = hop_length
__UpperCAmelCase : Optional[Any] = max_length_s
__UpperCAmelCase : Tuple = max_length_s * sampling_rate
__UpperCAmelCase : str = sampling_rate
__UpperCAmelCase : int = frequency_min
__UpperCAmelCase : Optional[Any] = frequency_max
__UpperCAmelCase : Any = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCamelCase_ , min_frequency=UpperCamelCase_ , max_frequency=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , norm=UpperCamelCase_ , mel_scale="htk" , )
__UpperCAmelCase : Any = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=UpperCamelCase_ , min_frequency=UpperCamelCase_ , max_frequency=UpperCamelCase_ , sampling_rate=UpperCamelCase_ , norm="slaney" , mel_scale="slaney" , )
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Dict = copy.deepcopy(self.__dict__)
__UpperCAmelCase : str = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def a_ ( self : int , UpperCamelCase_ : np.array , UpperCamelCase_ : Optional[np.array] = None):
"""simple docstring"""
__UpperCAmelCase : List[Any] = spectrogram(
UpperCamelCase_ , window_function(self.fft_window_size , "hann") , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=UpperCamelCase_ , log_mel="dB" , )
return log_mel_spectrogram.T
def a_ ( self : List[Any] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = np.array_split(list(range(0 , total_frames - chunk_frames + 1)) , 3)
if len(ranges[1]) == 0:
# if the audio is too short, we just use the first chunk
__UpperCAmelCase : str = [0]
if len(ranges[2]) == 0:
# if the audio is too short, we just use the first chunk
__UpperCAmelCase : Dict = [0]
# randomly choose index for each part
__UpperCAmelCase : Dict = np.random.choice(ranges[0])
__UpperCAmelCase : List[str] = np.random.choice(ranges[1])
__UpperCAmelCase : List[Any] = np.random.choice(ranges[2])
__UpperCAmelCase : List[Any] = mel[idx_front : idx_front + chunk_frames, :]
__UpperCAmelCase : List[str] = mel[idx_middle : idx_middle + chunk_frames, :]
__UpperCAmelCase : List[str] = mel[idx_back : idx_back + chunk_frames, :]
__UpperCAmelCase : Tuple = torch.tensor(mel[None, None, :])
__UpperCAmelCase : Union[str, Any] = torch.nn.functional.interpolate(
UpperCamelCase_ , size=[chunk_frames, 64] , mode="bilinear" , align_corners=UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = mel_shrink[0][0].numpy()
__UpperCAmelCase : Optional[int] = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0)
return mel_fusion
def a_ ( self : Optional[Any] , UpperCamelCase_ : np.array , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
__UpperCAmelCase : List[str] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
__UpperCAmelCase : List[Any] = len(UpperCamelCase_) - max_length
__UpperCAmelCase : int = np.random.randint(0 , overflow + 1)
__UpperCAmelCase : Union[str, Any] = waveform[idx : idx + max_length]
__UpperCAmelCase : Union[str, Any] = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters_slaney)[None, :]
elif truncation == "fusion":
__UpperCAmelCase : Any = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters)
__UpperCAmelCase : Dict = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
__UpperCAmelCase : Tuple = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
__UpperCAmelCase : List[str] = np.stack([mel, mel, mel, mel] , axis=0)
__UpperCAmelCase : Any = False
else:
__UpperCAmelCase : List[str] = self._random_mel_fusion(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Union[str, Any] = True
else:
raise NotImplementedError(F"data_truncating {truncation} not implemented")
else:
__UpperCAmelCase : Optional[Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
__UpperCAmelCase : Tuple = int(max_length / len(UpperCamelCase_))
__UpperCAmelCase : List[str] = np.stack(np.tile(UpperCamelCase_ , n_repeat + 1))[:max_length]
if padding == "repeatpad":
__UpperCAmelCase : Union[str, Any] = int(max_length / len(UpperCamelCase_))
__UpperCAmelCase : Optional[Any] = np.stack(np.tile(UpperCamelCase_ , UpperCamelCase_))
__UpperCAmelCase : int = np.pad(UpperCamelCase_ , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0)
if truncation == "fusion":
__UpperCAmelCase : Any = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters)
__UpperCAmelCase : List[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0)
else:
__UpperCAmelCase : Optional[int] = self._np_extract_fbank_features(UpperCamelCase_ , self.mel_filters_slaney)[None, :]
return input_mel, longer
def __call__( self : Dict , UpperCamelCase_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , UpperCamelCase_ : str = None , UpperCamelCase_ : Optional[str] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[Union[str, TensorType]] = None , **UpperCamelCase_ : Any , ):
"""simple docstring"""
__UpperCAmelCase : int = truncation if truncation is not None else self.truncation
__UpperCAmelCase : Optional[Any] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
F" was sampled with {self.sampling_rate} and not {sampling_rate}.")
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug.")
__UpperCAmelCase : List[str] = isinstance(UpperCamelCase_ , np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(F"Only mono-channel audio is supported for input to {self}")
__UpperCAmelCase : str = is_batched_numpy or (
isinstance(UpperCamelCase_ , (list, tuple)) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)))
)
if is_batched:
__UpperCAmelCase : Dict = [np.asarray(UpperCamelCase_ , dtype=np.floataa) for speech in raw_speech]
elif not is_batched and not isinstance(UpperCamelCase_ , np.ndarray):
__UpperCAmelCase : Tuple = np.asarray(UpperCamelCase_ , dtype=np.floataa)
elif isinstance(UpperCamelCase_ , np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
__UpperCAmelCase : Optional[int] = raw_speech.astype(np.floataa)
# always return batch
if not is_batched:
__UpperCAmelCase : int = [np.asarray(UpperCamelCase_)]
# convert to mel spectrogram, truncate and pad if needed.
__UpperCAmelCase : Optional[int] = [
self._get_input_mel(UpperCamelCase_ , max_length if max_length else self.nb_max_samples , UpperCamelCase_ , UpperCamelCase_)
for waveform in raw_speech
]
__UpperCAmelCase : Tuple = []
__UpperCAmelCase : List[Any] = []
for mel, longer in padded_inputs:
input_mel.append(UpperCamelCase_)
is_longer.append(UpperCamelCase_)
if truncation == "fusion" and sum(UpperCamelCase_) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
__UpperCAmelCase : Any = np.random.randint(0 , len(UpperCamelCase_))
__UpperCAmelCase : Optional[int] = True
if isinstance(input_mel[0] , UpperCamelCase_):
__UpperCAmelCase : Tuple = [np.asarray(UpperCamelCase_ , dtype=np.floataa) for feature in input_mel]
# is_longer is a list of bool
__UpperCAmelCase : List[str] = [[longer] for longer in is_longer]
__UpperCAmelCase : Optional[int] = {"input_features": input_mel, "is_longer": is_longer}
__UpperCAmelCase : Optional[int] = BatchFeature(UpperCamelCase_)
if return_tensors is not None:
__UpperCAmelCase : Any = input_features.convert_to_tensors(UpperCamelCase_)
return input_features
| 77 | 1 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ( nn.Module ):
def __init__( self : Union[str, Any]):
"""simple docstring"""
super().__init__()
__UpperCAmelCase : Optional[int] = nn.Linear(3 , 4)
__UpperCAmelCase : str = nn.BatchNormad(4)
__UpperCAmelCase : int = nn.Linear(4 , 5)
def a_ ( self : str , UpperCamelCase_ : List[str]):
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(UpperCamelCase_)))
class a__ ( unittest.TestCase ):
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = ModelForTest()
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , model.state_dict())
__UpperCAmelCase : Union[str, Any] = os.path.join(UpperCamelCase_ , "index.json")
self.assertTrue(os.path.isfile(UpperCamelCase_))
# TODO: add tests on what is inside the index
for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
__UpperCAmelCase : Optional[int] = os.path.join(UpperCamelCase_ , F"{key}.dat")
self.assertTrue(os.path.isfile(UpperCamelCase_))
# TODO: add tests on the fact weights are properly loaded
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : int = [torch.floataa, torch.floataa, torch.bfloataa]
for dtype in dtypes:
__UpperCAmelCase : List[Any] = torch.randn(2 , 3 , dtype=UpperCamelCase_)
with TemporaryDirectory() as tmp_dir:
__UpperCAmelCase : Tuple = offload_weight(UpperCamelCase_ , "weight" , UpperCamelCase_ , {})
__UpperCAmelCase : Dict = os.path.join(UpperCamelCase_ , "weight.dat")
self.assertTrue(os.path.isfile(UpperCamelCase_))
self.assertDictEqual(UpperCamelCase_ , {"weight": {"shape": [2, 3], "dtype": str(UpperCamelCase_).split(".")[1]}})
__UpperCAmelCase : Optional[Any] = load_offloaded_weight(UpperCamelCase_ , index["weight"])
self.assertTrue(torch.equal(UpperCamelCase_ , UpperCamelCase_))
def a_ ( self : List[str]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = ModelForTest()
__UpperCAmelCase : Optional[int] = model.state_dict()
__UpperCAmelCase : List[str] = {k: v for k, v in state_dict.items() if "linear2" not in k}
__UpperCAmelCase : Optional[int] = {k: v for k, v in state_dict.items() if "linear2" in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : List[str] = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
__UpperCAmelCase : Optional[int] = {k: v for k, v in state_dict.items() if "weight" in k}
__UpperCAmelCase : Optional[Any] = {k: v for k, v in state_dict.items() if "weight" not in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase_ , UpperCamelCase_)
# Duplicates are removed
__UpperCAmelCase : str = OffloadedWeightsLoader(state_dict=UpperCamelCase_ , save_folder=UpperCamelCase_)
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase_) , sorted(state_dict.keys()))
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase_ , weight_map[key]))
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Any = {"a.1": 0, "a.10": 1, "a.2": 2}
__UpperCAmelCase : Union[str, Any] = extract_submodules_state_dict(UpperCamelCase_ , ["a.1", "a.2"])
self.assertDictEqual(UpperCamelCase_ , {"a.1": 0, "a.2": 2})
__UpperCAmelCase : int = {"a.1.a": 0, "a.10.a": 1, "a.2.a": 2}
__UpperCAmelCase : int = extract_submodules_state_dict(UpperCamelCase_ , ["a.1", "a.2"])
self.assertDictEqual(UpperCamelCase_ , {"a.1.a": 0, "a.2.a": 2})
| 77 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
class a__ ( __magic_name__ ):
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : Optional[Any] , UpperCamelCase_ : List[Any]="</s>" , UpperCamelCase_ : Tuple="<unk>" , UpperCamelCase_ : List[str]="<pad>" , UpperCamelCase_ : Union[str, Any]=125 , UpperCamelCase_ : Dict=None , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
if extra_ids > 0 and additional_special_tokens is None:
__UpperCAmelCase : int = [F"<extra_id_{i}>" for i in range(UpperCamelCase_)]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__UpperCAmelCase : Dict = len(set(filter(lambda UpperCamelCase_: bool("extra_id" in str(UpperCamelCase_)) , UpperCamelCase_)))
if extra_tokens != extra_ids:
raise ValueError(
F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
" extra_ids tokens")
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else pad_token
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else eos_token
__UpperCAmelCase : Optional[int] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_ , UpperCamelCase_) else unk_token
super().__init__(
eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , extra_ids=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : List[str] = extra_ids
__UpperCAmelCase : int = 2**8 # utf is 8 bits
# define special tokens dict
__UpperCAmelCase : Dict[int, str] = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
__UpperCAmelCase : Any = len(self.special_tokens_encoder)
__UpperCAmelCase : List[Any] = len(UpperCamelCase_)
for i, token in enumerate(UpperCamelCase_):
__UpperCAmelCase : Union[str, Any] = self.vocab_size + i - n
__UpperCAmelCase : Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def a_ ( self : List[Any]):
"""simple docstring"""
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def a_ ( self : List[str] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase_ , token_ids_a=UpperCamelCase_ , already_has_special_tokens=UpperCamelCase_)
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(UpperCamelCase_)) + [1]
return ([0] * len(UpperCamelCase_)) + [1] + ([0] * len(UpperCamelCase_)) + [1]
def a_ ( self : Optional[Any] , UpperCamelCase_ : List[int]):
"""simple docstring"""
if len(UpperCamelCase_) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
" eos tokens being added.")
return token_ids
else:
return token_ids + [self.eos_token_id]
def a_ ( self : Dict , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Dict = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos) * [0]
return len(token_ids_a + eos + token_ids_a + eos) * [0]
def a_ ( self : Optional[int] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self._add_eos_if_not_present(UpperCamelCase_)
if token_ids_a is None:
return token_ids_a
else:
__UpperCAmelCase : List[Any] = self._add_eos_if_not_present(UpperCamelCase_)
return token_ids_a + token_ids_a
def a_ ( self : List[str] , UpperCamelCase_ : str):
"""simple docstring"""
__UpperCAmelCase : Any = [chr(UpperCamelCase_) for i in text.encode("utf-8")]
return tokens
def a_ ( self : Tuple , UpperCamelCase_ : List[Any]):
"""simple docstring"""
if token in self.special_tokens_encoder:
__UpperCAmelCase : Any = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
__UpperCAmelCase : int = self.added_tokens_encoder[token]
elif len(UpperCamelCase_) != 1:
__UpperCAmelCase : Optional[Any] = self.unk_token_id
else:
__UpperCAmelCase : Any = ord(UpperCamelCase_) + self._num_special_tokens
return token_id
def a_ ( self : Any , UpperCamelCase_ : List[str]):
"""simple docstring"""
if index in self.special_tokens_decoder:
__UpperCAmelCase : Any = self.special_tokens_decoder[index]
else:
__UpperCAmelCase : List[str] = chr(index - self._num_special_tokens)
return token
def a_ ( self : Dict , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : str = b""
for token in tokens:
if token in self.special_tokens_decoder:
__UpperCAmelCase : Tuple = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.added_tokens_decoder:
__UpperCAmelCase : Any = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.special_tokens_encoder:
__UpperCAmelCase : Optional[int] = token.encode("utf-8")
elif token in self.added_tokens_encoder:
__UpperCAmelCase : Optional[Any] = token.encode("utf-8")
else:
__UpperCAmelCase : Any = bytes([ord(UpperCamelCase_)])
bstring += tok_string
__UpperCAmelCase : List[Any] = bstring.decode("utf-8" , errors="ignore")
return string
def a_ ( self : Optional[int] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
return ()
| 77 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"""
),
}
class a__ ( __magic_name__ ):
lowercase_ = "gptsan-japanese"
lowercase_ = [
"past_key_values",
]
lowercase_ = {
"hidden_size": "d_model",
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : Any , UpperCamelCase_ : List[Any]=36000 , UpperCamelCase_ : Optional[Any]=1280 , UpperCamelCase_ : Optional[int]=1024 , UpperCamelCase_ : Dict=8192 , UpperCamelCase_ : Optional[int]=4096 , UpperCamelCase_ : Union[str, Any]=128 , UpperCamelCase_ : Dict=10 , UpperCamelCase_ : Optional[Any]=0 , UpperCamelCase_ : Union[str, Any]=16 , UpperCamelCase_ : List[Any]=16 , UpperCamelCase_ : List[str]=128 , UpperCamelCase_ : List[str]=0.0 , UpperCamelCase_ : str=1e-5 , UpperCamelCase_ : Tuple=False , UpperCamelCase_ : Dict=0.0 , UpperCamelCase_ : Tuple="float32" , UpperCamelCase_ : Optional[Any]=False , UpperCamelCase_ : Optional[Any]=False , UpperCamelCase_ : Optional[int]=False , UpperCamelCase_ : int=0.002 , UpperCamelCase_ : List[str]=False , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : List[Any]=35998 , UpperCamelCase_ : str=35995 , UpperCamelCase_ : Optional[int]=35999 , **UpperCamelCase_ : Dict , ):
"""simple docstring"""
__UpperCAmelCase : Tuple = vocab_size
__UpperCAmelCase : Union[str, Any] = max_position_embeddings
__UpperCAmelCase : str = d_model
__UpperCAmelCase : Optional[Any] = d_ff
__UpperCAmelCase : Optional[Any] = d_ext
__UpperCAmelCase : Any = d_spout
__UpperCAmelCase : Dict = num_switch_layers
__UpperCAmelCase : Any = num_ext_layers
__UpperCAmelCase : List[str] = num_switch_layers + num_ext_layers
__UpperCAmelCase : List[str] = num_heads
__UpperCAmelCase : Optional[int] = num_experts
__UpperCAmelCase : List[Any] = expert_capacity
__UpperCAmelCase : List[str] = dropout_rate
__UpperCAmelCase : Union[str, Any] = layer_norm_epsilon
__UpperCAmelCase : List[Any] = router_bias
__UpperCAmelCase : Tuple = router_jitter_noise
__UpperCAmelCase : Any = router_dtype
__UpperCAmelCase : List[str] = router_ignore_padding_tokens
__UpperCAmelCase : int = output_hidden_states
__UpperCAmelCase : Dict = output_attentions
__UpperCAmelCase : List[Any] = initializer_factor
__UpperCAmelCase : Dict = output_router_logits
__UpperCAmelCase : Optional[int] = use_cache
super().__init__(
separator_token_id=UpperCamelCase_ , pad_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_ , )
| 77 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self : List[Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple=3 , UpperCamelCase_ : Optional[int]=32 , UpperCamelCase_ : Dict=3 , UpperCamelCase_ : List[str]=10 , UpperCamelCase_ : str=[10, 20, 30, 40] , UpperCamelCase_ : Tuple=[1, 1, 2, 1] , UpperCamelCase_ : str=True , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Dict="relu" , UpperCamelCase_ : str=3 , UpperCamelCase_ : int=None , ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = parent
__UpperCAmelCase : List[str] = batch_size
__UpperCAmelCase : List[str] = image_size
__UpperCAmelCase : Tuple = num_channels
__UpperCAmelCase : Union[str, Any] = embeddings_size
__UpperCAmelCase : Dict = hidden_sizes
__UpperCAmelCase : Dict = depths
__UpperCAmelCase : Tuple = is_training
__UpperCAmelCase : List[Any] = use_labels
__UpperCAmelCase : Optional[int] = hidden_act
__UpperCAmelCase : str = num_labels
__UpperCAmelCase : Optional[int] = scope
__UpperCAmelCase : Dict = len(UpperCamelCase_)
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__UpperCAmelCase : Dict = self.get_config()
return config, pixel_values
def a_ ( self : Dict):
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def a_ ( self : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : List[str] = FlaxRegNetModel(config=UpperCamelCase_)
__UpperCAmelCase : Dict = model(UpperCamelCase_)
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def a_ ( self : Any , UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.num_labels
__UpperCAmelCase : Tuple = FlaxRegNetForImageClassification(config=UpperCamelCase_)
__UpperCAmelCase : str = model(UpperCamelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Any = self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase : Tuple = config_and_inputs
__UpperCAmelCase : Optional[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
lowercase_ = False
lowercase_ = False
lowercase_ = False
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = FlaxRegNetModelTester(self)
__UpperCAmelCase : str = ConfigTester(self , config_class=UpperCamelCase_ , has_text_modality=UpperCamelCase_)
def a_ ( self : Dict):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def a_ ( self : Tuple):
"""simple docstring"""
return
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_)
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_)
@unittest.skip(reason="RegNet does not use inputs_embeds")
def a_ ( self : Union[str, Any]):
"""simple docstring"""
pass
@unittest.skip(reason="RegNet does not support input and output embeddings")
def a_ ( self : Optional[int]):
"""simple docstring"""
pass
def a_ ( self : str):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : int = model_class(UpperCamelCase_)
__UpperCAmelCase : Optional[int] = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase : Any = [*signature.parameters.keys()]
__UpperCAmelCase : Dict = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCamelCase_)
def a_ ( self : int):
"""simple docstring"""
def check_hidden_states_output(UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Union[str, Any]):
__UpperCAmelCase : Union[str, Any] = model_class(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_))
__UpperCAmelCase : List[str] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__UpperCAmelCase : str = self.model_tester.num_stages
self.assertEqual(len(UpperCamelCase_) , expected_num_stages + 1)
__UpperCAmelCase , __UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : List[str] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase : Optional[int] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
def a_ ( self : Tuple):
"""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 : List[Any] = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_)
__UpperCAmelCase : Optional[int] = model_class(UpperCamelCase_)
@jax.jit
def model_jitted(UpperCamelCase_ : int , **UpperCamelCase_ : Optional[int]):
return model(pixel_values=UpperCamelCase_ , **UpperCamelCase_)
with self.subTest("JIT Enabled"):
__UpperCAmelCase : Optional[Any] = model_jitted(**UpperCamelCase_).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
__UpperCAmelCase : Dict = model_jitted(**UpperCamelCase_).to_tuple()
self.assertEqual(len(UpperCamelCase_) , len(UpperCamelCase_))
for jitted_output, output in zip(UpperCamelCase_ , UpperCamelCase_):
self.assertEqual(jitted_output.shape , output.shape)
def _UpperCamelCase ( ) -> Any:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_flax
class a__ ( unittest.TestCase ):
@cached_property
def a_ ( self : Optional[int]):
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/regnet-y-040") if is_vision_available() else None
@slow
def a_ ( self : int):
"""simple docstring"""
__UpperCAmelCase : Any = FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")
__UpperCAmelCase : Dict = self.default_image_processor
__UpperCAmelCase : str = prepare_img()
__UpperCAmelCase : int = image_processor(images=UpperCamelCase_ , return_tensors="np")
__UpperCAmelCase : Dict = model(**UpperCamelCase_)
# verify the logits
__UpperCAmelCase : Dict = (1, 1000)
self.assertEqual(outputs.logits.shape , UpperCamelCase_)
__UpperCAmelCase : Any = jnp.array([-0.4180, -1.5051, -3.4836])
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , UpperCamelCase_ , atol=1e-4))
| 77 | 1 |
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 77 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
A = """
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results['spearmanr'])
-0.7
>>> print(round(results['spearmanr_pvalue'], 2))
0.19
"""
A = r"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
def a_ ( self : Any):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("float"),
"references": datasets.Value("float"),
}) , reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"] , )
def a_ ( self : Union[str, Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : int=False):
"""simple docstring"""
__UpperCAmelCase : List[str] = spearmanr(UpperCamelCase_ , UpperCamelCase_)
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 77 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
from transformers.models.mobilevitva.modeling_mobilevitva import (
MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
make_divisible,
)
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class a__ ( __magic_name__ ):
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : str = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(UpperCamelCase_ , "width_multiplier"))
class a__ :
def __init__( self : Any , UpperCamelCase_ : str , UpperCamelCase_ : str=13 , UpperCamelCase_ : Optional[Any]=64 , UpperCamelCase_ : Tuple=2 , UpperCamelCase_ : Optional[int]=3 , UpperCamelCase_ : Dict="swish" , UpperCamelCase_ : Tuple=3 , UpperCamelCase_ : Optional[Any]=32 , UpperCamelCase_ : Dict=0.1 , UpperCamelCase_ : Any=0.02 , UpperCamelCase_ : Union[str, Any]=True , UpperCamelCase_ : int=True , UpperCamelCase_ : Union[str, Any]=10 , UpperCamelCase_ : Union[str, Any]=None , UpperCamelCase_ : Optional[int]=0.25 , UpperCamelCase_ : Optional[Any]=0.0 , UpperCamelCase_ : Optional[Any]=0.0 , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = parent
__UpperCAmelCase : Tuple = batch_size
__UpperCAmelCase : Union[str, Any] = image_size
__UpperCAmelCase : Any = patch_size
__UpperCAmelCase : int = num_channels
__UpperCAmelCase : Optional[Any] = make_divisible(512 * width_multiplier , divisor=8)
__UpperCAmelCase : Union[str, Any] = hidden_act
__UpperCAmelCase : Union[str, Any] = conv_kernel_size
__UpperCAmelCase : List[Any] = output_stride
__UpperCAmelCase : int = classifier_dropout_prob
__UpperCAmelCase : Any = use_labels
__UpperCAmelCase : List[Any] = is_training
__UpperCAmelCase : Any = num_labels
__UpperCAmelCase : str = initializer_range
__UpperCAmelCase : Union[str, Any] = scope
__UpperCAmelCase : int = width_multiplier
__UpperCAmelCase : Optional[int] = ffn_dropout
__UpperCAmelCase : Optional[int] = attn_dropout
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__UpperCAmelCase : str = None
__UpperCAmelCase : Optional[Any] = None
if self.use_labels:
__UpperCAmelCase : List[Any] = ids_tensor([self.batch_size] , self.num_labels)
__UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels)
__UpperCAmelCase : Any = self.get_config()
return config, pixel_values, labels, pixel_labels
def a_ ( self : Union[str, Any]):
"""simple docstring"""
return MobileViTVaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , width_multiplier=self.width_multiplier , ffn_dropout=self.ffn_dropout_prob , attn_dropout=self.attn_dropout_prob , )
def a_ ( self : Optional[int] , UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : str):
"""simple docstring"""
__UpperCAmelCase : str = MobileViTVaModel(config=UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
__UpperCAmelCase : Union[str, Any] = model(UpperCamelCase_)
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def a_ ( self : List[str] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : str , UpperCamelCase_ : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Dict = self.num_labels
__UpperCAmelCase : int = MobileViTVaForImageClassification(UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
__UpperCAmelCase : Any = model(UpperCamelCase_ , labels=UpperCamelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def a_ ( self : int , UpperCamelCase_ : List[str] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Dict , UpperCamelCase_ : Tuple):
"""simple docstring"""
__UpperCAmelCase : Any = self.num_labels
__UpperCAmelCase : List[str] = MobileViTVaForSemanticSegmentation(UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
__UpperCAmelCase : Tuple = model(UpperCamelCase_)
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
__UpperCAmelCase : Union[str, Any] = model(UpperCamelCase_ , labels=UpperCamelCase_)
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : str = self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Tuple = config_and_inputs
__UpperCAmelCase : Any = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class a__ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase_ = (
(MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
if is_torch_available()
else ()
)
lowercase_ = (
{
"feature-extraction": MobileViTVaModel,
"image-classification": MobileViTVaForImageClassification,
"image-segmentation": MobileViTVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowercase_ = False
lowercase_ = False
lowercase_ = False
lowercase_ = False
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Dict = MobileViTVaModelTester(self)
__UpperCAmelCase : List[Any] = MobileViTVaConfigTester(self , config_class=UpperCamelCase_ , has_text_modality=UpperCamelCase_)
def a_ ( self : Union[str, Any]):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileViTV2 does not use inputs_embeds")
def a_ ( self : Optional[int]):
"""simple docstring"""
pass
@unittest.skip(reason="MobileViTV2 does not support input and output embeddings")
def a_ ( self : List[Any]):
"""simple docstring"""
pass
@unittest.skip(reason="MobileViTV2 does not output attentions")
def a_ ( self : List[Any]):
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run.")
def a_ ( self : str):
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def a_ ( self : List[str]):
"""simple docstring"""
pass
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : List[Any] = model_class(UpperCamelCase_)
__UpperCAmelCase : Dict = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase : List[Any] = [*signature.parameters.keys()]
__UpperCAmelCase : int = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCamelCase_)
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_)
def a_ ( self : Optional[int]):
"""simple docstring"""
def check_hidden_states_output(UpperCamelCase_ : str , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[int]):
__UpperCAmelCase : str = model_class(UpperCamelCase_)
model.to(UpperCamelCase_)
model.eval()
with torch.no_grad():
__UpperCAmelCase : Dict = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_))
__UpperCAmelCase : Tuple = outputs.hidden_states
__UpperCAmelCase : Tuple = 5
self.assertEqual(len(UpperCamelCase_) , UpperCamelCase_)
# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
__UpperCAmelCase : Dict = 2
for i in range(len(UpperCamelCase_)):
self.assertListEqual(
list(hidden_states[i].shape[-2:]) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2)
__UpperCAmelCase , __UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase : Union[str, Any] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase : Union[str, Any] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_)
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_)
@slow
def a_ ( self : Dict):
"""simple docstring"""
for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase : List[str] = MobileViTVaModel.from_pretrained(UpperCamelCase_)
self.assertIsNotNone(UpperCamelCase_)
def _UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : Optional[int] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class a__ ( unittest.TestCase ):
@cached_property
def a_ ( self : Optional[int]):
"""simple docstring"""
return (
MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
if is_vision_available()
else None
)
@slow
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256").to(
UpperCamelCase_)
__UpperCAmelCase : List[str] = self.default_image_processor
__UpperCAmelCase : str = prepare_img()
__UpperCAmelCase : Union[str, Any] = image_processor(images=UpperCamelCase_ , return_tensors="pt").to(UpperCamelCase_)
# forward pass
with torch.no_grad():
__UpperCAmelCase : Union[str, Any] = model(**UpperCamelCase_)
# verify the logits
__UpperCAmelCase : str = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape , UpperCamelCase_)
__UpperCAmelCase : List[str] = torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]).to(UpperCamelCase_)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCamelCase_ , atol=1e-4))
@slow
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
__UpperCAmelCase : int = model.to(UpperCamelCase_)
__UpperCAmelCase : Dict = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
__UpperCAmelCase : Dict = prepare_img()
__UpperCAmelCase : Optional[Any] = image_processor(images=UpperCamelCase_ , return_tensors="pt").to(UpperCamelCase_)
# forward pass
with torch.no_grad():
__UpperCAmelCase : List[Any] = model(**UpperCamelCase_)
__UpperCAmelCase : Tuple = outputs.logits
# verify the logits
__UpperCAmelCase : int = torch.Size((1, 21, 32, 32))
self.assertEqual(logits.shape , UpperCamelCase_)
__UpperCAmelCase : List[str] = torch.tensor(
[
[[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
[[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
[[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
] , device=UpperCamelCase_ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , UpperCamelCase_ , atol=1e-4))
@slow
def a_ ( self : Dict):
"""simple docstring"""
__UpperCAmelCase : List[str] = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
__UpperCAmelCase : Union[str, Any] = model.to(UpperCamelCase_)
__UpperCAmelCase : int = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
__UpperCAmelCase : List[str] = prepare_img()
__UpperCAmelCase : Dict = image_processor(images=UpperCamelCase_ , return_tensors="pt").to(UpperCamelCase_)
# forward pass
with torch.no_grad():
__UpperCAmelCase : List[str] = model(**UpperCamelCase_)
__UpperCAmelCase : int = outputs.logits.detach().cpu()
__UpperCAmelCase : int = image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_ , target_sizes=[(50, 60)])
__UpperCAmelCase : Tuple = torch.Size((50, 60))
self.assertEqual(segmentation[0].shape , UpperCamelCase_)
__UpperCAmelCase : Any = image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_)
__UpperCAmelCase : Tuple = torch.Size((32, 32))
self.assertEqual(segmentation[0].shape , UpperCamelCase_)
| 77 |
"""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""": """spiece.model"""}
A = {
"""vocab_file""": {
"""bert_for_seq_generation""": (
"""https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
),
}
}
A = {"""bert_for_seq_generation""": 512}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = []
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : List[str] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : List[str]="<s>" , UpperCamelCase_ : Optional[Any]="</s>" , UpperCamelCase_ : Optional[int]="<unk>" , UpperCamelCase_ : int="<pad>" , UpperCamelCase_ : List[Any]="<::::>" , UpperCamelCase_ : Optional[Dict[str, Any]] = None , **UpperCamelCase_ : List[Any] , ):
"""simple docstring"""
__UpperCAmelCase : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase_ , )
__UpperCAmelCase : Dict = vocab_file
__UpperCAmelCase : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(UpperCamelCase_)
@property
def a_ ( self : List[str]):
"""simple docstring"""
return self.sp_model.get_piece_size()
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : int = {self.convert_ids_to_tokens(UpperCamelCase_): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self : int):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = self.__dict__.copy()
__UpperCAmelCase : List[Any] = None
return state
def __setstate__( self : Optional[Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs"):
__UpperCAmelCase : List[Any] = {}
__UpperCAmelCase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def a_ ( self : Any , UpperCamelCase_ : str):
"""simple docstring"""
return self.sp_model.encode(UpperCamelCase_ , out_type=UpperCamelCase_)
def a_ ( self : Optional[Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
return self.sp_model.piece_to_id(UpperCamelCase_)
def a_ ( self : Tuple , UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : int = self.sp_model.IdToPiece(UpperCamelCase_)
return token
def a_ ( self : Dict , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : int = []
__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(UpperCamelCase_) + token
__UpperCAmelCase : List[Any] = []
else:
current_sub_tokens.append(UpperCamelCase_)
out_string += self.sp_model.decode(UpperCamelCase_)
return out_string.strip()
def a_ ( self : List[str] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : Tuple = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , UpperCamelCase_)
elif not os.path.isfile(self.vocab_file):
with open(UpperCamelCase_ , "wb") as fi:
__UpperCAmelCase : List[str] = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase_)
return (out_vocab_file,)
| 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> float:
"""simple docstring"""
__UpperCAmelCase : List[Any] = 0
while len(UpperCamelCase ) > 1:
__UpperCAmelCase : Optional[int] = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
__UpperCAmelCase : Dict = files.index(min(UpperCamelCase ) )
temp += files[min_index]
files.pop(UpperCamelCase )
files.append(UpperCamelCase )
optimal_merge_cost += temp
return optimal_merge_cost
if __name__ == "__main__":
import doctest
doctest.testmod()
| 77 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
A = """true"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=82 , UpperCamelCase=16 ) -> Tuple:
"""simple docstring"""
set_seed(42 )
__UpperCAmelCase : Dict = RegressionModel()
__UpperCAmelCase : Optional[Any] = deepcopy(UpperCamelCase )
__UpperCAmelCase : Any = RegressionDataset(length=UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = DataLoader(UpperCamelCase , batch_size=UpperCamelCase )
model.to(accelerator.device )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = accelerator.prepare(UpperCamelCase , UpperCamelCase )
return model, ddp_model, dataloader
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=False ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : List[str] = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
__UpperCAmelCase : Dict = load_dataset("glue" , "mrpc" , split="validation" )
def tokenize_function(UpperCamelCase ):
__UpperCAmelCase : Dict = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=UpperCamelCase , max_length=UpperCamelCase )
return outputs
with accelerator.main_process_first():
__UpperCAmelCase : str = dataset.map(
UpperCamelCase , batched=UpperCamelCase , remove_columns=["idx", "sentence1", "sentence2"] , )
__UpperCAmelCase : List[Any] = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(UpperCamelCase ):
if use_longest:
return tokenizer.pad(UpperCamelCase , padding="longest" , return_tensors="pt" )
return tokenizer.pad(UpperCamelCase , padding="max_length" , max_length=128 , return_tensors="pt" )
return DataLoader(UpperCamelCase , shuffle=UpperCamelCase , collate_fn=UpperCamelCase , batch_size=16 )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase : List[Any] = Accelerator(dispatch_batches=UpperCamelCase , split_batches=UpperCamelCase )
__UpperCAmelCase : int = get_dataloader(UpperCamelCase , not dispatch_batches )
__UpperCAmelCase : Any = AutoModelForSequenceClassification.from_pretrained(
"hf-internal-testing/mrpc-bert-base-cased" , return_dict=UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Dict = accelerator.prepare(UpperCamelCase , UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase : Dict = []
for batch in dataloader:
__UpperCAmelCase , __UpperCAmelCase : int = batch.values()
with torch.no_grad():
__UpperCAmelCase : int = model(UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : List[str] = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = [], []
for logit, targ in logits_and_targets:
logits.append(UpperCamelCase )
targs.append(UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = torch.cat(UpperCamelCase ), torch.cat(UpperCamelCase )
return logits, targs
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase=82 , UpperCamelCase=False , UpperCamelCase=False , UpperCamelCase=16 ) -> int:
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = get_basic_setup(UpperCamelCase , UpperCamelCase , UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = generate_predictions(UpperCamelCase , UpperCamelCase , UpperCamelCase )
assert (
len(UpperCamelCase ) == num_samples
), f"Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(UpperCamelCase )}"
def _UpperCamelCase ( UpperCamelCase = False , UpperCamelCase = False ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : List[str] = evaluate.load("glue" , "mrpc" )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = get_mrpc_setup(UpperCamelCase , UpperCamelCase )
# First do baseline
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Tuple = setup["no"]
model.to(UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(UpperCamelCase )
with torch.inference_mode():
__UpperCAmelCase : List[str] = model(**UpperCamelCase )
__UpperCAmelCase : str = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=UpperCamelCase , references=batch["labels"] )
__UpperCAmelCase : str = metric.compute()
# Then do distributed
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
__UpperCAmelCase : Any = model(**UpperCamelCase )
__UpperCAmelCase : str = outputs.logits.argmax(dim=-1 )
__UpperCAmelCase : Union[str, Any] = batch["labels"]
__UpperCAmelCase , __UpperCAmelCase : Any = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=UpperCamelCase , references=UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"
def _UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
__UpperCAmelCase : Dict = Accelerator(split_batches=UpperCamelCase , dispatch_batches=UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("**Testing gather_for_metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`" )
test_mrpc(UpperCamelCase , UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test torch metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__UpperCAmelCase : Union[str, Any] = Accelerator(split_batches=UpperCamelCase , dispatch_batches=UpperCamelCase )
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99" )
test_torch_metrics(UpperCamelCase , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test last batch is not dropped when perfectly divisible**" )
__UpperCAmelCase : Any = Accelerator()
test_torch_metrics(UpperCamelCase , 512 )
accelerator.state._reset_state()
def _UpperCamelCase ( UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 77 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : list[list[str]] = [[] for _ in range(UpperCamelCase )]
__UpperCAmelCase : Union[str, Any] = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative" )
if key == 1 or len(UpperCamelCase ) <= key:
return input_string
for position, character in enumerate(UpperCamelCase ):
__UpperCAmelCase : Dict = position % (lowest * 2) # puts it in bounds
__UpperCAmelCase : List[str] = min(UpperCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = ["".join(UpperCamelCase ) for row in temp_grid]
__UpperCAmelCase : Any = "".join(UpperCamelCase )
return output_string
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : Tuple = []
__UpperCAmelCase : Union[str, Any] = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative" )
if key == 1:
return input_string
__UpperCAmelCase : list[list[str]] = [[] for _ in range(UpperCamelCase )] # generates template
for position in range(len(UpperCamelCase ) ):
__UpperCAmelCase : Optional[int] = position % (lowest * 2) # puts it in bounds
__UpperCAmelCase : str = min(UpperCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("*" )
__UpperCAmelCase : Union[str, Any] = 0
for row in temp_grid: # fills in the characters
__UpperCAmelCase : Tuple = input_string[counter : counter + len(UpperCamelCase )]
grid.append(list(UpperCamelCase ) )
counter += len(UpperCamelCase )
__UpperCAmelCase : List[str] = "" # reads as zigzag
for position in range(len(UpperCamelCase ) ):
__UpperCAmelCase : Dict = position % (lowest * 2) # puts it in bounds
__UpperCAmelCase : Union[str, Any] = min(UpperCamelCase , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def _UpperCamelCase ( UpperCamelCase ) -> dict[int, str]:
"""simple docstring"""
__UpperCAmelCase : Tuple = {}
for key_guess in range(1 , len(UpperCamelCase ) ): # tries every key
__UpperCAmelCase : str = decrypt(UpperCamelCase , UpperCamelCase )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 77 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = 0 , UpperCamelCase = 0 ) -> list:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = end or len(UpperCamelCase )
for i in range(UpperCamelCase , UpperCamelCase ):
__UpperCAmelCase : List[Any] = i
__UpperCAmelCase : Any = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
__UpperCAmelCase : Dict = array[temp_index - 1]
temp_index -= 1
__UpperCAmelCase : str = temp_index_value
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> None: # Max Heap
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = index
__UpperCAmelCase : List[str] = 2 * index + 1 # Left Node
__UpperCAmelCase : Union[str, Any] = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
__UpperCAmelCase : Tuple = left_index
if right_index < heap_size and array[largest] < array[right_index]:
__UpperCAmelCase : int = right_index
if largest != index:
__UpperCAmelCase , __UpperCAmelCase : List[str] = array[largest], array[index]
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
__UpperCAmelCase : List[Any] = len(UpperCamelCase )
for i in range(n // 2 , -1 , -1 ):
heapify(UpperCamelCase , UpperCamelCase , UpperCamelCase )
for i in range(n - 1 , 0 , -1 ):
__UpperCAmelCase , __UpperCAmelCase : int = array[0], array[i]
heapify(UpperCamelCase , 0 , UpperCamelCase )
return array
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = low
__UpperCAmelCase : List[str] = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = array[j], array[i]
i += 1
def _UpperCamelCase ( UpperCamelCase ) -> list:
"""simple docstring"""
if len(UpperCamelCase ) == 0:
return array
__UpperCAmelCase : Optional[int] = 2 * math.ceil(math.loga(len(UpperCamelCase ) ) )
__UpperCAmelCase : List[Any] = 16
return intro_sort(UpperCamelCase , 0 , len(UpperCamelCase ) , UpperCamelCase , UpperCamelCase )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> list:
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(UpperCamelCase )
max_depth -= 1
__UpperCAmelCase : List[Any] = median_of_a(UpperCamelCase , UpperCamelCase , start + ((end - start) // 2) + 1 , end - 1 )
__UpperCAmelCase : Union[str, Any] = partition(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
intro_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Optional[Any] = p
return insertion_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
A = input("""Enter numbers separated by a comma : """).strip()
A = [float(item) for item in user_input.split(""",""")]
print(sort(unsorted))
| 77 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> list:
"""simple docstring"""
if len(UpperCamelCase ) != 2 or len(a[0] ) != 2 or len(UpperCamelCase ) != 2 or len(b[0] ) != 2:
raise Exception("Matrices are not 2x2" )
__UpperCAmelCase : Optional[Any] = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Dict:
"""simple docstring"""
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(UpperCamelCase ) )
]
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(UpperCamelCase ) )
]
def _UpperCamelCase ( UpperCamelCase ) -> tuple[list, list, list, list]:
"""simple docstring"""
if len(UpperCamelCase ) % 2 != 0 or len(a[0] ) % 2 != 0:
raise Exception("Odd matrices are not supported!" )
__UpperCAmelCase : List[str] = len(UpperCamelCase )
__UpperCAmelCase : int = matrix_length // 2
__UpperCAmelCase : Tuple = [[a[i][j] for j in range(UpperCamelCase , UpperCamelCase )] for i in range(UpperCamelCase )]
__UpperCAmelCase : Dict = [
[a[i][j] for j in range(UpperCamelCase , UpperCamelCase )] for i in range(UpperCamelCase , UpperCamelCase )
]
__UpperCAmelCase : List[Any] = [[a[i][j] for j in range(UpperCamelCase )] for i in range(UpperCamelCase )]
__UpperCAmelCase : Any = [[a[i][j] for j in range(UpperCamelCase )] for i in range(UpperCamelCase , UpperCamelCase )]
return top_left, top_right, bot_left, bot_right
def _UpperCamelCase ( UpperCamelCase ) -> tuple[int, int]:
"""simple docstring"""
return len(UpperCamelCase ), len(matrix[0] )
def _UpperCamelCase ( UpperCamelCase ) -> None:
"""simple docstring"""
print("\n".join(str(UpperCamelCase ) for line in matrix ) )
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> list:
"""simple docstring"""
if matrix_dimensions(UpperCamelCase ) == (2, 2):
return default_matrix_multiplication(UpperCamelCase , UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = split_matrix(UpperCamelCase )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Tuple = split_matrix(UpperCamelCase )
__UpperCAmelCase : Dict = actual_strassen(UpperCamelCase , matrix_subtraction(UpperCamelCase , UpperCamelCase ) )
__UpperCAmelCase : str = actual_strassen(matrix_addition(UpperCamelCase , UpperCamelCase ) , UpperCamelCase )
__UpperCAmelCase : Optional[Any] = actual_strassen(matrix_addition(UpperCamelCase , UpperCamelCase ) , UpperCamelCase )
__UpperCAmelCase : Optional[Any] = actual_strassen(UpperCamelCase , matrix_subtraction(UpperCamelCase , UpperCamelCase ) )
__UpperCAmelCase : Optional[Any] = actual_strassen(matrix_addition(UpperCamelCase , UpperCamelCase ) , matrix_addition(UpperCamelCase , UpperCamelCase ) )
__UpperCAmelCase : str = actual_strassen(matrix_subtraction(UpperCamelCase , UpperCamelCase ) , matrix_addition(UpperCamelCase , UpperCamelCase ) )
__UpperCAmelCase : Dict = actual_strassen(matrix_subtraction(UpperCamelCase , UpperCamelCase ) , matrix_addition(UpperCamelCase , UpperCamelCase ) )
__UpperCAmelCase : List[Any] = matrix_addition(matrix_subtraction(matrix_addition(UpperCamelCase , UpperCamelCase ) , UpperCamelCase ) , UpperCamelCase )
__UpperCAmelCase : Dict = matrix_addition(UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : List[str] = matrix_addition(UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Any = matrix_subtraction(matrix_subtraction(matrix_addition(UpperCamelCase , UpperCamelCase ) , UpperCamelCase ) , UpperCamelCase )
# construct the new matrix from our 4 quadrants
__UpperCAmelCase : str = []
for i in range(len(UpperCamelCase ) ):
new_matrix.append(top_left[i] + top_right[i] )
for i in range(len(UpperCamelCase ) ):
new_matrix.append(bot_left[i] + bot_right[i] )
return new_matrix
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> list:
"""simple docstring"""
if matrix_dimensions(UpperCamelCase )[1] != matrix_dimensions(UpperCamelCase )[0]:
__UpperCAmelCase : List[str] = (
"Unable to multiply these matrices, please check the dimensions.\n"
f"Matrix A: {matrixa}\n"
f"Matrix B: {matrixa}"
)
raise Exception(UpperCamelCase )
__UpperCAmelCase : int = matrix_dimensions(UpperCamelCase )
__UpperCAmelCase : Any = matrix_dimensions(UpperCamelCase )
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
__UpperCAmelCase : Optional[int] = max(*UpperCamelCase , *UpperCamelCase )
__UpperCAmelCase : Optional[Any] = int(math.pow(2 , math.ceil(math.loga(UpperCamelCase ) ) ) )
__UpperCAmelCase : List[str] = matrixa
__UpperCAmelCase : Union[str, Any] = matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0 , UpperCamelCase ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , UpperCamelCase ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
if i < dimensiona[0]:
for _ in range(dimensiona[1] , UpperCamelCase ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
__UpperCAmelCase : str = actual_strassen(UpperCamelCase , UpperCamelCase )
# Removing the additional zeros
for i in range(0 , UpperCamelCase ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , UpperCamelCase ):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
A = [
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
A = [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
print(strassen(matrixa, matrixa))
| 77 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : str = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
__UpperCAmelCase : Any = 0
__UpperCAmelCase : Dict = 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Tuple = 0
# compute the shape of the output matrix
__UpperCAmelCase : Optional[int] = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
__UpperCAmelCase : List[str] = np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
__UpperCAmelCase : str = np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
__UpperCAmelCase : int = 0
__UpperCAmelCase : int = 0
return updated_arr
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
__UpperCAmelCase : List[str] = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Any = 0
# compute the shape of the output matrix
__UpperCAmelCase : Tuple = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
__UpperCAmelCase : str = np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
__UpperCAmelCase : Tuple = int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Optional[Any] = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name="""avgpooling""", verbose=True)
# Loading the image
A = Image.open("""path_to_image""")
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 77 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ["""MMBTForClassification""", """MMBTModel""", """ModalEmbeddings"""]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A = None
A = logging.get_logger(__name__)
A = """▁"""
A = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
A = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
A = {
"""google/pegasus-xsum""": 512,
}
class a__ ( __magic_name__ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = PegasusTokenizer
lowercase_ = ["input_ids", "attention_mask"]
def __init__( self : str , UpperCamelCase_ : str=None , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : int="<pad>" , UpperCamelCase_ : Optional[Any]="</s>" , UpperCamelCase_ : Any="<unk>" , UpperCamelCase_ : Tuple="<mask_2>" , UpperCamelCase_ : Any="<mask_1>" , UpperCamelCase_ : Tuple=None , UpperCamelCase_ : str=103 , **UpperCamelCase_ : Optional[Any] , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = offset
if additional_special_tokens is not None:
if not isinstance(UpperCamelCase_ , UpperCamelCase_):
raise TypeError(
F"additional_special_tokens should be of type {type(UpperCamelCase_)}, but is"
F" {type(UpperCamelCase_)}")
__UpperCAmelCase : Any = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F"<unk_{i}>" for i in range(len(UpperCamelCase_) , self.offset - 1)
]
if len(set(UpperCamelCase_)) != len(UpperCamelCase_):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.")
__UpperCAmelCase : str = additional_special_tokens_extended
else:
__UpperCAmelCase : Tuple = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F"<unk_{i}>" for i in range(2 , self.offset)]
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , pad_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , mask_token_sent=UpperCamelCase_ , offset=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
__UpperCAmelCase : Optional[int] = vocab_file
__UpperCAmelCase : List[str] = False if not self.vocab_file else True
def a_ ( self : Union[str, Any] , UpperCamelCase_ : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : int = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
F" {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}")
return [1 if x in all_special_ids else 0 for x in seq]
def a_ ( self : Union[str, Any] , UpperCamelCase_ : List , UpperCamelCase_ : Optional[List] = None , UpperCamelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(UpperCamelCase_)
elif token_ids_a is None:
return self._special_token_mask(UpperCamelCase_) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a) + [1]
def a_ ( self : int , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[Any]=None):
"""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 a_ ( self : Union[str, Any] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer.")
if not os.path.isdir(UpperCamelCase_):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
__UpperCAmelCase : List[str] = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_):
copyfile(self.vocab_file , UpperCamelCase_)
return (out_vocab_file,)
| 77 | 1 |
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