code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(_A ):
for j in range(_A ):
if dist[i][j] != float('''inf''' ):
print(int(dist[i][j] ) , end='''\t''' )
else:
print('''INF''' , end='''\t''' )
print()
def _A ( lowercase , lowercase ):
"""simple docstring"""
a =[[float('''inf''' ) for _ in range(_A )] for _ in range(_A )]
for i in range(_A ):
for j in range(_A ):
a =graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(_A ):
# looping through rows of graph array
for i in range(_A ):
# looping through columns of graph array
for j in range(_A ):
if (
dist[i][k] != float('''inf''' )
and dist[k][j] != float('''inf''' )
and dist[i][k] + dist[k][j] < dist[i][j]
):
a =dist[i][k] + dist[k][j]
_print_dist(_A , _A )
return dist, v
if __name__ == "__main__":
lowerCamelCase_ : Tuple = int(input("""Enter number of vertices: """))
lowerCamelCase_ : int = int(input("""Enter number of edges: """))
lowerCamelCase_ : Dict = [[float("""inf""") for i in range(v)] for j in range(v)]
for i in range(v):
lowerCamelCase_ : Optional[Any] = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("""\nEdge """, i + 1)
lowerCamelCase_ : List[str] = int(input("""Enter source:"""))
lowerCamelCase_ : int = int(input("""Enter destination:"""))
lowerCamelCase_ : Union[str, Any] = float(input("""Enter weight:"""))
lowerCamelCase_ : Dict = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0 | 81 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE( a_ ):
_UpperCAmelCase = ["pixel_values"]
def __init__( self: List[Any] , UpperCamelCase: bool = True , UpperCamelCase: Optional[Dict[str, int]] = None , UpperCamelCase: PILImageResampling = PILImageResampling.BILINEAR , UpperCamelCase: bool = True , UpperCamelCase: Dict[str, int] = None , UpperCamelCase: bool = True , UpperCamelCase: Union[int, float] = 1 / 2_55 , UpperCamelCase: bool = True , UpperCamelCase: Optional[Union[float, List[float]]] = None , UpperCamelCase: Optional[Union[float, List[float]]] = None , **UpperCamelCase: Optional[int] , ) -> None:
super().__init__(**UpperCamelCase )
snake_case__ = size if size is not None else {'shortest_edge': 2_56}
snake_case__ = get_size_dict(UpperCamelCase , default_to_square=UpperCamelCase )
snake_case__ = crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
snake_case__ = get_size_dict(UpperCamelCase )
snake_case__ = do_resize
snake_case__ = size
snake_case__ = resample
snake_case__ = do_center_crop
snake_case__ = crop_size
snake_case__ = do_rescale
snake_case__ = rescale_factor
snake_case__ = do_normalize
snake_case__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
snake_case__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase_ ( self: Tuple , UpperCamelCase: np.ndarray , UpperCamelCase: Dict[str, int] , UpperCamelCase: PILImageResampling = PILImageResampling.BICUBIC , UpperCamelCase: Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase: Dict , ) -> np.ndarray:
snake_case__ = get_size_dict(UpperCamelCase , default_to_square=UpperCamelCase )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
snake_case__ = get_resize_output_image_size(UpperCamelCase , size=size['shortest_edge'] , default_to_square=UpperCamelCase )
return resize(UpperCamelCase , size=UpperCamelCase , resample=UpperCamelCase , data_format=UpperCamelCase , **UpperCamelCase )
def lowerCAmelCase_ ( self: List[Any] , UpperCamelCase: np.ndarray , UpperCamelCase: Dict[str, int] , UpperCamelCase: Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase: List[Any] , ) -> np.ndarray:
snake_case__ = get_size_dict(UpperCamelCase )
return center_crop(UpperCamelCase , size=(size['height'], size['width']) , data_format=UpperCamelCase , **UpperCamelCase )
def lowerCAmelCase_ ( self: Union[str, Any] , UpperCamelCase: np.ndarray , UpperCamelCase: float , UpperCamelCase: Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase: Dict ) -> np.ndarray:
return rescale(UpperCamelCase , scale=UpperCamelCase , data_format=UpperCamelCase , **UpperCamelCase )
def lowerCAmelCase_ ( self: Optional[Any] , UpperCamelCase: np.ndarray , UpperCamelCase: Union[float, List[float]] , UpperCamelCase: Union[float, List[float]] , UpperCamelCase: Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase: Any , ) -> np.ndarray:
return normalize(UpperCamelCase , mean=UpperCamelCase , std=UpperCamelCase , data_format=UpperCamelCase , **UpperCamelCase )
def lowerCAmelCase_ ( self: Any , UpperCamelCase: ImageInput , UpperCamelCase: Optional[bool] = None , UpperCamelCase: Dict[str, int] = None , UpperCamelCase: PILImageResampling = None , UpperCamelCase: bool = None , UpperCamelCase: Dict[str, int] = None , UpperCamelCase: Optional[bool] = None , UpperCamelCase: Optional[float] = None , UpperCamelCase: Optional[bool] = None , UpperCamelCase: Optional[Union[float, List[float]]] = None , UpperCamelCase: Optional[Union[float, List[float]]] = None , UpperCamelCase: Optional[Union[str, TensorType]] = None , UpperCamelCase: Union[str, ChannelDimension] = ChannelDimension.FIRST , **UpperCamelCase: Any , ) -> Optional[Any]:
snake_case__ = do_resize if do_resize is not None else self.do_resize
snake_case__ = size if size is not None else self.size
snake_case__ = get_size_dict(UpperCamelCase , default_to_square=UpperCamelCase )
snake_case__ = resample if resample is not None else self.resample
snake_case__ = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case__ = crop_size if crop_size is not None else self.crop_size
snake_case__ = get_size_dict(UpperCamelCase )
snake_case__ = do_rescale if do_rescale is not None else self.do_rescale
snake_case__ = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case__ = do_normalize if do_normalize is not None else self.do_normalize
snake_case__ = image_mean if image_mean is not None else self.image_mean
snake_case__ = image_std if image_std is not None else self.image_std
snake_case__ = make_list_of_images(UpperCamelCase )
if not valid_images(UpperCamelCase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
snake_case__ = [to_numpy_array(UpperCamelCase ) for image in images]
if do_resize:
snake_case__ = [self.resize(image=UpperCamelCase , size=UpperCamelCase , resample=UpperCamelCase ) for image in images]
if do_center_crop:
snake_case__ = [self.center_crop(image=UpperCamelCase , size=UpperCamelCase ) for image in images]
if do_rescale:
snake_case__ = [self.rescale(image=UpperCamelCase , scale=UpperCamelCase ) for image in images]
if do_normalize:
snake_case__ = [self.normalize(image=UpperCamelCase , mean=UpperCamelCase , std=UpperCamelCase ) for image in images]
snake_case__ = [to_channel_dimension_format(UpperCamelCase , UpperCamelCase ) for image in images]
snake_case__ = {'pixel_values': images}
return BatchFeature(data=UpperCamelCase , tensor_type=UpperCamelCase )
| 307 | 0 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer
logging.basicConfig(
format="""%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s""",
datefmt="""%Y-%m-%d %H:%M:%S""",
level=os.environ.get("""LOGLEVEL""", """INFO""").upper(),
stream=sys.stdout,
)
UpperCamelCase = logging.getLogger(__name__)
UpperCamelCase = {'facebook/bart-base': BartForConditionalGeneration}
UpperCamelCase = {'facebook/bart-base': BartTokenizer}
def _SCREAMING_SNAKE_CASE ( ):
A_ : Tuple = argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.''' )
parser.add_argument(
'''--validation_file''' , type=snake_case_ , default=snake_case_ , help='''A csv or a json file containing the validation data.''' )
parser.add_argument(
'''--max_length''' , type=snake_case_ , default=5 , help='''The maximum total input sequence length after tokenization.''' , )
parser.add_argument(
'''--num_beams''' , type=snake_case_ , default=snake_case_ , help=(
'''Number of beams to use for evaluation. This argument will be '''
'''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
) , )
parser.add_argument(
'''--model_name_or_path''' , type=snake_case_ , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , required=snake_case_ , )
parser.add_argument(
'''--config_name''' , type=snake_case_ , default=snake_case_ , help='''Pretrained config name or path if not the same as model_name''' , )
parser.add_argument(
'''--device''' , type=snake_case_ , default='''cpu''' , help='''Device where the model will be run''' , )
parser.add_argument('''--output_file_path''' , type=snake_case_ , default=snake_case_ , help='''Where to store the final ONNX file.''' )
A_ : Optional[Any] = parser.parse_args()
return args
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE="cpu" ):
A_ : int = model_dict[model_name].from_pretrained(snake_case_ ).to(snake_case_ )
A_ : List[str] = tokenizer_dict[model_name].from_pretrained(snake_case_ )
if model_name in ["facebook/bart-base"]:
A_ : List[str] = 0
A_ : List[Any] = None
A_ : List[str] = 0
return huggingface_model, tokenizer
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
model.eval()
A_ : List[Any] = None
A_ : List[str] = torch.jit.script(BARTBeamSearchGenerator(snake_case_ ) )
with torch.no_grad():
A_ : Optional[int] = '''My friends are cool but they eat too many carbs.'''
A_ : Optional[int] = tokenizer([ARTICLE_TO_SUMMARIZE] , max_length=1_024 , return_tensors='''pt''' ).to(model.device )
A_ : int = model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , num_beams=snake_case_ , max_length=snake_case_ , early_stopping=snake_case_ , decoder_start_token_id=model.config.decoder_start_token_id , )
torch.onnx.export(
snake_case_ , (
inputs['''input_ids'''],
inputs['''attention_mask'''],
num_beams,
max_length,
model.config.decoder_start_token_id,
) , snake_case_ , opset_version=14 , input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id'''] , output_names=['''output_ids'''] , dynamic_axes={
'''input_ids''': {0: '''batch''', 1: '''seq'''},
'''output_ids''': {0: '''batch''', 1: '''seq_out'''},
} , example_outputs=snake_case_ , )
logger.info('''Model exported to {}'''.format(snake_case_ ) )
A_ : List[Any] = remove_dup_initializers(os.path.abspath(snake_case_ ) )
logger.info('''Deduplicated and optimized model written to {}'''.format(snake_case_ ) )
A_ : int = onnxruntime.InferenceSession(snake_case_ )
A_ : Optional[Any] = ort_sess.run(
snake_case_ , {
'''input_ids''': inputs['''input_ids'''].cpu().numpy(),
'''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
'''num_beams''': np.array(snake_case_ ),
'''max_length''': np.array(snake_case_ ),
'''decoder_start_token_id''': np.array(model.config.decoder_start_token_id ),
} , )
np.testing.assert_allclose(summary_ids.cpu().numpy() , ort_out[0] , rtol=1e-3 , atol=1e-3 )
logger.info('''Model outputs from torch and ONNX Runtime are similar.''' )
logger.info('''Success.''' )
def _SCREAMING_SNAKE_CASE ( ):
A_ : Union[str, Any] = parse_args()
A_ : List[str] = 5
A_ : str = 4
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO , )
logger.setLevel(logging.INFO )
transformers.utils.logging.set_verbosity_error()
A_ : int = torch.device(args.device )
A_ , A_ : int = load_model_tokenizer(args.model_name_or_path , snake_case_ )
if model.config.decoder_start_token_id is None:
raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined''' )
model.to(snake_case_ )
if args.max_length:
A_ : Optional[Any] = args.max_length
if args.num_beams:
A_ : List[str] = args.num_beams
if args.output_file_path:
A_ : Union[str, Any] = args.output_file_path
else:
A_ : Dict = '''BART.onnx'''
logger.info('''Exporting model to ONNX''' )
export_and_validate_model(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
if __name__ == "__main__":
main()
| 367 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = ["image_processor"]
snake_case = "SamImageProcessor"
def __init__( self , _SCREAMING_SNAKE_CASE )->Union[str, Any]:
'''simple docstring'''
super().__init__(_SCREAMING_SNAKE_CASE )
A_ : Any = self.image_processor
A_ : Optional[int] = -10
A_ : List[Any] = self.image_processor.size['''longest_edge''']
def __call__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , )->BatchEncoding:
'''simple docstring'''
A_ : Union[str, Any] = self.image_processor(
_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
# pop arguments that are not used in the foward but used nevertheless
A_ : Tuple = encoding_image_processor['''original_sizes''']
if hasattr(_SCREAMING_SNAKE_CASE , '''numpy''' ): # Checks if Torch or TF tensor
A_ : int = original_sizes.numpy()
A_ , A_ , A_ : str = self._check_and_preprocess_points(
input_points=_SCREAMING_SNAKE_CASE , input_labels=_SCREAMING_SNAKE_CASE , input_boxes=_SCREAMING_SNAKE_CASE , )
A_ : Optional[Any] = self._normalize_and_convert(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , input_points=_SCREAMING_SNAKE_CASE , input_labels=_SCREAMING_SNAKE_CASE , input_boxes=_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , )
return encoding_image_processor
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="pt" , )->Dict:
'''simple docstring'''
if input_points is not None:
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
A_ : Optional[Any] = [
self._normalize_coordinates(self.target_size , _SCREAMING_SNAKE_CASE , original_sizes[0] ) for point in input_points
]
else:
A_ : str = [
self._normalize_coordinates(self.target_size , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for point, original_size in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
A_ , A_ : Optional[Any] = self._pad_points_and_labels(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : List[str] = np.array(_SCREAMING_SNAKE_CASE )
if input_labels is not None:
A_ : Dict = np.array(_SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
A_ : Tuple = [
self._normalize_coordinates(self.target_size , _SCREAMING_SNAKE_CASE , original_sizes[0] , is_bounding_box=_SCREAMING_SNAKE_CASE )
for box in input_boxes
]
else:
A_ : List[Any] = [
self._normalize_coordinates(self.target_size , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , is_bounding_box=_SCREAMING_SNAKE_CASE )
for box, original_size in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
]
A_ : Union[str, Any] = np.array(_SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if return_tensors == "pt":
A_ : Dict = torch.from_numpy(_SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
A_ : Optional[Any] = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
A_ : Optional[int] = tf.convert_to_tensor(_SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
A_ : List[Any] = tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({'''input_boxes''': input_boxes} )
if input_points is not None:
if return_tensors == "pt":
A_ : Union[str, Any] = torch.from_numpy(_SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
A_ : Union[str, Any] = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
A_ : List[str] = tf.convert_to_tensor(_SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
A_ : Union[str, Any] = tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({'''input_points''': input_points} )
if input_labels is not None:
if return_tensors == "pt":
A_ : Optional[Any] = torch.from_numpy(_SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
A_ : List[Any] = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
A_ : int = tf.convert_to_tensor(_SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
A_ : List[Any] = tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({'''input_labels''': input_labels} )
return encoding_image_processor
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->Dict:
'''simple docstring'''
A_ : Optional[Any] = max([point.shape[0] for point in input_points] )
A_ : int = []
for i, point in enumerate(_SCREAMING_SNAKE_CASE ):
if point.shape[0] != expected_nb_points:
A_ : Optional[int] = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value] , axis=0 )
A_ : int = np.append(input_labels[i] , [self.point_pad_value] )
processed_input_points.append(_SCREAMING_SNAKE_CASE )
A_ : Optional[int] = processed_input_points
return input_points, input_labels
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False )->np.ndarray:
'''simple docstring'''
A_ , A_ : str = original_size
A_ , A_ : Dict = self.image_processor._get_preprocess_shape(_SCREAMING_SNAKE_CASE , longest_edge=_SCREAMING_SNAKE_CASE )
A_ : Optional[int] = deepcopy(_SCREAMING_SNAKE_CASE ).astype(_SCREAMING_SNAKE_CASE )
if is_bounding_box:
A_ : Union[str, Any] = coords.reshape(-1 , 2 , 2 )
A_ : Any = coords[..., 0] * (new_w / old_w)
A_ : List[str] = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
A_ : str = coords.reshape(-1 , 4 )
return coords
def _snake_case ( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , )->str:
'''simple docstring'''
if input_points is not None:
if hasattr(_SCREAMING_SNAKE_CASE , '''numpy''' ): # Checks for TF or Torch tensor
A_ : List[str] = input_points.numpy().tolist()
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not isinstance(input_points[0] , _SCREAMING_SNAKE_CASE ):
raise ValueError('''Input points must be a list of list of floating points.''' )
A_ : Optional[Any] = [np.array(_SCREAMING_SNAKE_CASE ) for input_point in input_points]
else:
A_ : Tuple = None
if input_labels is not None:
if hasattr(_SCREAMING_SNAKE_CASE , '''numpy''' ):
A_ : Dict = input_labels.numpy().tolist()
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not isinstance(input_labels[0] , _SCREAMING_SNAKE_CASE ):
raise ValueError('''Input labels must be a list of list integers.''' )
A_ : Union[str, Any] = [np.array(_SCREAMING_SNAKE_CASE ) for label in input_labels]
else:
A_ : str = None
if input_boxes is not None:
if hasattr(_SCREAMING_SNAKE_CASE , '''numpy''' ):
A_ : str = input_boxes.numpy().tolist()
if (
not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0] , _SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0][0] , _SCREAMING_SNAKE_CASE )
):
raise ValueError('''Input boxes must be a list of list of list of floating points.''' )
A_ : Tuple = [np.array(_SCREAMING_SNAKE_CASE ).astype(np.floataa ) for box in input_boxes]
else:
A_ : Dict = None
return input_points, input_labels, input_boxes
@property
def _snake_case ( self )->List[str]:
'''simple docstring'''
A_ : Optional[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(_SCREAMING_SNAKE_CASE ) )
def _snake_case ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )->Union[str, Any]:
'''simple docstring'''
return self.image_processor.post_process_masks(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
| 65 | 0 |
from functools import lru_cache
def lowerCAmelCase_ (lowerCAmelCase__: int ):
"""simple docstring"""
UpperCAmelCase_: List[str] = 2
UpperCAmelCase_: Union[str, Any] = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(lowerCAmelCase__ )
if n > 1:
factors.add(lowerCAmelCase__ )
return factors
@lru_cache
def lowerCAmelCase_ (lowerCAmelCase__: int ):
"""simple docstring"""
return len(unique_prime_factors(lowerCAmelCase__ ) )
def lowerCAmelCase_ (lowerCAmelCase__: list ):
"""simple docstring"""
return len(set(lowerCAmelCase__ ) ) in (0, 1)
def lowerCAmelCase_ (lowerCAmelCase__: int ):
"""simple docstring"""
UpperCAmelCase_: int = 2
while True:
# Increment each value of a generated range
UpperCAmelCase_: List[str] = [base + i for i in range(lowerCAmelCase__ )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
UpperCAmelCase_: Any = [upf_len(lowerCAmelCase__ ) for x in group]
checker.append(lowerCAmelCase__ )
# If all numbers in the list are equal, return the group variable.
if equality(lowerCAmelCase__ ):
return group
# Increment our base variable by 1
base += 1
def lowerCAmelCase_ (lowerCAmelCase__: int = 4 ):
"""simple docstring"""
UpperCAmelCase_: str = run(lowerCAmelCase__ )
return results[0] if len(lowerCAmelCase__ ) else None
if __name__ == "__main__":
print(solution())
| 147 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a : List[str] = 'src/diffusers'
a : Optional[Any] = '.'
# This is to make sure the diffusers module imported is the one in the repo.
a : Dict = importlib.util.spec_from_file_location(
'diffusers',
os.path.join(DIFFUSERS_PATH, '__init__.py'),
submodule_search_locations=[DIFFUSERS_PATH],
)
a : str = spec.loader.load_module()
def lowerCAmelCase_ (lowerCAmelCase__: Optional[int] , lowerCAmelCase__: List[str] ):
"""simple docstring"""
return line.startswith(lowerCAmelCase__ ) or len(lowerCAmelCase__ ) <= 1 or re.search(r"""^\s*\)(\s*->.*:|:)\s*$""" , lowerCAmelCase__ ) is not None
def lowerCAmelCase_ (lowerCAmelCase__: str ):
"""simple docstring"""
UpperCAmelCase_: Optional[Any] = object_name.split(""".""" )
UpperCAmelCase_: Tuple = 0
# First let's find the module where our object lives.
UpperCAmelCase_: Union[str, Any] = parts[i]
while i < len(lowerCAmelCase__ ) and not os.path.isfile(os.path.join(lowerCAmelCase__ , F'{module}.py' ) ):
i += 1
if i < len(lowerCAmelCase__ ):
UpperCAmelCase_: List[Any] = os.path.join(lowerCAmelCase__ , parts[i] )
if i >= len(lowerCAmelCase__ ):
raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.' )
with open(os.path.join(lowerCAmelCase__ , F'{module}.py' ) , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
UpperCAmelCase_: List[Any] = f.readlines()
# Now let's find the class / func in the code!
UpperCAmelCase_: Any = """"""
UpperCAmelCase_: Tuple = 0
for name in parts[i + 1 :]:
while (
line_index < len(lowerCAmelCase__ ) and re.search(rF'^{indent}(class|def)\s+{name}(\(|\:)' , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(lowerCAmelCase__ ):
raise ValueError(F' {object_name} does not match any function or class in {module}.' )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
UpperCAmelCase_: Dict = line_index
while line_index < len(lowerCAmelCase__ ) and _should_continue(lines[line_index] , lowerCAmelCase__ ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
UpperCAmelCase_: Optional[int] = lines[start_index:line_index]
return "".join(lowerCAmelCase__ )
a : List[str] = re.compile(r'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
a : Optional[int] = re.compile(r'^\s*(\S+)->(\S+)(\s+.*|$)')
a : List[Any] = re.compile(r'<FILL\s+[^>]*>')
def lowerCAmelCase_ (lowerCAmelCase__: Dict ):
"""simple docstring"""
UpperCAmelCase_: Dict = code.split("""\n""" )
UpperCAmelCase_: Any = 0
while idx < len(lowerCAmelCase__ ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(lowerCAmelCase__ ):
return re.search(r"""^(\s*)\S""" , lines[idx] ).groups()[0]
return ""
def lowerCAmelCase_ (lowerCAmelCase__: Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase_: str = len(get_indent(lowerCAmelCase__ ) ) > 0
if has_indent:
UpperCAmelCase_: Union[str, Any] = F'class Bla:\n{code}'
UpperCAmelCase_: int = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_1_9 , preview=lowerCAmelCase__ )
UpperCAmelCase_: int = black.format_str(lowerCAmelCase__ , mode=lowerCAmelCase__ )
UpperCAmelCase_ , UpperCAmelCase_: List[Any] = style_docstrings_in_code(lowerCAmelCase__ )
return result[len("""class Bla:\n""" ) :] if has_indent else result
def lowerCAmelCase_ (lowerCAmelCase__: Tuple , lowerCAmelCase__: int=False ):
"""simple docstring"""
with open(lowerCAmelCase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
UpperCAmelCase_: List[str] = f.readlines()
UpperCAmelCase_: List[str] = []
UpperCAmelCase_: Tuple = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(lowerCAmelCase__ ):
UpperCAmelCase_: Dict = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_: Any = search.groups()
UpperCAmelCase_: str = find_code_in_diffusers(lowerCAmelCase__ )
UpperCAmelCase_: int = get_indent(lowerCAmelCase__ )
UpperCAmelCase_: Dict = line_index + 1 if indent == theoretical_indent else line_index + 2
UpperCAmelCase_: Tuple = theoretical_indent
UpperCAmelCase_: Dict = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
UpperCAmelCase_: Tuple = True
while line_index < len(lowerCAmelCase__ ) and should_continue:
line_index += 1
if line_index >= len(lowerCAmelCase__ ):
break
UpperCAmelCase_: Any = lines[line_index]
UpperCAmelCase_: Tuple = _should_continue(lowerCAmelCase__ , lowerCAmelCase__ ) and re.search(F'^{indent}# End copy' , lowerCAmelCase__ ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
UpperCAmelCase_: int = lines[start_index:line_index]
UpperCAmelCase_: Union[str, Any] = """""".join(lowerCAmelCase__ )
# Remove any nested `Copied from` comments to avoid circular copies
UpperCAmelCase_: int = [line for line in theoretical_code.split("""\n""" ) if _re_copy_warning.search(lowerCAmelCase__ ) is None]
UpperCAmelCase_: Union[str, Any] = """\n""".join(lowerCAmelCase__ )
# Before comparing, use the `replace_pattern` on the original code.
if len(lowerCAmelCase__ ) > 0:
UpperCAmelCase_: Any = replace_pattern.replace("""with""" , """""" ).split(""",""" )
UpperCAmelCase_: int = [_re_replace_pattern.search(lowerCAmelCase__ ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_: str = pattern.groups()
UpperCAmelCase_: int = re.sub(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
if option.strip() == "all-casing":
UpperCAmelCase_: List[Any] = re.sub(obja.lower() , obja.lower() , lowerCAmelCase__ )
UpperCAmelCase_: Optional[int] = re.sub(obja.upper() , obja.upper() , lowerCAmelCase__ )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
UpperCAmelCase_: Union[str, Any] = blackify(lines[start_index - 1] + theoretical_code )
UpperCAmelCase_: Dict = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
UpperCAmelCase_: str = lines[:start_index] + [theoretical_code] + lines[line_index:]
UpperCAmelCase_: Optional[int] = start_index + 1
if overwrite and len(lowerCAmelCase__ ) > 0:
# Warn the user a file has been modified.
print(F'Detected changes, rewriting {filename}.' )
with open(lowerCAmelCase__ , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(lowerCAmelCase__ )
return diffs
def lowerCAmelCase_ (lowerCAmelCase__: bool = False ):
"""simple docstring"""
UpperCAmelCase_: Dict = glob.glob(os.path.join(lowerCAmelCase__ , """**/*.py""" ) , recursive=lowerCAmelCase__ )
UpperCAmelCase_: Optional[Any] = []
for filename in all_files:
UpperCAmelCase_: str = is_copy_consistent(lowerCAmelCase__ , lowerCAmelCase__ )
diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
if not overwrite and len(lowerCAmelCase__ ) > 0:
UpperCAmelCase_: Dict = """\n""".join(lowerCAmelCase__ )
raise Exception(
"""Found the following copy inconsistencies:\n"""
+ diff
+ """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.""" )
if __name__ == "__main__":
a : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
a : List[Any] = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 147 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : Optional[int] = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
),
'''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
),
'''distilbert-base-uncased-finetuned-sst-2-english''': (
'''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
),
}
class UpperCamelCase_ ( a_ ):
_A : Union[str, Any] = 'distilbert'
_A : Any = {
'hidden_size': 'dim',
'num_attention_heads': 'n_heads',
'num_hidden_layers': 'n_layers',
}
def __init__( self , snake_case__=3_05_22 , snake_case__=5_12 , snake_case__=False , snake_case__=6 , snake_case__=12 , snake_case__=7_68 , snake_case__=4 * 7_68 , snake_case__=0.1 , snake_case__=0.1 , snake_case__="gelu" , snake_case__=0.02 , snake_case__=0.1 , snake_case__=0.2 , snake_case__=0 , **snake_case__ , ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = vocab_size
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = sinusoidal_pos_embds
UpperCAmelCase = n_layers
UpperCAmelCase = n_heads
UpperCAmelCase = dim
UpperCAmelCase = hidden_dim
UpperCAmelCase = dropout
UpperCAmelCase = attention_dropout
UpperCAmelCase = activation
UpperCAmelCase = initializer_range
UpperCAmelCase = qa_dropout
UpperCAmelCase = seq_classif_dropout
super().__init__(**snake_case__ , pad_token_id=snake_case__ )
class UpperCamelCase_ ( a_ ):
@property
def UpperCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
UpperCAmelCase = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
UpperCAmelCase = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 248 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
lowerCAmelCase_ : Any = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq''': (
'''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wikisql-supervised''': (
'''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-tabfact''': (
'''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
),
}
class UpperCamelCase_ ( a_ ):
_A : List[str] = 'tapas'
def __init__( self , snake_case__=3_05_22 , snake_case__=7_68 , snake_case__=12 , snake_case__=12 , snake_case__=30_72 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=10_24 , snake_case__=[3, 2_56, 2_56, 2, 2_56, 2_56, 10] , snake_case__=0.02 , snake_case__=1e-12 , snake_case__=0 , snake_case__=10.0 , snake_case__=0 , snake_case__=1.0 , snake_case__=None , snake_case__=1.0 , snake_case__=False , snake_case__=None , snake_case__=1.0 , snake_case__=1.0 , snake_case__=False , snake_case__=False , snake_case__="ratio" , snake_case__=None , snake_case__=None , snake_case__=64 , snake_case__=32 , snake_case__=False , snake_case__=True , snake_case__=False , snake_case__=False , snake_case__=True , snake_case__=False , snake_case__=None , snake_case__=None , **snake_case__ , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(pad_token_id=snake_case__ , **snake_case__ )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = hidden_act
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_sizes
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
# Fine-tuning task hyperparameters
UpperCAmelCase = positive_label_weight
UpperCAmelCase = num_aggregation_labels
UpperCAmelCase = aggregation_loss_weight
UpperCAmelCase = use_answer_as_supervision
UpperCAmelCase = answer_loss_importance
UpperCAmelCase = use_normalized_answer_loss
UpperCAmelCase = huber_loss_delta
UpperCAmelCase = temperature
UpperCAmelCase = aggregation_temperature
UpperCAmelCase = use_gumbel_for_cells
UpperCAmelCase = use_gumbel_for_aggregation
UpperCAmelCase = average_approximation_function
UpperCAmelCase = cell_selection_preference
UpperCAmelCase = answer_loss_cutoff
UpperCAmelCase = max_num_rows
UpperCAmelCase = max_num_columns
UpperCAmelCase = average_logits_per_cell
UpperCAmelCase = select_one_column
UpperCAmelCase = allow_empty_column_selection
UpperCAmelCase = init_cell_selection_weights_to_zero
UpperCAmelCase = reset_position_index_per_cell
UpperCAmelCase = disable_per_token_loss
# Aggregation hyperparameters
UpperCAmelCase = aggregation_labels
UpperCAmelCase = no_aggregation_label_index
if isinstance(self.aggregation_labels , snake_case__ ):
UpperCAmelCase = {int(snake_case__ ): v for k, v in aggregation_labels.items()}
| 248 | 1 |
# 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.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
SCREAMING_SNAKE_CASE :Union[str, Any] = 'Create a default config file for Accelerate with only a few flags set.'
def UpperCAmelCase ( a_="no" , a_ = default_json_config_file , a_ = False ) -> Optional[int]:
"""simple docstring"""
__A = Path(a_ )
path.parent.mkdir(parents=a_ , exist_ok=a_ )
if path.exists():
print(
F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.''' )
return False
__A = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}''' )
__A = {
"compute_environment": "LOCAL_MACHINE",
"mixed_precision": mixed_precision,
}
if torch.cuda.is_available():
__A = torch.cuda.device_count()
__A = num_gpus
__A = False
if num_gpus > 1:
__A = "MULTI_GPU"
else:
__A = "NO"
elif is_xpu_available() and use_xpu:
__A = torch.xpu.device_count()
__A = num_xpus
__A = False
if num_xpus > 1:
__A = "MULTI_XPU"
else:
__A = "NO"
elif is_npu_available():
__A = torch.npu.device_count()
__A = num_npus
__A = False
if num_npus > 1:
__A = "MULTI_NPU"
else:
__A = "NO"
else:
__A = 0
__A = True
__A = 1
__A = "NO"
__A = ClusterConfig(**a_ )
config.to_json_file(a_ )
return path
def UpperCAmelCase ( a_ , a_ ) -> List[Any]:
"""simple docstring"""
__A = parser.add_parser("default" , parents=a_ , help=a_ , formatter_class=a_ )
parser.add_argument(
"--config_file" , default=a_ , 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'."
) , dest="save_location" , )
parser.add_argument(
"--mixed_precision" , choices=["no", "fp16", "bf16"] , type=a_ , help="Whether or not to use mixed precision training. "
"Choose between FP16 and BF16 (bfloat16) training. "
"BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later." , default="no" , )
parser.set_defaults(func=a_ )
return parser
def UpperCAmelCase ( a_ ) -> Dict:
"""simple docstring"""
__A = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F'''accelerate configuration saved at {config_file}''' )
| 15 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase__ = '''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
'''
lowerCAmelCase__ = '''
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the \'positive class\' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.
- `\'binary\'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
- `\'micro\'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
- `\'macro\'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- `\'weighted\'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
- `\'samples\'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
- `\'warn\'`: If there is a zero division, the return value is `0`, but warnings are also raised.
- `0`: If there is a zero division, the return value is `0`.
- `1`: If there is a zero division, the return value is `1`.
Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.
Examples:
Example 1-A simple example with some errors
>>> recall_metric = datasets.load_metric(\'recall\')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
>>> print(results)
{\'recall\': 0.6666666666666666}
Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
>>> recall_metric = datasets.load_metric(\'recall\')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
>>> print(results)
{\'recall\': 0.5}
Example 3-The same example as Example 1, but with `sample_weight` included.
>>> recall_metric = datasets.load_metric(\'recall\')
>>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
>>> print(results)
{\'recall\': 0.55}
Example 4-A multiclass example, using different averages.
>>> recall_metric = datasets.load_metric(\'recall\')
>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = recall_metric.compute(predictions=predictions, references=references, average=\'macro\')
>>> print(results)
{\'recall\': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average=\'micro\')
>>> print(results)
{\'recall\': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average=\'weighted\')
>>> print(results)
{\'recall\': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{\'recall\': array([1., 0., 0.])}
'''
lowerCAmelCase__ = '''
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCamelCase ( datasets.Metric ):
def snake_case_ (self ) -> str:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("int32" ) ),
"references": datasets.Sequence(datasets.Value("int32" ) ),
}
if self.config_name == "multilabel"
else {
"predictions": datasets.Value("int32" ),
"references": datasets.Value("int32" ),
} ) , reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"] , )
def snake_case_ (self , __a , __a , __a=None , __a=1 , __a="binary" , __a=None , __a="warn" , ) -> str:
UpperCamelCase = recall_score(
__a , __a , labels=__a , pos_label=__a , average=__a , sample_weight=__a , zero_division=__a , )
return {"recall": float(__a ) if score.size == 1 else score}
| 153 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a__ : List[str] = logging.get_logger(__name__)
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = original_name.split("." )[0]
__SCREAMING_SNAKE_CASE = key.split("." )
__SCREAMING_SNAKE_CASE = int(key_list[key_list.index(lowerCAmelCase_ ) - 2] )
__SCREAMING_SNAKE_CASE = int(key_list[key_list.index(lowerCAmelCase_ ) - 1] )
__SCREAMING_SNAKE_CASE = orig_block_num - offset
__SCREAMING_SNAKE_CASE = key.replace(f"""{orig_block_num}.{layer_num}.{original_name}""" , f"""block.{new_block_num}.{layer_num}.{new_name}""" )
return key
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = OrderedDict()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 0, 0
for key, value in state_dict.items():
if key.startswith("network" ):
__SCREAMING_SNAKE_CASE = key.replace("network" , "poolformer.encoder" )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith("bias" ) and "patch_embed" not in key:
patch_emb_offset += 1
__SCREAMING_SNAKE_CASE = key[: key.find("proj" )]
__SCREAMING_SNAKE_CASE = key.replace(lowerCAmelCase_ , f"""patch_embeddings.{total_embed_found}.""" )
__SCREAMING_SNAKE_CASE = key.replace("proj" , "projection" )
if key.endswith("bias" ):
total_embed_found += 1
if "patch_embeddings" in key:
__SCREAMING_SNAKE_CASE = "poolformer.encoder." + key
if "mlp.fc1" in key:
__SCREAMING_SNAKE_CASE = replace_key_with_offset(lowerCAmelCase_ , lowerCAmelCase_ , "mlp.fc1" , "output.conv1" )
if "mlp.fc2" in key:
__SCREAMING_SNAKE_CASE = replace_key_with_offset(lowerCAmelCase_ , lowerCAmelCase_ , "mlp.fc2" , "output.conv2" )
if "norm1" in key:
__SCREAMING_SNAKE_CASE = replace_key_with_offset(lowerCAmelCase_ , lowerCAmelCase_ , "norm1" , "before_norm" )
if "norm2" in key:
__SCREAMING_SNAKE_CASE = replace_key_with_offset(lowerCAmelCase_ , lowerCAmelCase_ , "norm2" , "after_norm" )
if "layer_scale_1" in key:
__SCREAMING_SNAKE_CASE = replace_key_with_offset(lowerCAmelCase_ , lowerCAmelCase_ , "layer_scale_1" , "layer_scale_1" )
if "layer_scale_2" in key:
__SCREAMING_SNAKE_CASE = replace_key_with_offset(lowerCAmelCase_ , lowerCAmelCase_ , "layer_scale_2" , "layer_scale_2" )
if "head" in key:
__SCREAMING_SNAKE_CASE = key.replace("head" , "classifier" )
__SCREAMING_SNAKE_CASE = value
return new_state_dict
def UpperCAmelCase__ ():
'''simple docstring'''
__SCREAMING_SNAKE_CASE = "http://images.cocodataset.org/val2017/000000039769.jpg"
__SCREAMING_SNAKE_CASE = Image.open(requests.get(lowerCAmelCase_ , stream=lowerCAmelCase_ ).raw )
return image
@torch.no_grad()
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = PoolFormerConfig()
# set attributes based on model_name
__SCREAMING_SNAKE_CASE = "huggingface/label-files"
__SCREAMING_SNAKE_CASE = model_name[-3:]
__SCREAMING_SNAKE_CASE = 1000
__SCREAMING_SNAKE_CASE = "imagenet-1k-id2label.json"
__SCREAMING_SNAKE_CASE = (1, 1000)
# set config attributes
__SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(lowerCAmelCase_ , lowerCAmelCase_ , repo_type="dataset" ) , "r" ) )
__SCREAMING_SNAKE_CASE = {int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
__SCREAMING_SNAKE_CASE = idalabel
__SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
if size == "s12":
__SCREAMING_SNAKE_CASE = [2, 2, 6, 2]
__SCREAMING_SNAKE_CASE = [64, 128, 320, 512]
__SCREAMING_SNAKE_CASE = 4.0
__SCREAMING_SNAKE_CASE = 0.9
elif size == "s24":
__SCREAMING_SNAKE_CASE = [4, 4, 12, 4]
__SCREAMING_SNAKE_CASE = [64, 128, 320, 512]
__SCREAMING_SNAKE_CASE = 4.0
__SCREAMING_SNAKE_CASE = 0.9
elif size == "s36":
__SCREAMING_SNAKE_CASE = [6, 6, 18, 6]
__SCREAMING_SNAKE_CASE = [64, 128, 320, 512]
__SCREAMING_SNAKE_CASE = 4.0
__SCREAMING_SNAKE_CASE = 1E-6
__SCREAMING_SNAKE_CASE = 0.9
elif size == "m36":
__SCREAMING_SNAKE_CASE = [6, 6, 18, 6]
__SCREAMING_SNAKE_CASE = [96, 192, 384, 768]
__SCREAMING_SNAKE_CASE = 4.0
__SCREAMING_SNAKE_CASE = 1E-6
__SCREAMING_SNAKE_CASE = 0.95
elif size == "m48":
__SCREAMING_SNAKE_CASE = [8, 8, 24, 8]
__SCREAMING_SNAKE_CASE = [96, 192, 384, 768]
__SCREAMING_SNAKE_CASE = 4.0
__SCREAMING_SNAKE_CASE = 1E-6
__SCREAMING_SNAKE_CASE = 0.95
else:
raise ValueError(f"""Size {size} not supported""" )
# load image processor
__SCREAMING_SNAKE_CASE = PoolFormerImageProcessor(crop_pct=lowerCAmelCase_ )
# Prepare image
__SCREAMING_SNAKE_CASE = prepare_img()
__SCREAMING_SNAKE_CASE = image_processor(images=lowerCAmelCase_ , return_tensors="pt" ).pixel_values
logger.info(f"""Converting model {model_name}...""" )
# load original state dict
__SCREAMING_SNAKE_CASE = torch.load(lowerCAmelCase_ , map_location=torch.device("cpu" ) )
# rename keys
__SCREAMING_SNAKE_CASE = rename_keys(lowerCAmelCase_ )
# create HuggingFace model and load state dict
__SCREAMING_SNAKE_CASE = PoolFormerForImageClassification(lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
model.eval()
# Define image processor
__SCREAMING_SNAKE_CASE = PoolFormerImageProcessor(crop_pct=lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = image_processor(images=prepare_img() , return_tensors="pt" ).pixel_values
# forward pass
__SCREAMING_SNAKE_CASE = model(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = outputs.logits
# define expected logit slices for different models
if size == "s12":
__SCREAMING_SNAKE_CASE = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
__SCREAMING_SNAKE_CASE = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
__SCREAMING_SNAKE_CASE = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
__SCREAMING_SNAKE_CASE = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
__SCREAMING_SNAKE_CASE = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(f"""Size {size} not supported""" )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , lowerCAmelCase_ , atol=1E-2 )
# finally, save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
a__ : Any = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''poolformer_s12''',
type=str,
help='''Name of the model you\'d like to convert.''',
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
a__ : List[str] = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 195 |
"""simple docstring"""
from __future__ import annotations
import requests
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(lowerCAmelCase_ ).json()
def UpperCAmelCase__ (lowerCAmelCase_ = 10 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = "https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
__SCREAMING_SNAKE_CASE = requests.get(lowerCAmelCase_ ).json()[:max_stories]
return [get_hackernews_story(lowerCAmelCase_ ) for story_id in story_ids]
def UpperCAmelCase__ (lowerCAmelCase_ = 10 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = hackernews_top_stories(lowerCAmelCase_ )
return "\n".join("* [{title}]({url})".format(**lowerCAmelCase_ ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown())
| 195 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase__ : Dict = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
),
}
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
__UpperCamelCase : Optional[int] = '''audio-spectrogram-transformer'''
def __init__(self , SCREAMING_SNAKE_CASE__=7_68 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=30_72 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=10 , SCREAMING_SNAKE_CASE__=10 , SCREAMING_SNAKE_CASE__=10_24 , SCREAMING_SNAKE_CASE__=1_28 , **SCREAMING_SNAKE_CASE__ , ) -> Tuple:
"""simple docstring"""
super().__init__(**SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : str = num_hidden_layers
SCREAMING_SNAKE_CASE__ : int = num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE__ : Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : int = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Dict = patch_size
SCREAMING_SNAKE_CASE__ : Optional[int] = qkv_bias
SCREAMING_SNAKE_CASE__ : Optional[int] = frequency_stride
SCREAMING_SNAKE_CASE__ : Any = time_stride
SCREAMING_SNAKE_CASE__ : Optional[int] = max_length
SCREAMING_SNAKE_CASE__ : Any = num_mel_bins
| 25 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 2000000 )-> int:
UpperCamelCase = [0 for i in range(n + 1 )]
UpperCamelCase = 1
UpperCamelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , __UpperCamelCase ):
UpperCamelCase = 1
UpperCamelCase = 0
for i in range(__UpperCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 0 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
class a__ ( UpperCAmelCase__ ):
lowerCamelCase : str ="linear"
lowerCamelCase : int ="cosine"
lowerCamelCase : Union[str, Any] ="cosine_with_restarts"
lowerCamelCase : Tuple ="polynomial"
lowerCamelCase : Dict ="constant"
lowerCamelCase : Dict ="constant_with_warmup"
lowerCamelCase : Union[str, Any] ="piecewise_constant"
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ = -1 ) -> Dict:
return LambdaLR(UpperCamelCase__ , lambda UpperCamelCase__ : 1 , last_epoch=UpperCamelCase__ )
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = -1 ) -> Any:
def lr_lambda(UpperCamelCase__ ):
if current_step < num_warmup_steps:
return float(UpperCamelCase__ ) / float(max(1.0 , UpperCamelCase__ ) )
return 1.0
return LambdaLR(UpperCamelCase__ , UpperCamelCase__ , last_epoch=UpperCamelCase__ )
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = -1 ) -> Any:
__lowerCamelCase = {}
__lowerCamelCase = step_rules.split(''',''' )
for rule_str in rule_list[:-1]:
__lowerCamelCase , __lowerCamelCase = rule_str.split(''':''' )
__lowerCamelCase = int(UpperCamelCase__ )
__lowerCamelCase = float(UpperCamelCase__ )
__lowerCamelCase = value
__lowerCamelCase = float(rule_list[-1] )
def create_rules_function(UpperCamelCase__ , UpperCamelCase__ ):
def rule_func(UpperCamelCase__ ) -> float:
__lowerCamelCase = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(UpperCamelCase__ ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
__lowerCamelCase = create_rules_function(UpperCamelCase__ , UpperCamelCase__ )
return LambdaLR(UpperCamelCase__ , UpperCamelCase__ , last_epoch=UpperCamelCase__ )
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=-1 ) -> Optional[int]:
def lr_lambda(UpperCamelCase__ ):
if current_step < num_warmup_steps:
return float(UpperCamelCase__ ) / float(max(1 , UpperCamelCase__ ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 0.5 , UpperCamelCase__ = -1 ) -> int:
def lr_lambda(UpperCamelCase__ ):
if current_step < num_warmup_steps:
return float(UpperCamelCase__ ) / float(max(1 , UpperCamelCase__ ) )
__lowerCamelCase = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(UpperCamelCase__ ) * 2.0 * progress )) )
return LambdaLR(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 1 , UpperCamelCase__ = -1 ) -> Any:
def lr_lambda(UpperCamelCase__ ):
if current_step < num_warmup_steps:
return float(UpperCamelCase__ ) / float(max(1 , UpperCamelCase__ ) )
__lowerCamelCase = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(UpperCamelCase__ ) * progress) % 1.0) )) )
return LambdaLR(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=1E-7 , UpperCamelCase__=1.0 , UpperCamelCase__=-1 ) -> Dict:
__lowerCamelCase = optimizer.defaults['''lr''']
if not (lr_init > lr_end):
raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})""" )
def lr_lambda(UpperCamelCase__ ):
if current_step < num_warmup_steps:
return float(UpperCamelCase__ ) / float(max(1 , UpperCamelCase__ ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
__lowerCamelCase = lr_init - lr_end
__lowerCamelCase = num_training_steps - num_warmup_steps
__lowerCamelCase = 1 - (current_step - num_warmup_steps) / decay_steps
__lowerCamelCase = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
__UpperCAmelCase ={
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = 1 , UpperCamelCase__ = 1.0 , UpperCamelCase__ = -1 , ) -> int:
__lowerCamelCase = SchedulerType(UpperCamelCase__ )
__lowerCamelCase = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(UpperCamelCase__ , last_epoch=UpperCamelCase__ )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(UpperCamelCase__ , step_rules=UpperCamelCase__ , last_epoch=UpperCamelCase__ )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument.""" )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(UpperCamelCase__ , num_warmup_steps=UpperCamelCase__ , last_epoch=UpperCamelCase__ )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument.""" )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
UpperCamelCase__ , num_warmup_steps=UpperCamelCase__ , num_training_steps=UpperCamelCase__ , num_cycles=UpperCamelCase__ , last_epoch=UpperCamelCase__ , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
UpperCamelCase__ , num_warmup_steps=UpperCamelCase__ , num_training_steps=UpperCamelCase__ , power=UpperCamelCase__ , last_epoch=UpperCamelCase__ , )
return schedule_func(
UpperCamelCase__ , num_warmup_steps=UpperCamelCase__ , num_training_steps=UpperCamelCase__ , last_epoch=UpperCamelCase__ )
| 237 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ ) -> str:
return "".join(chr(ord(UpperCamelCase__ ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 237 | 1 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
if not head:
return True
# split the list to two parts
A_ , A_ : List[str] = head.next, head
while fast and fast.next:
A_ : List[Any] = fast.next.next
A_ : Optional[int] = slow.next
A_ : Dict = slow.next
A_ : List[str] = None # Don't forget here! But forget still works!
# reverse the second part
A_ : Union[str, Any] = None
while second:
A_ : str = second.next
A_ : Tuple = node
A_ : List[Any] = second
A_ : Optional[int] = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
A_ : Tuple = node.next
A_ : Union[str, Any] = head.next
return True
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
A_ : Union[str, Any] = head
while fast and fast.next:
A_ , A_ : Dict = fast.next.next, slow.next
# 2. Push the second half into the stack
A_ : Dict = [slow.val]
while slow.next:
A_ : Optional[Any] = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
A_ : Optional[int] = cur.next
return True
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
if not head or not head.next:
return True
A_ : List[Any] = {}
A_ : str = 0
while head:
if head.val in d:
d[head.val].append(SCREAMING_SNAKE_CASE )
else:
A_ : Any = [pos]
A_ : Dict = head.next
pos += 1
A_ : Union[str, Any] = pos - 1
A_ : Dict = 0
for v in d.values():
if len(SCREAMING_SNAKE_CASE ) % 2 != 0:
middle += 1
else:
A_ : Dict = 0
for i in range(0 , len(SCREAMING_SNAKE_CASE ) ):
if v[i] + v[len(SCREAMING_SNAKE_CASE ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 186 |
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 _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->List[Any]:
'''simple docstring'''
A_ : Optional[int] = jnp.ones((batch_size, length) ) / length
return scores
def _snake_case ( self )->Tuple:
'''simple docstring'''
A_ : Union[str, Any] = None
A_ : Any = 20
A_ : Any = self._get_uniform_logits(batch_size=2 , length=_SCREAMING_SNAKE_CASE )
# tweak scores to not be uniform anymore
A_ : Dict = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
A_ : Tuple = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
A_ : List[str] = jax.nn.softmax(_SCREAMING_SNAKE_CASE , axis=-1 )
A_ : Any = FlaxTemperatureLogitsWarper(temperature=0.5 )
A_ : Optional[int] = FlaxTemperatureLogitsWarper(temperature=1.3 )
A_ : Optional[Any] = jax.nn.softmax(temp_dist_warper_sharper(_SCREAMING_SNAKE_CASE , scores.copy() , cur_len=_SCREAMING_SNAKE_CASE ) , axis=-1 )
A_ : List[Any] = jax.nn.softmax(temp_dist_warper_smoother(_SCREAMING_SNAKE_CASE , scores.copy() , cur_len=_SCREAMING_SNAKE_CASE ) , 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 _snake_case ( self )->List[Any]:
'''simple docstring'''
A_ : Any = None
A_ : List[Any] = 10
A_ : str = 2
# create ramp distribution
A_ : Any = np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE )[None, :] , (batch_size, vocab_size) ).copy()
A_ : List[Any] = ramp_logits[1:, : vocab_size // 2] + vocab_size
A_ : Any = FlaxTopKLogitsWarper(3 )
A_ : Tuple = top_k_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
# 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
A_ : Optional[int] = 5
A_ : List[Any] = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3 )
A_ : Optional[Any] = np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE )[None, :] , (batch_size, length) ).copy()
A_ : Dict = top_k_warp_safety_check(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() , [2, 2] )
def _snake_case ( self )->Any:
'''simple docstring'''
A_ : str = None
A_ : Optional[Any] = 10
A_ : Any = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
A_ : Optional[int] = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.1_5, 0.3, 0.3, 0.2_5]] ) )
A_ : str = FlaxTopPLogitsWarper(0.8 )
A_ : Optional[int] = np.exp(top_p_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
A_ : Tuple = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.2_5]] )
self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# check edge cases with negative and extreme logits
A_ : Union[str, Any] = np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE )[None, :] , (batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
A_ : str = ramp_logits[1] * 1_0_0.0
# make sure at least 2 tokens are kept
A_ : str = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0 )
A_ : str = top_p_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
# 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 _snake_case ( self )->Any:
'''simple docstring'''
A_ : str = 20
A_ : Union[str, Any] = 4
A_ : Optional[Any] = 0
A_ : Union[str, Any] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=_SCREAMING_SNAKE_CASE )
# check that min length is applied at length 5
A_ : int = ids_tensor((batch_size, 20) , vocab_size=20 )
A_ : List[Any] = 5
A_ : Optional[int] = self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : Optional[int] = min_dist_processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float('''inf''' )] )
# check that min length is not applied anymore at length 15
A_ : Tuple = self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : Any = 15
A_ : int = min_dist_processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE ).any() )
def _snake_case ( self )->Optional[Any]:
'''simple docstring'''
A_ : Optional[int] = 20
A_ : Optional[int] = 4
A_ : Optional[int] = 0
A_ : Optional[Any] = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE )
# check that all scores are -inf except the bos_token_id score
A_ : Optional[Any] = ids_tensor((batch_size, 1) , vocab_size=20 )
A_ : str = 1
A_ : List[str] = self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : List[str] = logits_processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
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
A_ : Optional[int] = 3
A_ : List[Any] = self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : Tuple = logits_processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE ).any() )
def _snake_case ( self )->List[str]:
'''simple docstring'''
A_ : Union[str, Any] = 20
A_ : str = 4
A_ : Dict = 0
A_ : Optional[int] = 5
A_ : Tuple = FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE )
# check that all scores are -inf except the eos_token_id when max_length is reached
A_ : List[Any] = ids_tensor((batch_size, 4) , vocab_size=20 )
A_ : Any = 4
A_ : Optional[Any] = self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : List[Any] = logits_processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
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
A_ : int = 3
A_ : Union[str, Any] = self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : Dict = logits_processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE ).any() )
def _snake_case ( self )->str:
'''simple docstring'''
A_ : str = 4
A_ : Dict = 10
A_ : Union[str, Any] = 15
A_ : str = 2
A_ : int = 1
A_ : List[str] = 15
# dummy input_ids and scores
A_ : Tuple = ids_tensor((batch_size, sequence_length) , _SCREAMING_SNAKE_CASE )
A_ : int = input_ids.copy()
A_ : List[Any] = self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = scores.copy()
# instantiate all dist processors
A_ : Dict = FlaxTemperatureLogitsWarper(temperature=0.5 )
A_ : Any = FlaxTopKLogitsWarper(3 )
A_ : List[Any] = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
A_ : List[Any] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=_SCREAMING_SNAKE_CASE )
A_ : Optional[int] = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE )
A_ : Dict = FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = 10
# no processor list
A_ : int = temp_dist_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
A_ : List[str] = top_k_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
A_ : Any = top_p_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
A_ : Dict = min_dist_proc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
A_ : Optional[int] = bos_dist_proc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = eos_dist_proc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
# with processor list
A_ : Any = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
A_ : List[str] = processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
# scores should be equal
self.assertTrue(jnp.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
def _snake_case ( self )->Dict:
'''simple docstring'''
A_ : str = 4
A_ : Dict = 10
A_ : Tuple = 15
A_ : List[str] = 2
A_ : List[str] = 1
A_ : Union[str, Any] = 15
# dummy input_ids and scores
A_ : Any = ids_tensor((batch_size, sequence_length) , _SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = input_ids.copy()
A_ : Optional[Any] = self._get_uniform_logits(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : Tuple = scores.copy()
# instantiate all dist processors
A_ : List[str] = FlaxTemperatureLogitsWarper(temperature=0.5 )
A_ : Optional[Any] = FlaxTopKLogitsWarper(3 )
A_ : int = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
A_ : List[Any] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=_SCREAMING_SNAKE_CASE )
A_ : Dict = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE )
A_ : str = 10
# no processor list
def run_no_processor_list(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
A_ : int = temp_dist_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = top_k_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
A_ : List[Any] = top_p_warp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
A_ : Dict = min_dist_proc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
A_ : Any = bos_dist_proc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = eos_dist_proc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
return scores
# with processor list
def run_processor_list(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
A_ : Optional[int] = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
A_ : Optional[int] = processor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , cur_len=_SCREAMING_SNAKE_CASE )
return scores
A_ : Optional[int] = jax.jit(_SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = jax.jit(_SCREAMING_SNAKE_CASE )
A_ : Dict = jitted_run_no_processor_list(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : List[Any] = jitted_run_processor_list(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# scores should be equal
self.assertTrue(jnp.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
| 186 | 1 |
def _a ( SCREAMING_SNAKE_CASE_ : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 364 |
import enum
import shutil
import sys
UpperCamelCase__ , UpperCamelCase__ = shutil.get_terminal_size()
UpperCamelCase__ = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}
class a__ ( enum.Enum ):
_a : Any = 0
_a : Dict = 1
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Dict="" ):
sys.stdout.write(str(SCREAMING_SNAKE_CASE_ ) + end )
sys.stdout.flush()
def _a ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : str="" ):
forceWrite(F"""\u001b[{color}m{content}\u001b[0m""" , SCREAMING_SNAKE_CASE_ )
def _a ( ):
forceWrite("\r" )
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : str ):
forceWrite(F"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}""" )
def _a ( ):
forceWrite(" " * TERMINAL_WIDTH )
reset_cursor()
def _a ( ):
reset_cursor()
forceWrite("-" * TERMINAL_WIDTH )
| 102 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a : Optional[int]= {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : int= ["XLNetTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[int]= ["XLNetTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Union[str, Any]= [
"XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLNetForMultipleChoice",
"XLNetForQuestionAnswering",
"XLNetForQuestionAnsweringSimple",
"XLNetForSequenceClassification",
"XLNetForTokenClassification",
"XLNetLMHeadModel",
"XLNetModel",
"XLNetPreTrainedModel",
"load_tf_weights_in_xlnet",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Dict= [
"TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLNetForMultipleChoice",
"TFXLNetForQuestionAnsweringSimple",
"TFXLNetForSequenceClassification",
"TFXLNetForTokenClassification",
"TFXLNetLMHeadModel",
"TFXLNetMainLayer",
"TFXLNetModel",
"TFXLNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
_a : Union[str, Any]= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 172 | """simple docstring"""
from __future__ import annotations
_a : List[Any]= []
def __UpperCAmelCase ( UpperCAmelCase_ : list[list[int]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> 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_ : list[list[int]] , UpperCAmelCase_ : int ) -> 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_ ):
__snake_case : Any = 1
solve(UpperCAmelCase_ , row + 1 )
__snake_case : List[str] = 0
return False
def __UpperCAmelCase ( UpperCAmelCase_ : list[list[int]] ) -> 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 : Optional[int]= 8
_a : List[str]= [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))
| 172 | 1 |
from PIL import Image
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Any = (259 * (level + 255)) / (255 * (259 - level))
def contrast(__lowerCamelCase ) -> int:
return int(128 + factor * (c - 128) )
return img.point(lowercase_ )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change contrast to 170
UpperCamelCase__ =change_contrast(img, 170)
cont_img.save('image_data/lena_high_contrast.png', format='png') | 362 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
__snake_case = ['vqvae']
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> List[Any]:
super().__init__()
self.register_modules(unet=__lowerCamelCase , scheduler=__lowerCamelCase , mel=__lowerCamelCase , vqvae=__lowerCamelCase )
def UpperCamelCase_ ( self ) -> int:
return 5_0 if isinstance(self.scheduler , __lowerCamelCase ) else 1_0_0_0
@torch.no_grad()
def __call__( self , __lowerCamelCase = 1 , __lowerCamelCase = None , __lowerCamelCase = None , __lowerCamelCase = 0 , __lowerCamelCase = 0 , __lowerCamelCase = None , __lowerCamelCase = None , __lowerCamelCase = 0 , __lowerCamelCase = 0 , __lowerCamelCase = None , __lowerCamelCase = 0 , __lowerCamelCase = None , __lowerCamelCase = None , __lowerCamelCase=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
_SCREAMING_SNAKE_CASE : List[str] = steps or self.get_default_steps()
self.scheduler.set_timesteps(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
_SCREAMING_SNAKE_CASE : Optional[int] = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
_SCREAMING_SNAKE_CASE : Union[str, Any] = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=__lowerCamelCase , device=self.device , )
_SCREAMING_SNAKE_CASE : Union[str, Any] = noise
_SCREAMING_SNAKE_CASE : Optional[int] = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = self.mel.audio_slice_to_image(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = np.frombuffer(input_image.tobytes() , dtype="uint8" ).reshape(
(input_image.height, input_image.width) )
_SCREAMING_SNAKE_CASE : Optional[int] = (input_image / 2_5_5) * 2 - 1
_SCREAMING_SNAKE_CASE : List[Any] = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.vqvae.encode(torch.unsqueeze(__lowerCamelCase , 0 ) ).latent_dist.sample(
generator=__lowerCamelCase )[0]
_SCREAMING_SNAKE_CASE : int = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
_SCREAMING_SNAKE_CASE : List[Any] = self.scheduler.add_noise(__lowerCamelCase , __lowerCamelCase , self.scheduler.timesteps[start_step - 1] )
_SCREAMING_SNAKE_CASE : int = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
_SCREAMING_SNAKE_CASE : Optional[Any] = int(mask_start_secs * pixels_per_second )
_SCREAMING_SNAKE_CASE : Optional[int] = int(mask_end_secs * pixels_per_second )
_SCREAMING_SNAKE_CASE : Optional[Any] = self.scheduler.add_noise(__lowerCamelCase , __lowerCamelCase , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[str] = self.unet(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )["sample"]
else:
_SCREAMING_SNAKE_CASE : str = self.unet(__lowerCamelCase , __lowerCamelCase )["sample"]
if isinstance(self.scheduler , __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.scheduler.step(
model_output=__lowerCamelCase , timestep=__lowerCamelCase , sample=__lowerCamelCase , eta=__lowerCamelCase , generator=__lowerCamelCase , )["prev_sample"]
else:
_SCREAMING_SNAKE_CASE : List[Any] = self.scheduler.step(
model_output=__lowerCamelCase , timestep=__lowerCamelCase , sample=__lowerCamelCase , generator=__lowerCamelCase , )["prev_sample"]
if mask is not None:
if mask_start > 0:
_SCREAMING_SNAKE_CASE : str = mask[:, step, :, :mask_start]
if mask_end > 0:
_SCREAMING_SNAKE_CASE : Dict = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
_SCREAMING_SNAKE_CASE : Optional[Any] = 1 / self.vqvae.config.scaling_factor * images
_SCREAMING_SNAKE_CASE : Dict = self.vqvae.decode(__lowerCamelCase )["sample"]
_SCREAMING_SNAKE_CASE : Union[str, Any] = (images / 2 + 0.5).clamp(0 , 1 )
_SCREAMING_SNAKE_CASE : Union[str, Any] = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
_SCREAMING_SNAKE_CASE : List[str] = (images * 2_5_5).round().astype("uint8" )
_SCREAMING_SNAKE_CASE : Tuple = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(__lowerCamelCase , mode="RGB" ).convert("L" ) for _ in images) )
_SCREAMING_SNAKE_CASE : Tuple = [self.mel.image_to_audio(__lowerCamelCase ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(__lowerCamelCase )[:, np.newaxis, :] ) , **ImagePipelineOutput(__lowerCamelCase ) )
@torch.no_grad()
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase = 5_0 ) -> np.ndarray:
assert isinstance(self.scheduler , __lowerCamelCase )
self.scheduler.set_timesteps(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = np.array(
[np.frombuffer(image.tobytes() , dtype="uint8" ).reshape((1, image.height, image.width) ) for image in images] )
_SCREAMING_SNAKE_CASE : Union[str, Any] = (sample / 2_5_5) * 2 - 1
_SCREAMING_SNAKE_CASE : Any = torch.Tensor(__lowerCamelCase ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
_SCREAMING_SNAKE_CASE : Optional[int] = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
_SCREAMING_SNAKE_CASE : Optional[Any] = self.scheduler.alphas_cumprod[t]
_SCREAMING_SNAKE_CASE : List[str] = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
_SCREAMING_SNAKE_CASE : Optional[int] = 1 - alpha_prod_t
_SCREAMING_SNAKE_CASE : Optional[int] = self.unet(__lowerCamelCase , __lowerCamelCase )["sample"]
_SCREAMING_SNAKE_CASE : List[str] = (1 - alpha_prod_t_prev) ** 0.5 * model_output
_SCREAMING_SNAKE_CASE : str = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
_SCREAMING_SNAKE_CASE : List[str] = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def UpperCamelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> torch.Tensor:
_SCREAMING_SNAKE_CASE : Any = acos(torch.dot(torch.flatten(__lowerCamelCase ) , torch.flatten(__lowerCamelCase ) ) / torch.norm(__lowerCamelCase ) / torch.norm(__lowerCamelCase ) )
return sin((1 - alpha) * theta ) * xa / sin(__lowerCamelCase ) + sin(alpha * theta ) * xa / sin(__lowerCamelCase ) | 325 | 0 |
def A ( _SCREAMING_SNAKE_CASE ) -> int:
lowerCamelCase : list[list[int]] = [[0 for _ in range(_SCREAMING_SNAKE_CASE )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowerCamelCase : Optional[int] = 1
for n in range(m + 1 ):
for k in range(1 ,_SCREAMING_SNAKE_CASE ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
SCREAMING_SNAKE_CASE__ : List[str] = int(input('Enter a number: ').strip())
print(partition(n))
except ValueError:
print('Please enter a number.')
else:
try:
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(sys.argv[1])
print(partition(n))
except ValueError:
print('Please pass a number.')
| 48 |
def A ( _SCREAMING_SNAKE_CASE = 100_0000 ) -> int:
lowerCamelCase : Tuple = 1
lowerCamelCase : int = 1
lowerCamelCase : Optional[Any] = {1: 1}
for inputa in range(2 ,_SCREAMING_SNAKE_CASE ):
lowerCamelCase : Union[str, Any] = 0
lowerCamelCase : List[str] = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
lowerCamelCase : str = (3 * number) + 1
counter += 1
if inputa not in counters:
lowerCamelCase : str = counter
if counter > pre_counter:
lowerCamelCase : str = inputa
lowerCamelCase : Any = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 48 | 1 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __a ( A__ ):
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE : NestedDataStructureLike[PathLike] , SCREAMING_SNAKE_CASE : Optional[NamedSplit] = None , SCREAMING_SNAKE_CASE : Optional[Features] = None , SCREAMING_SNAKE_CASE : str = None , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : Optional[str] = None , SCREAMING_SNAKE_CASE : Optional[int] = None , **SCREAMING_SNAKE_CASE : Any , ):
'''simple docstring'''
super().__init__(
SCREAMING_SNAKE_CASE , split=SCREAMING_SNAKE_CASE , features=SCREAMING_SNAKE_CASE , cache_dir=SCREAMING_SNAKE_CASE , keep_in_memory=SCREAMING_SNAKE_CASE , streaming=SCREAMING_SNAKE_CASE , num_proc=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
UpperCamelCase__ : Any = field
UpperCamelCase__ : Any = path_or_paths if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else {self.split: path_or_paths}
UpperCamelCase__ : List[str] = Json(
cache_dir=SCREAMING_SNAKE_CASE , data_files=SCREAMING_SNAKE_CASE , features=SCREAMING_SNAKE_CASE , field=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
def __lowercase ( self : Tuple ):
'''simple docstring'''
if self.streaming:
UpperCamelCase__ : Union[str, Any] = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
UpperCamelCase__ : Optional[Any] = None
UpperCamelCase__ : Union[str, Any] = None
UpperCamelCase__ : Optional[Any] = None
UpperCamelCase__ : Union[str, Any] = None
self.builder.download_and_prepare(
download_config=SCREAMING_SNAKE_CASE , download_mode=SCREAMING_SNAKE_CASE , verification_mode=SCREAMING_SNAKE_CASE , base_path=SCREAMING_SNAKE_CASE , num_proc=self.num_proc , )
UpperCamelCase__ : str = self.builder.as_dataset(
split=self.split , verification_mode=SCREAMING_SNAKE_CASE , in_memory=self.keep_in_memory )
return dataset
class __a :
def __init__( self : List[str] , SCREAMING_SNAKE_CASE : Dataset , SCREAMING_SNAKE_CASE : Union[PathLike, BinaryIO] , SCREAMING_SNAKE_CASE : Optional[int] = None , SCREAMING_SNAKE_CASE : Optional[int] = None , **SCREAMING_SNAKE_CASE : Optional[int] , ):
'''simple docstring'''
if num_proc is not None and num_proc <= 0:
raise ValueError(F'num_proc {num_proc} must be an integer > 0.' )
UpperCamelCase__ : str = dataset
UpperCamelCase__ : Dict = path_or_buf
UpperCamelCase__ : List[str] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCamelCase__ : Optional[Any] = num_proc
UpperCamelCase__ : Tuple = "utf-8"
UpperCamelCase__ : Dict = to_json_kwargs
def __lowercase ( self : str ):
'''simple docstring'''
UpperCamelCase__ : List[Any] = self.to_json_kwargs.pop("path_or_buf" , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = self.to_json_kwargs.pop("orient" , "records" )
UpperCamelCase__ : Any = self.to_json_kwargs.pop("lines" , True if orient == "records" else False )
UpperCamelCase__ : Optional[int] = self.to_json_kwargs.pop("index" , False if orient in ["split", "table"] else True )
UpperCamelCase__ : Any = self.to_json_kwargs.pop("compression" , SCREAMING_SNAKE_CASE )
if compression not in [None, "infer", "gzip", "bz2", "xz"]:
raise NotImplementedError(F'`datasets` currently does not support {compression} compression' )
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with fsspec.open(self.path_or_buf , "wb" , compression=SCREAMING_SNAKE_CASE ) as buffer:
UpperCamelCase__ : str = self._write(file_obj=SCREAMING_SNAKE_CASE , orient=SCREAMING_SNAKE_CASE , lines=SCREAMING_SNAKE_CASE , index=SCREAMING_SNAKE_CASE , **self.to_json_kwargs )
else:
if compression:
raise NotImplementedError(
F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
" was passed. Please provide a local path instead." )
UpperCamelCase__ : Tuple = self._write(
file_obj=self.path_or_buf , orient=SCREAMING_SNAKE_CASE , lines=SCREAMING_SNAKE_CASE , index=SCREAMING_SNAKE_CASE , **self.to_json_kwargs )
return written
def __lowercase ( self : List[str] , SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : int = args
UpperCamelCase__ : List[Any] = query_table(
table=self.dataset.data , key=slice(SCREAMING_SNAKE_CASE , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCamelCase__ : Dict = batch.to_pandas().to_json(
path_or_buf=SCREAMING_SNAKE_CASE , orient=SCREAMING_SNAKE_CASE , lines=SCREAMING_SNAKE_CASE , index=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
if not json_str.endswith("\n" ):
json_str += "\n"
return json_str.encode(self.encoding )
def __lowercase ( self : Tuple , SCREAMING_SNAKE_CASE : BinaryIO , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Tuple , **SCREAMING_SNAKE_CASE : Optional[int] , ):
'''simple docstring'''
UpperCamelCase__ : List[str] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating json from Arrow format" , ):
UpperCamelCase__ : Any = self._batch_json((offset, orient, lines, index, to_json_kwargs) )
written += file_obj.write(SCREAMING_SNAKE_CASE )
else:
UpperCamelCase__ , UpperCamelCase__ : Optional[int] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for json_str in logging.tqdm(
pool.imap(
self._batch_json , [(offset, orient, lines, index, to_json_kwargs) for offset in range(0 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating json from Arrow format" , ):
written += file_obj.write(SCREAMING_SNAKE_CASE )
return written | 196 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
UpperCamelCase__ : Optional[Any] = 0
UpperCamelCase__ : Any = len(__lowerCAmelCase ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
UpperCamelCase__ : Optional[int] = i + 1
else:
UpperCamelCase__ : Dict = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F"""{two_pointer([2, 7, 11, 15], 9) = }""") | 196 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : List[Any] ={
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
"""tokenization_electra""": ["""ElectraTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : str =["""ElectraTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Any =[
"""ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ElectraForCausalLM""",
"""ElectraForMaskedLM""",
"""ElectraForMultipleChoice""",
"""ElectraForPreTraining""",
"""ElectraForQuestionAnswering""",
"""ElectraForSequenceClassification""",
"""ElectraForTokenClassification""",
"""ElectraModel""",
"""ElectraPreTrainedModel""",
"""load_tf_weights_in_electra""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Dict =[
"""TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFElectraForMaskedLM""",
"""TFElectraForMultipleChoice""",
"""TFElectraForPreTraining""",
"""TFElectraForQuestionAnswering""",
"""TFElectraForSequenceClassification""",
"""TFElectraForTokenClassification""",
"""TFElectraModel""",
"""TFElectraPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Dict =[
"""FlaxElectraForCausalLM""",
"""FlaxElectraForMaskedLM""",
"""FlaxElectraForMultipleChoice""",
"""FlaxElectraForPreTraining""",
"""FlaxElectraForQuestionAnswering""",
"""FlaxElectraForSequenceClassification""",
"""FlaxElectraForTokenClassification""",
"""FlaxElectraModel""",
"""FlaxElectraPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
from .tokenization_electra import ElectraTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_electra_fast import ElectraTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_electra import (
ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
ElectraForCausalLM,
ElectraForMaskedLM,
ElectraForMultipleChoice,
ElectraForPreTraining,
ElectraForQuestionAnswering,
ElectraForSequenceClassification,
ElectraForTokenClassification,
ElectraModel,
ElectraPreTrainedModel,
load_tf_weights_in_electra,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_electra import (
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFElectraForMaskedLM,
TFElectraForMultipleChoice,
TFElectraForPreTraining,
TFElectraForQuestionAnswering,
TFElectraForSequenceClassification,
TFElectraForTokenClassification,
TFElectraModel,
TFElectraPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_electra import (
FlaxElectraForCausalLM,
FlaxElectraForMaskedLM,
FlaxElectraForMultipleChoice,
FlaxElectraForPreTraining,
FlaxElectraForQuestionAnswering,
FlaxElectraForSequenceClassification,
FlaxElectraForTokenClassification,
FlaxElectraModel,
FlaxElectraPreTrainedModel,
)
else:
import sys
UpperCAmelCase : int =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 128 | """simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> tuple:
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise ValueError('''Capacitance cannot be 0 or negative''' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 177 | 0 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
find_tied_parameters,
get_balanced_memory,
infer_auto_device_map,
load_checkpoint_in_model,
offload_weight,
set_module_tensor_to_device,
)
if is_bnb_available():
import bitsandbytes as bnb
from copy import deepcopy
lowercase__ : Union[str, Any] = logging.getLogger(__name__)
def a__ ( lowercase : torch.nn.Module, lowercase : BnbQuantizationConfig, lowercase : Union[str, os.PathLike] = None, lowercase : Optional[Dict[str, Union[int, str, torch.device]]] = None, lowercase : Optional[List[str]] = None, lowercase : Optional[Dict[Union[int, str], Union[int, str]]] = None, lowercase : Optional[Union[str, os.PathLike]] = None, lowercase : bool = False, ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = bnb_quantization_config.load_in_abit
_UpperCamelCase = bnb_quantization_config.load_in_abit
if load_in_abit and not is_abit_bnb_available():
raise ImportError(
'''You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'''
''' make sure you have the latest version of `bitsandbytes` installed.''' )
if load_in_abit and not is_abit_bnb_available():
raise ValueError(
'''You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'''
'''make sure you have the latest version of `bitsandbytes` installed.''' )
_UpperCamelCase = []
# custom device map
if isinstance(lowerCamelCase_, lowerCamelCase_ ) and len(device_map.keys() ) > 1:
_UpperCamelCase = [key for key, value in device_map.items() if value in ['''disk''', '''cpu''']]
# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
if bnb_quantization_config.skip_modules is None:
_UpperCamelCase = get_keys_to_not_convert(lowerCamelCase_ )
# add cpu modules to skip modules only for 4-bit modules
if load_in_abit:
bnb_quantization_config.skip_modules.extend(lowerCamelCase_ )
_UpperCamelCase = bnb_quantization_config.skip_modules
# We add the modules we want to keep in full precision
if bnb_quantization_config.keep_in_fpaa_modules is None:
_UpperCamelCase = []
_UpperCamelCase = bnb_quantization_config.keep_in_fpaa_modules
modules_to_not_convert.extend(lowerCamelCase_ )
# compatibility with peft
_UpperCamelCase = load_in_abit
_UpperCamelCase = load_in_abit
_UpperCamelCase = get_parameter_device(lowerCamelCase_ )
if model_device.type != "meta":
# quantization of an already loaded model
logger.warning(
'''It is not recommended to quantize a loaded model. '''
'''The model should be instantiated under the `init_empty_weights` context manager.''' )
_UpperCamelCase = replace_with_bnb_layers(lowerCamelCase_, lowerCamelCase_, modules_to_not_convert=lowerCamelCase_ )
# convert param to the right dtype
_UpperCamelCase = bnb_quantization_config.torch_dtype
for name, param in model.state_dict().items():
if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules ):
param.to(torch.floataa )
if param.dtype != torch.floataa:
_UpperCamelCase = name.replace('''.weight''', '''''' ).replace('''.bias''', '''''' )
_UpperCamelCase = getattr(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
if param is not None:
param.to(torch.floataa )
elif torch.is_floating_point(lowerCamelCase_ ):
param.to(lowerCamelCase_ )
if model_device.type == "cuda":
# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
model.cuda(torch.cuda.current_device() )
torch.cuda.empty_cache()
elif torch.cuda.is_available():
model.to(torch.cuda.current_device() )
else:
raise RuntimeError('''No GPU found. A GPU is needed for quantization.''' )
logger.info(
F"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
'''We move the model to cuda.''' )
return model
elif weights_location is None:
raise RuntimeError(
F"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """ )
else:
with init_empty_weights():
_UpperCamelCase = replace_with_bnb_layers(
lowerCamelCase_, lowerCamelCase_, modules_to_not_convert=lowerCamelCase_ )
_UpperCamelCase = get_quantized_model_device_map(
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, max_memory=lowerCamelCase_, no_split_module_classes=lowerCamelCase_, )
if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
_UpperCamelCase = True
_UpperCamelCase = any(x in list(device_map.values() ) for x in ['''cpu''', '''disk'''] )
load_checkpoint_in_model(
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, dtype=bnb_quantization_config.torch_dtype, offload_folder=lowerCamelCase_, offload_state_dict=lowerCamelCase_, keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules, offload_abit_bnb=load_in_abit and offload, )
return dispatch_model(lowerCamelCase_, device_map=lowerCamelCase_, offload_dir=lowerCamelCase_ )
def a__ ( lowercase : Optional[int], lowercase : List[str], lowercase : Tuple=None, lowercase : Union[str, Any]=None, lowercase : List[str]=None ) -> Dict:
"""simple docstring"""
if device_map is None:
if torch.cuda.is_available():
_UpperCamelCase = {'''''': torch.cuda.current_device()}
else:
raise RuntimeError('''No GPU found. A GPU is needed for quantization.''' )
logger.info('''The device_map was not initialized.''' '''Setting device_map to `{\'\':torch.cuda.current_device()}`.''' )
if isinstance(lowerCamelCase_, lowerCamelCase_ ):
if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
raise ValueError(
'''If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '''
'''\'sequential\'.''' )
_UpperCamelCase = {}
special_dtypes.update(
{
name: bnb_quantization_config.torch_dtype
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.skip_modules )
} )
special_dtypes.update(
{
name: torch.floataa
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules )
} )
_UpperCamelCase = {}
_UpperCamelCase = special_dtypes
_UpperCamelCase = no_split_module_classes
_UpperCamelCase = bnb_quantization_config.target_dtype
# get max_memory for each device.
if device_map != "sequential":
_UpperCamelCase = get_balanced_memory(
lowerCamelCase_, low_zero=(device_map == '''balanced_low_0'''), max_memory=lowerCamelCase_, **lowerCamelCase_, )
_UpperCamelCase = max_memory
_UpperCamelCase = infer_auto_device_map(lowerCamelCase_, **lowerCamelCase_ )
if isinstance(lowerCamelCase_, lowerCamelCase_ ):
# check if don't have any quantized module on the cpu
_UpperCamelCase = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules
_UpperCamelCase = {
key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
}
for device in ["cpu", "disk"]:
if device in device_map_without_some_modules.values():
if bnb_quantization_config.load_in_abit:
raise ValueError(
'''
Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
these modules in `torch_dtype`, you need to pass a custom `device_map` to
`load_and_quantize_model`. Check
https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
for more details.
''' )
else:
logger.info(
'''Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit''' )
del device_map_without_some_modules
return device_map
def a__ ( lowercase : List[Any], lowercase : Optional[Any], lowercase : int=None, lowercase : int=None ) -> List[Any]:
"""simple docstring"""
if modules_to_not_convert is None:
_UpperCamelCase = []
_UpperCamelCase = _replace_with_bnb_layers(
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
if not has_been_replaced:
logger.warning(
'''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
''' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'''
''' Please double check your model architecture, or submit an issue on github if you think this is'''
''' a bug.''' )
return model
def a__ ( lowercase : Optional[int], lowercase : List[str], lowercase : List[Any]=None, lowercase : str=None, ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = False
for name, module in model.named_children():
if current_key_name is None:
_UpperCamelCase = []
current_key_name.append(lowerCamelCase_ )
if isinstance(lowerCamelCase_, nn.Linear ) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
_UpperCamelCase = '''.'''.join(lowerCamelCase_ )
_UpperCamelCase = True
for key in modules_to_not_convert:
if (
(key in current_key_name_str) and (key + "." in current_key_name_str)
) or key == current_key_name_str:
_UpperCamelCase = False
break
if proceed:
# Load bnb module with empty weight and replace ``nn.Linear` module
if bnb_quantization_config.load_in_abit:
_UpperCamelCase = bnb.nn.LinearabitLt(
module.in_features, module.out_features, module.bias is not None, has_fpaa_weights=lowerCamelCase_, threshold=bnb_quantization_config.llm_inta_threshold, )
elif bnb_quantization_config.load_in_abit:
_UpperCamelCase = bnb.nn.Linearabit(
module.in_features, module.out_features, module.bias is not None, bnb_quantization_config.bnb_abit_compute_dtype, compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant, quant_type=bnb_quantization_config.bnb_abit_quant_type, )
else:
raise ValueError('''load_in_8bit and load_in_4bit can\'t be both False''' )
_UpperCamelCase = module.weight.data
if module.bias is not None:
_UpperCamelCase = module.bias.data
bnb_module.requires_grad_(lowerCamelCase_ )
setattr(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
_UpperCamelCase = True
if len(list(module.children() ) ) > 0:
_UpperCamelCase = _replace_with_bnb_layers(
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
_UpperCamelCase = has_been_replaced | _has_been_replaced
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def a__ ( lowercase : str ) -> Union[str, Any]:
"""simple docstring"""
with init_empty_weights():
_UpperCamelCase = deepcopy(lowerCamelCase_ ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
_UpperCamelCase = find_tied_parameters(lowerCamelCase_ )
# For compatibility with Accelerate < 0.18
if isinstance(lowerCamelCase_, lowerCamelCase_ ):
_UpperCamelCase = sum(list(tied_params.values() ), [] ) + list(tied_params.keys() )
else:
_UpperCamelCase = sum(lowerCamelCase_, [] )
_UpperCamelCase = len(lowerCamelCase_ ) > 0
# Check if it is a base model
_UpperCamelCase = False
if hasattr(lowerCamelCase_, '''base_model_prefix''' ):
_UpperCamelCase = not hasattr(lowerCamelCase_, model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
_UpperCamelCase = list(model.named_children() )
_UpperCamelCase = [list_modules[-1][0]]
# add last module together with tied weights
_UpperCamelCase = set(lowerCamelCase_ ) - set(lowerCamelCase_ )
_UpperCamelCase = list(set(lowerCamelCase_ ) ) + list(lowerCamelCase_ )
# remove ".weight" from the keys
_UpperCamelCase = ['''.weight''', '''.bias''']
_UpperCamelCase = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
_UpperCamelCase = name.replace(lowerCamelCase_, '''''' )
filtered_module_names.append(lowerCamelCase_ )
return filtered_module_names
def a__ ( lowercase : str ) -> Optional[int]:
"""simple docstring"""
for m in model.modules():
if isinstance(lowerCamelCase_, bnb.nn.Linearabit ):
return True
return False
def a__ ( lowercase : nn.Module ) -> Union[str, Any]:
"""simple docstring"""
return next(parameter.parameters() ).device
def a__ ( lowercase : List[str], lowercase : Dict, lowercase : Tuple, lowercase : Tuple, lowercase : Union[str, Any], lowercase : int, lowercase : str ) -> Union[str, Any]:
"""simple docstring"""
if fpaa_statistics is None:
set_module_tensor_to_device(lowerCamelCase_, lowerCamelCase_, 0, dtype=lowerCamelCase_, value=lowerCamelCase_ )
_UpperCamelCase = param_name
_UpperCamelCase = model
if "." in tensor_name:
_UpperCamelCase = tensor_name.split('''.''' )
for split in splits[:-1]:
_UpperCamelCase = getattr(lowerCamelCase_, lowerCamelCase_ )
if new_module is None:
raise ValueError(F"""{module} has no attribute {split}.""" )
_UpperCamelCase = new_module
_UpperCamelCase = splits[-1]
# offload weights
_UpperCamelCase = False
offload_weight(module._parameters[tensor_name], lowerCamelCase_, lowerCamelCase_, index=lowerCamelCase_ )
if hasattr(module._parameters[tensor_name], '''SCB''' ):
offload_weight(
module._parameters[tensor_name].SCB, param_name.replace('''weight''', '''SCB''' ), lowerCamelCase_, index=lowerCamelCase_, )
else:
offload_weight(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, index=lowerCamelCase_ )
offload_weight(lowerCamelCase_, param_name.replace('''weight''', '''SCB''' ), lowerCamelCase_, index=lowerCamelCase_ )
set_module_tensor_to_device(lowerCamelCase_, lowerCamelCase_, '''meta''', dtype=lowerCamelCase_, value=torch.empty(*param.size() ) )
| 356 |
'''simple docstring'''
def a__ ( lowercase : int, lowercase : int ) -> int:
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(lowercase, x % y )
def a__ ( lowercase : int, lowercase : int ) -> int:
"""simple docstring"""
return (x * y) // greatest_common_divisor(lowercase, lowercase )
def a__ ( lowercase : int = 20 ) -> int:
"""simple docstring"""
_UpperCamelCase = 1
for i in range(1, n + 1 ):
_UpperCamelCase = lcm(lowercase, lowercase )
return g
if __name__ == "__main__":
print(F"""{solution() = }""")
| 287 | 0 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
"""facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}
class a ( UpperCAmelCase__ ):
UpperCamelCase : str = 'encodec'
def __init__( self : str , lowerCAmelCase : Union[str, Any]=[1.5, 3.0, 6.0, 1_2.0, 2_4.0] , lowerCAmelCase : Optional[int]=2_4000 , lowerCAmelCase : Union[str, Any]=1 , lowerCAmelCase : Tuple=False , lowerCAmelCase : Tuple=None , lowerCAmelCase : Optional[int]=None , lowerCAmelCase : int=128 , lowerCAmelCase : Optional[Any]=32 , lowerCAmelCase : str=1 , lowerCAmelCase : Optional[int]=[8, 5, 4, 2] , lowerCAmelCase : Tuple="weight_norm" , lowerCAmelCase : Tuple=7 , lowerCAmelCase : int=7 , lowerCAmelCase : Optional[int]=3 , lowerCAmelCase : int=2 , lowerCAmelCase : int=True , lowerCAmelCase : Any="reflect" , lowerCAmelCase : int=2 , lowerCAmelCase : Union[str, Any]=2 , lowerCAmelCase : Union[str, Any]=1.0 , lowerCAmelCase : Optional[int]=1024 , lowerCAmelCase : List[str]=None , lowerCAmelCase : Union[str, Any]=True , **lowerCAmelCase : List[Any] , ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Union[str, Any] =target_bandwidths
SCREAMING_SNAKE_CASE_: Any =sampling_rate
SCREAMING_SNAKE_CASE_: Tuple =audio_channels
SCREAMING_SNAKE_CASE_: Optional[Any] =normalize
SCREAMING_SNAKE_CASE_: List[Any] =chunk_length_s
SCREAMING_SNAKE_CASE_: Dict =overlap
SCREAMING_SNAKE_CASE_: str =hidden_size
SCREAMING_SNAKE_CASE_: Any =num_filters
SCREAMING_SNAKE_CASE_: List[str] =num_residual_layers
SCREAMING_SNAKE_CASE_: List[str] =upsampling_ratios
SCREAMING_SNAKE_CASE_: Union[str, Any] =norm_type
SCREAMING_SNAKE_CASE_: Optional[int] =kernel_size
SCREAMING_SNAKE_CASE_: Optional[int] =last_kernel_size
SCREAMING_SNAKE_CASE_: Optional[int] =residual_kernel_size
SCREAMING_SNAKE_CASE_: Union[str, Any] =dilation_growth_rate
SCREAMING_SNAKE_CASE_: List[Any] =use_causal_conv
SCREAMING_SNAKE_CASE_: List[str] =pad_mode
SCREAMING_SNAKE_CASE_: Optional[Any] =compress
SCREAMING_SNAKE_CASE_: List[Any] =num_lstm_layers
SCREAMING_SNAKE_CASE_: Optional[int] =trim_right_ratio
SCREAMING_SNAKE_CASE_: List[str] =codebook_size
SCREAMING_SNAKE_CASE_: Optional[int] =codebook_dim if codebook_dim is not None else hidden_size
SCREAMING_SNAKE_CASE_: List[str] =use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f'''self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}''' )
super().__init__(**_lowerCAmelCase )
@property
def lowerCamelCase__ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def lowerCamelCase__ ( self : str ) -> List[Any]:
'''simple docstring'''
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def lowerCamelCase__ ( self : Any ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Dict =np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def lowerCamelCase__ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 173 |
'''simple docstring'''
from datetime import datetime
import requests
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase ='https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
__lowercase =requests.get(base_url + url ).json()[0]['urls'][0]['src']
return requests.get(_lowerCAmelCase ).content
if __name__ == "__main__":
lowerCamelCase = input("""Enter Video/IGTV url: """).strip()
lowerCamelCase = f"{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"
with open(file_name, """wb""") as fp:
fp.write(download_video(url))
print(f"Done. Video saved to disk as {file_name}.")
| 166 | 0 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
lowercase ='src/transformers'
# Matches is_xxx_available()
lowercase =re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
lowercase =re.compile(r'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowercase =re.compile(r'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
lowercase =re.compile(r'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
lowercase =re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowercase =re.compile(r'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma: "MyModel",
lowercase =re.compile('^\s+"([^"]+)",')
# Catches a line with objects between brackets only: ["foo", "bar"],
lowercase =re.compile('^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
lowercase =re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
lowercase =re.compile(r'^\s*try:')
# Catches a line with else:
lowercase =re.compile(r'^\s*else:')
def lowerCamelCase__ ( __lowerCamelCase ):
'''simple docstring'''
if _re_test_backend.search(_A ) is None:
return None
_UpperCAmelCase : Dict =[b[0] for b in _re_backend.findall(_A )]
backends.sort()
return "_and_".join(_A )
def lowerCamelCase__ ( __lowerCamelCase ):
'''simple docstring'''
with open(_A , 'r' , encoding='utf-8' , newline='\n' ) as f:
_UpperCAmelCase : Optional[int] =f.readlines()
_UpperCAmelCase : Tuple =0
while line_index < len(_A ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(_A ):
return None
# First grab the objects without a specific backend in _import_structure
_UpperCAmelCase : Optional[int] =[]
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
_UpperCAmelCase : Dict =lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(_A ):
_UpperCAmelCase : Any =_re_one_line_import_struct.search(_A ).groups()[0]
_UpperCAmelCase : Union[str, Any] =re.findall('\[([^\]]+)\]' , _A )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
_UpperCAmelCase : List[Any] =_re_import_struct_key_value.search(_A )
if single_line_import_search is not None:
_UpperCAmelCase : Union[str, Any] =[obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(_A ) > 0]
objects.extend(_A )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
line_index += 1
_UpperCAmelCase : List[str] ={'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
_UpperCAmelCase : Dict =find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_UpperCAmelCase : List[Any] =None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_UpperCAmelCase : int =[]
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
_UpperCAmelCase : Union[str, Any] =lines[line_index]
if _re_import_struct_add_one.search(_A ) is not None:
objects.append(_re_import_struct_add_one.search(_A ).groups()[0] )
elif _re_import_struct_add_many.search(_A ) is not None:
_UpperCAmelCase : Tuple =_re_import_struct_add_many.search(_A ).groups()[0].split(', ' )
_UpperCAmelCase : Any =[obj[1:-1] for obj in imports if len(_A ) > 0]
objects.extend(_A )
elif _re_between_brackets.search(_A ) is not None:
_UpperCAmelCase : Dict =_re_between_brackets.search(_A ).groups()[0].split(', ' )
_UpperCAmelCase : List[str] =[obj[1:-1] for obj in imports if len(_A ) > 0]
objects.extend(_A )
elif _re_quote_object.search(_A ) is not None:
objects.append(_re_quote_object.search(_A ).groups()[0] )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 1_2 + '"' ):
objects.append(line[1_3:-3] )
line_index += 1
_UpperCAmelCase : Dict =objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
_UpperCAmelCase : Optional[Any] =[]
while (
line_index < len(_A )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
_UpperCAmelCase : Any =lines[line_index]
_UpperCAmelCase : Dict =_re_import.search(_A )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
_UpperCAmelCase : List[str] ={'none': objects}
# Let's continue with backend-specific objects
while line_index < len(_A ):
# If the line is an if is_backend_available, we grab all objects associated.
_UpperCAmelCase : Tuple =find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_UpperCAmelCase : Optional[Any] =None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_UpperCAmelCase : Dict =[]
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
_UpperCAmelCase : List[Any] =lines[line_index]
_UpperCAmelCase : int =_re_import.search(_A )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 1_2 ):
objects.append(line[1_2:-2] )
line_index += 1
_UpperCAmelCase : Tuple =objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def lowerCamelCase__ ( __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
def find_duplicates(__lowerCamelCase ):
return [k for k, v in collections.Counter(_A ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
_UpperCAmelCase : Dict =[]
for key in import_dict_objects.keys():
_UpperCAmelCase : Tuple =find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(f"Duplicate _import_structure definitions for: {duplicate_imports}" )
_UpperCAmelCase : Dict =find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(f"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
_UpperCAmelCase : Dict ='base imports' if key == 'none' else f"{key} backend"
errors.append(f"Differences for {name}:" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(f" {a} in TYPE_HINT but not in _import_structure." )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(f" {a} in _import_structure but not in TYPE_HINT." )
return errors
def lowerCamelCase__ ( ):
'''simple docstring'''
_UpperCAmelCase : Dict =[]
for root, _, files in os.walk(_A ):
if "__init__.py" in files:
_UpperCAmelCase : Tuple =os.path.join(_A , '__init__.py' )
_UpperCAmelCase : Dict =parse_init(_A )
if objects is not None:
_UpperCAmelCase : Optional[Any] =analyze_results(*_A )
if len(_A ) > 0:
_UpperCAmelCase : List[str] =f"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
failures.append('\n'.join(_A ) )
if len(_A ) > 0:
raise ValueError('\n\n'.join(_A ) )
def lowerCamelCase__ ( ):
'''simple docstring'''
_UpperCAmelCase : str =[]
for path, directories, files in os.walk(_A ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(_A )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(_A ) / folder).glob('*.py' ) ) ) == 0:
continue
_UpperCAmelCase : Dict =str((Path(_A ) / folder).relative_to(_A ) )
_UpperCAmelCase : Any =short_path.replace(os.path.sep , '.' )
submodules.append(_A )
for fname in files:
if fname == "__init__.py":
continue
_UpperCAmelCase : int =str((Path(_A ) / fname).relative_to(_A ) )
_UpperCAmelCase : Dict =short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(_A )
return submodules
lowercase =[
'convert_pytorch_checkpoint_to_tf2',
'modeling_flax_pytorch_utils',
]
def lowerCamelCase__ ( ):
'''simple docstring'''
_UpperCAmelCase : Dict =importlib.util.spec_from_file_location(
'transformers' , os.path.join(_A , '__init__.py' ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
_UpperCAmelCase : Union[str, Any] =spec.loader.load_module()
_UpperCAmelCase : int =[
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(_A ) > 0:
_UpperCAmelCase : Optional[Any] ='\n'.join(f"- {module}" for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registered in the main init of Transformers:\n'
f"{list_of_modules}\n"
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 358 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class __magic_name__ ( lowerCAmelCase ):
UpperCAmelCase =42
UpperCAmelCase =42
class __magic_name__ ( nn.Module ):
UpperCAmelCase =42
UpperCAmelCase =(1_6, 3_2, 9_6, 2_5_6)
UpperCAmelCase =jnp.floataa
def lowerCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : str =nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_UpperCAmelCase : Tuple =[]
for i in range(len(self.block_out_channels) - 1):
_UpperCAmelCase : Optional[int] =self.block_out_channels[i]
_UpperCAmelCase : List[Any] =self.block_out_channels[i + 1]
_UpperCAmelCase : Tuple =nn.Conv(
snake_case , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case)
_UpperCAmelCase : Optional[int] =nn.Conv(
snake_case , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case)
_UpperCAmelCase : Dict =blocks
_UpperCAmelCase : Tuple =nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case) -> List[str]:
'''simple docstring'''
_UpperCAmelCase : int =self.conv_in(snake_case)
_UpperCAmelCase : Any =nn.silu(snake_case)
for block in self.blocks:
_UpperCAmelCase : Optional[Any] =block(snake_case)
_UpperCAmelCase : Union[str, Any] =nn.silu(snake_case)
_UpperCAmelCase : str =self.conv_out(snake_case)
return embedding
@flax_register_to_config
class __magic_name__ ( nn.Module ,lowerCAmelCase ,lowerCAmelCase ):
UpperCAmelCase =3_2
UpperCAmelCase =4
UpperCAmelCase =(
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
UpperCAmelCase =False
UpperCAmelCase =(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
UpperCAmelCase =2
UpperCAmelCase =8
UpperCAmelCase =None
UpperCAmelCase =1_2_8_0
UpperCAmelCase =0.0
UpperCAmelCase =False
UpperCAmelCase =jnp.floataa
UpperCAmelCase =True
UpperCAmelCase =0
UpperCAmelCase ="rgb"
UpperCAmelCase =(1_6, 3_2, 9_6, 2_5_6)
def lowerCAmelCase ( self , snake_case) -> FrozenDict:
'''simple docstring'''
# init input tensors
_UpperCAmelCase : Any =(1, self.in_channels, self.sample_size, self.sample_size)
_UpperCAmelCase : Optional[Any] =jnp.zeros(snake_case , dtype=jnp.floataa)
_UpperCAmelCase : Optional[int] =jnp.ones((1,) , dtype=jnp.intaa)
_UpperCAmelCase : str =jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa)
_UpperCAmelCase : Optional[Any] =(1, 3, self.sample_size * 8, self.sample_size * 8)
_UpperCAmelCase : int =jnp.zeros(snake_case , dtype=jnp.floataa)
_UpperCAmelCase , _UpperCAmelCase : List[Any] =jax.random.split(snake_case)
_UpperCAmelCase : str ={'params': params_rng, 'dropout': dropout_rng}
return self.init(snake_case , snake_case , snake_case , snake_case , snake_case)["params"]
def lowerCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =self.block_out_channels
_UpperCAmelCase : Tuple =block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
_UpperCAmelCase : Optional[Any] =self.num_attention_heads or self.attention_head_dim
# input
_UpperCAmelCase : Tuple =nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
_UpperCAmelCase : Union[str, Any] =FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift)
_UpperCAmelCase : str =FlaxTimestepEmbedding(snake_case , dtype=self.dtype)
_UpperCAmelCase : Optional[Any] =FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
_UpperCAmelCase : Optional[int] =self.only_cross_attention
if isinstance(snake_case , snake_case):
_UpperCAmelCase : Dict =(only_cross_attention,) * len(self.down_block_types)
if isinstance(snake_case , snake_case):
_UpperCAmelCase : Optional[Any] =(num_attention_heads,) * len(self.down_block_types)
# down
_UpperCAmelCase : int =[]
_UpperCAmelCase : Optional[int] =[]
_UpperCAmelCase : List[str] =block_out_channels[0]
_UpperCAmelCase : int =nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case)
for i, down_block_type in enumerate(self.down_block_types):
_UpperCAmelCase : Tuple =output_channel
_UpperCAmelCase : Dict =block_out_channels[i]
_UpperCAmelCase : str =i == len(snake_case) - 1
if down_block_type == "CrossAttnDownBlock2D":
_UpperCAmelCase : Tuple =FlaxCrossAttnDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
_UpperCAmelCase : Optional[Any] =FlaxDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(snake_case)
for _ in range(self.layers_per_block):
_UpperCAmelCase : Tuple =nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case)
if not is_final_block:
_UpperCAmelCase : List[str] =nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case)
_UpperCAmelCase : List[Any] =down_blocks
_UpperCAmelCase : Optional[Any] =controlnet_down_blocks
# mid
_UpperCAmelCase : int =block_out_channels[-1]
_UpperCAmelCase : Optional[Any] =FlaxUNetMidBlockaDCrossAttn(
in_channels=snake_case , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
_UpperCAmelCase : Optional[int] =nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case , snake_case , snake_case , snake_case , snake_case = 1.0 , snake_case = True , snake_case = False , ) -> Union[FlaxControlNetOutput, Tuple]:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] =self.controlnet_conditioning_channel_order
if channel_order == "bgr":
_UpperCAmelCase : Optional[int] =jnp.flip(snake_case , axis=1)
# 1. time
if not isinstance(snake_case , jnp.ndarray):
_UpperCAmelCase : Optional[int] =jnp.array([timesteps] , dtype=jnp.intaa)
elif isinstance(snake_case , jnp.ndarray) and len(timesteps.shape) == 0:
_UpperCAmelCase : str =timesteps.astype(dtype=jnp.floataa)
_UpperCAmelCase : Dict =jnp.expand_dims(snake_case , 0)
_UpperCAmelCase : int =self.time_proj(snake_case)
_UpperCAmelCase : Any =self.time_embedding(snake_case)
# 2. pre-process
_UpperCAmelCase : str =jnp.transpose(snake_case , (0, 2, 3, 1))
_UpperCAmelCase : Any =self.conv_in(snake_case)
_UpperCAmelCase : List[str] =jnp.transpose(snake_case , (0, 2, 3, 1))
_UpperCAmelCase : Optional[int] =self.controlnet_cond_embedding(snake_case)
sample += controlnet_cond
# 3. down
_UpperCAmelCase : Tuple =(sample,)
for down_block in self.down_blocks:
if isinstance(snake_case , snake_case):
_UpperCAmelCase , _UpperCAmelCase : Dict =down_block(snake_case , snake_case , snake_case , deterministic=not train)
else:
_UpperCAmelCase , _UpperCAmelCase : Dict =down_block(snake_case , snake_case , deterministic=not train)
down_block_res_samples += res_samples
# 4. mid
_UpperCAmelCase : List[Any] =self.mid_block(snake_case , snake_case , snake_case , deterministic=not train)
# 5. contronet blocks
_UpperCAmelCase : Union[str, Any] =()
for down_block_res_sample, controlnet_block in zip(snake_case , self.controlnet_down_blocks):
_UpperCAmelCase : List[str] =controlnet_block(snake_case)
controlnet_down_block_res_samples += (down_block_res_sample,)
_UpperCAmelCase : Optional[int] =controlnet_down_block_res_samples
_UpperCAmelCase : List[str] =self.controlnet_mid_block(snake_case)
# 6. scaling
_UpperCAmelCase : Tuple =[sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=snake_case , mid_block_res_sample=snake_case)
| 242 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : Any , __snake_case : int ) -> None:
UpperCAmelCase : List[Any] = value
UpperCAmelCase : Node | None = None
UpperCAmelCase : Node | None = None
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : str , __snake_case : Node ) -> None:
UpperCAmelCase : Optional[int] = tree
def A ( self : Union[str, Any] , __snake_case : Node | None ) -> int:
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self : List[Any] ) -> Iterator[int]:
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 23 | """simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( _UpperCamelCase : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(_UpperCamelCase ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(_UpperCamelCase ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 150 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
a__ = get_logger()
a__ = None
class UpperCAmelCase_ ( TensorFormatter[Mapping, "jax.Array", Mapping] ):
"""simple docstring"""
def __init__( self , _a=None , _a=None , **_a ) -> str:
super().__init__(features=_a )
import jax
from jaxlib.xla_client import Device
if isinstance(_a , _a ):
raise ValueError(
F"""Expected {device} to be a `str` not {type(_a )}, as `jaxlib.xla_extension.Device` """
'''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
'''the device with `str()` to get its string identifier that will be internally mapped '''
'''to the actual `jaxlib.xla_extension.Device`.''' )
_a : List[str] = device if isinstance(_a , _a ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
_a : Dict = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
F"""Device with string identifier {self.device} not listed among the available """
F"""devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default """
F"""device: {str(jax.devices()[0] )}.""" )
_a : Any = str(jax.devices()[0] )
_a : Optional[Any] = jnp_array_kwargs
@staticmethod
def __lowercase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
import jax
return {str(_a ): device for device in jax.devices()}
def __lowercase ( self , _a ) -> str:
import jax
import jax.numpy as jnp
if isinstance(_a , _a ) and column:
if all(
isinstance(_a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(_a , axis=0 )
return column
def __lowercase ( self , _a ) -> Optional[Any]:
import jax
import jax.numpy as jnp
if isinstance(_a , (str, bytes, type(_a )) ):
return value
elif isinstance(_a , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
_a : str = {}
if isinstance(_a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
_a : Dict = {'''dtype''': jnp.intaa}
else:
_a : Optional[int] = {'''dtype''': jnp.intaa}
elif isinstance(_a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
_a : Optional[Any] = {'''dtype''': jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(_a , PIL.Image.Image ):
_a : int = np.asarray(_a )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
_a : str = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(_a , **{**default_dtype, **self.jnp_array_kwargs} )
def __lowercase ( self , _a ) -> int:
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(_a , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(_a , '''__array__''' ) and not isinstance(_a , jax.Array ):
_a : List[Any] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(_a , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(_a ) for substruct in data_struct] )
elif isinstance(_a , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(_a ) for substruct in data_struct] )
return self._tensorize(_a )
def __lowercase ( self , _a ) -> List[Any]:
return map_nested(self._recursive_tensorize , _a , map_list=_a )
def __lowercase ( self , _a ) -> Mapping:
_a : Any = self.numpy_arrow_extractor().extract_row(_a )
_a : int = self.python_features_decoder.decode_row(_a )
return self.recursive_tensorize(_a )
def __lowercase ( self , _a ) -> "jax.Array":
_a : int = self.numpy_arrow_extractor().extract_column(_a )
_a : Dict = self.python_features_decoder.decode_column(_a , pa_table.column_names[0] )
_a : Optional[Any] = self.recursive_tensorize(_a )
_a : Dict = self._consolidate(_a )
return column
def __lowercase ( self , _a ) -> Mapping:
_a : Union[str, Any] = self.numpy_arrow_extractor().extract_batch(_a )
_a : Optional[Any] = self.python_features_decoder.decode_batch(_a )
_a : Any = self.recursive_tensorize(_a )
for column_name in batch:
_a : int = self._consolidate(batch[column_name] )
return batch
| 15 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : Union[str, Any] ,__a : str ,__a : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return [
int(1_000 * (box[0] / width) ),
int(1_000 * (box[1] / height) ),
int(1_000 * (box[2] / width) ),
int(1_000 * (box[3] / height) ),
]
def __UpperCAmelCase ( __a : np.ndarray ,__a : Optional[str] ,__a : Optional[str] ) -> List[Any]:
"""simple docstring"""
_a : str = to_pil_image(__a )
_a , _a : Optional[Any] = pil_image.size
_a : Tuple = pytesseract.image_to_data(__a ,lang=__a ,output_type='''dict''' ,config=__a )
_a , _a , _a , _a , _a : List[str] = data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']
# filter empty words and corresponding coordinates
_a : Dict = [idx for idx, word in enumerate(__a ) if not word.strip()]
_a : str = [word for idx, word in enumerate(__a ) if idx not in irrelevant_indices]
_a : List[str] = [coord for idx, coord in enumerate(__a ) if idx not in irrelevant_indices]
_a : Union[str, Any] = [coord for idx, coord in enumerate(__a ) if idx not in irrelevant_indices]
_a : str = [coord for idx, coord in enumerate(__a ) if idx not in irrelevant_indices]
_a : Union[str, Any] = [coord for idx, coord in enumerate(__a ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
_a : int = []
for x, y, w, h in zip(__a ,__a ,__a ,__a ):
_a : List[str] = [x, y, x + w, y + h]
actual_boxes.append(__a )
# finally, normalize the bounding boxes
_a : Dict = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(__a ,__a ,__a ) )
assert len(__a ) == len(__a ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = ["pixel_values"]
def __init__( self , _a = True , _a = None , _a = PILImageResampling.BILINEAR , _a = True , _a = 1 / 2_5_5 , _a = True , _a = None , _a = None , _a = True , _a = None , _a = "" , **_a , ) -> None:
super().__init__(**_a )
_a : List[str] = size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
_a : Union[str, Any] = get_size_dict(_a )
_a : int = do_resize
_a : Optional[int] = size
_a : str = resample
_a : str = do_rescale
_a : Any = rescale_value
_a : Optional[Any] = do_normalize
_a : int = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_a : List[str] = image_std if image_std is not None else IMAGENET_STANDARD_STD
_a : List[Any] = apply_ocr
_a : Optional[int] = ocr_lang
_a : Tuple = tesseract_config
def __lowercase ( self , _a , _a , _a = PILImageResampling.BILINEAR , _a = None , **_a , ) -> np.ndarray:
_a : Any = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_a : Optional[int] = (size['''height'''], size['''width'''])
return resize(_a , size=_a , resample=_a , data_format=_a , **_a )
def __lowercase ( self , _a , _a , _a = None , **_a , ) -> np.ndarray:
return rescale(_a , scale=_a , data_format=_a , **_a )
def __lowercase ( self , _a , _a , _a , _a = None , **_a , ) -> np.ndarray:
return normalize(_a , mean=_a , std=_a , data_format=_a , **_a )
def __lowercase ( self , _a , _a = None , _a = None , _a=None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = ChannelDimension.FIRST , **_a , ) -> PIL.Image.Image:
_a : Optional[int] = do_resize if do_resize is not None else self.do_resize
_a : Union[str, Any] = size if size is not None else self.size
_a : Any = get_size_dict(_a )
_a : List[str] = resample if resample is not None else self.resample
_a : int = do_rescale if do_rescale is not None else self.do_rescale
_a : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
_a : int = do_normalize if do_normalize is not None else self.do_normalize
_a : str = image_mean if image_mean is not None else self.image_mean
_a : Tuple = image_std if image_std is not None else self.image_std
_a : Any = apply_ocr if apply_ocr is not None else self.apply_ocr
_a : int = ocr_lang if ocr_lang is not None else self.ocr_lang
_a : Optional[int] = tesseract_config if tesseract_config is not None else self.tesseract_config
_a : List[Any] = make_list_of_images(_a )
if not valid_images(_a ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.''' )
# All transformations expect numpy arrays.
_a : Any = [to_numpy_array(_a ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , '''pytesseract''' )
_a : str = []
_a : str = []
for image in images:
_a , _a : Union[str, Any] = apply_tesseract(_a , _a , _a )
words_batch.append(_a )
boxes_batch.append(_a )
if do_resize:
_a : List[str] = [self.resize(image=_a , size=_a , resample=_a ) for image in images]
if do_rescale:
_a : Optional[Any] = [self.rescale(image=_a , scale=_a ) for image in images]
if do_normalize:
_a : List[Any] = [self.normalize(image=_a , mean=_a , std=_a ) for image in images]
_a : List[str] = [to_channel_dimension_format(_a , _a ) for image in images]
_a : List[str] = BatchFeature(data={'''pixel_values''': images} , tensor_type=_a )
if apply_ocr:
_a : Optional[int] = words_batch
_a : List[Any] = boxes_batch
return data
| 15 | 1 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, 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 (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class a__:
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=13 , __lowerCAmelCase=7 , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=99 , __lowerCAmelCase=24 , __lowerCAmelCase=2 , __lowerCAmelCase=6 , __lowerCAmelCase=37 , __lowerCAmelCase="gelu" , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=512 , __lowerCAmelCase=16 , __lowerCAmelCase=2 , __lowerCAmelCase=0.02 , __lowerCAmelCase=3 , __lowerCAmelCase=None , __lowerCAmelCase=1000 , ):
"""simple docstring"""
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = seq_length
lowerCAmelCase = is_training
lowerCAmelCase = use_input_mask
lowerCAmelCase = use_token_type_ids
lowerCAmelCase = use_labels
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = type_vocab_size
lowerCAmelCase = type_sequence_label_size
lowerCAmelCase = initializer_range
lowerCAmelCase = num_labels
lowerCAmelCase = scope
lowerCAmelCase = range_bbox
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox)
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
lowerCAmelCase = bbox[i, j, 3]
lowerCAmelCase = bbox[i, j, 1]
lowerCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
lowerCAmelCase = bbox[i, j, 2]
lowerCAmelCase = bbox[i, j, 0]
lowerCAmelCase = t
lowerCAmelCase = None
if self.use_input_mask:
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2)
lowerCAmelCase = None
if self.use_token_type_ids:
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
lowerCAmelCase = None
lowerCAmelCase = None
if self.use_labels:
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
lowerCAmelCase = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def a_ ( self):
"""simple docstring"""
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def a_ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ):
"""simple docstring"""
lowerCAmelCase = LiltModel(config=__lowerCAmelCase)
model.to(__lowerCAmelCase)
model.eval()
lowerCAmelCase = model(__lowerCAmelCase , bbox=__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase)
lowerCAmelCase = model(__lowerCAmelCase , bbox=__lowerCAmelCase , token_type_ids=__lowerCAmelCase)
lowerCAmelCase = model(__lowerCAmelCase , bbox=__lowerCAmelCase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size))
def a_ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = LiltForTokenClassification(config=__lowerCAmelCase)
model.to(__lowerCAmelCase)
model.eval()
lowerCAmelCase = model(
__lowerCAmelCase , bbox=__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def a_ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ):
"""simple docstring"""
lowerCAmelCase = LiltForQuestionAnswering(config=__lowerCAmelCase)
model.to(__lowerCAmelCase)
model.eval()
lowerCAmelCase = model(
__lowerCAmelCase , bbox=__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
"""input_ids""": input_ids,
"""bbox""": bbox,
"""token_type_ids""": token_type_ids,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_torch
class a__( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : int = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCAmelCase_ : Dict = (
{
'''feature-extraction''': LiltModel,
'''question-answering''': LiltForQuestionAnswering,
'''text-classification''': LiltForSequenceClassification,
'''token-classification''': LiltForTokenClassification,
'''zero-shot''': LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase_ : Optional[Any] = False
UpperCAmelCase_ : List[Any] = False
def a_ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase):
"""simple docstring"""
return True
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = LiltModelTester(self)
lowerCAmelCase = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37)
def a_ ( self):
"""simple docstring"""
self.config_tester.run_common_tests()
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase)
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowerCAmelCase = type
self.model_tester.create_and_check_model(*__lowerCAmelCase)
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase)
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase)
@slow
def a_ ( self):
"""simple docstring"""
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = LiltModel.from_pretrained(__lowerCAmelCase)
self.assertIsNotNone(__lowerCAmelCase)
@require_torch
@slow
class a__( unittest.TestCase ):
'''simple docstring'''
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base""").to(__lowerCAmelCase)
lowerCAmelCase = torch.tensor([[1, 2]] , device=__lowerCAmelCase)
lowerCAmelCase = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=__lowerCAmelCase)
# forward pass
with torch.no_grad():
lowerCAmelCase = model(input_ids=__lowerCAmelCase , bbox=__lowerCAmelCase)
lowerCAmelCase = torch.Size([1, 2, 768])
lowerCAmelCase = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=__lowerCAmelCase , )
self.assertTrue(outputs.last_hidden_state.shape , __lowerCAmelCase)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , __lowerCAmelCase , atol=1E-3))
| 272 | '''simple docstring'''
from math import sqrt
def snake_case__ ( _A: int = 1000000 ) -> int:
'''simple docstring'''
lowerCAmelCase = 0
lowerCAmelCase = 0
lowerCAmelCase = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
num_cuboids += (
min(_A , sum_shortest_sides // 2 )
- max(1 , sum_shortest_sides - max_cuboid_size )
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(f'{solution() = }')
| 272 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
lowerCAmelCase_ = imread(r"""digital_image_processing/image_data/lena_small.jpg""")
lowerCAmelCase_ = cvtColor(img, COLOR_BGR2GRAY)
def lowerCamelCase_ ( )-> Optional[int]:
_snake_case : List[Any] = cn.convert_to_negative(lowerCAmelCase )
# assert negative_img array for at least one True
assert negative_img.any()
def lowerCamelCase_ ( )-> str:
with Image.open('digital_image_processing/image_data/lena_small.jpg' ) as img:
# Work around assertion for response
assert str(cc.change_contrast(lowerCAmelCase , 1_10 ) ).startswith(
'<PIL.Image.Image image mode=RGB size=100x100 at' )
def lowerCamelCase_ ( )-> int:
_snake_case : Dict = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def lowerCamelCase_ ( )-> Tuple:
_snake_case : Any = imread('digital_image_processing/image_data/lena_small.jpg' , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
_snake_case : Any = canny.canny(lowerCAmelCase )
# assert canny array for at least one True
assert canny_array.any()
def lowerCamelCase_ ( )-> Dict:
assert gg.gaussian_filter(lowerCAmelCase , 5 , sigma=0.9 ).all()
def lowerCamelCase_ ( )-> int:
# laplace diagonals
_snake_case : List[str] = array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]] )
_snake_case : Any = conv.img_convolve(lowerCAmelCase , lowerCAmelCase ).astype(lowerCAmelCase )
assert res.any()
def lowerCamelCase_ ( )-> Union[str, Any]:
assert med.median_filter(lowerCAmelCase , 3 ).any()
def lowerCamelCase_ ( )-> List[Any]:
_snake_case , _snake_case : Any = sob.sobel_filter(lowerCAmelCase )
assert grad.any() and theta.any()
def lowerCamelCase_ ( )-> int:
_snake_case : Tuple = sp.make_sepia(lowerCAmelCase , 20 )
assert sepia.all()
def lowerCamelCase_ ( lowerCAmelCase: str = "digital_image_processing/image_data/lena_small.jpg" )-> List[str]:
_snake_case : Optional[int] = bs.Burkes(imread(lowerCAmelCase , 1 ) , 1_20 )
burkes.process()
assert burkes.output_img.any()
def lowerCamelCase_ ( lowerCAmelCase: str = "digital_image_processing/image_data/lena_small.jpg" , )-> List[Any]:
_snake_case : Optional[int] = rs.NearestNeighbour(imread(lowerCAmelCase , 1 ) , 4_00 , 2_00 )
nn.process()
assert nn.output.any()
def lowerCamelCase_ ( )-> Dict:
_snake_case : str = 'digital_image_processing/image_data/lena.jpg'
# Reading the image and converting it to grayscale.
_snake_case : Dict = imread(lowerCAmelCase , 0 )
# Test for get_neighbors_pixel function() return not None
_snake_case : List[Any] = 0
_snake_case : Union[str, Any] = 0
_snake_case : Tuple = image[x_coordinate][y_coordinate]
_snake_case : List[Any] = lbp.get_neighbors_pixel(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
_snake_case : int = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
_snake_case : Any = lbp.local_binary_value(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
assert lbp_image.any()
| 260 |
def lowerCamelCase_ ( lowerCAmelCase: int )-> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def __init__( self : List[Any] ,*lowercase__ : Optional[Any] ,**lowercase__ : List[Any] ):
warnings.warn(
'''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use CLIPImageProcessor instead.''' ,lowercase__ ,)
super().__init__(*lowercase__ ,**lowercase__ )
| 104 | import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : torch.FloatTensor
__UpperCAmelCase : Optional[torch.FloatTensor] = None
def lowerCAmelCase_ ( __A, __A=0.999, __A="cosine", ) -> Tuple:
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(__A ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__A ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
UpperCAmelCase__ = []
for i in range(__A ):
UpperCAmelCase__ = i / num_diffusion_timesteps
UpperCAmelCase__ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__A ) / alpha_bar_fn(__A ), __A ) )
return torch.tensor(__A, dtype=torch.floataa )
class A ( UpperCAmelCase_ , UpperCAmelCase_ ):
@register_to_config
def __init__(self : List[str] , __UpperCAmelCase : int = 1_0_0_0 , __UpperCAmelCase : str = "fixed_small_log" , __UpperCAmelCase : bool = True , __UpperCAmelCase : Optional[float] = 1.0 , __UpperCAmelCase : str = "epsilon" , __UpperCAmelCase : str = "squaredcos_cap_v2" , ) -> Optional[int]:
"""simple docstring"""
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'" )
UpperCAmelCase__ = betas_for_alpha_bar(__UpperCAmelCase )
UpperCAmelCase__ = 1.0 - self.betas
UpperCAmelCase__ = torch.cumprod(self.alphas , dim=0 )
UpperCAmelCase__ = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
UpperCAmelCase__ = 1.0
# setable values
UpperCAmelCase__ = None
UpperCAmelCase__ = torch.from_numpy(np.arange(0 , __UpperCAmelCase )[::-1].copy() )
UpperCAmelCase__ = variance_type
def lowercase_ (self : List[str] , __UpperCAmelCase : torch.FloatTensor , __UpperCAmelCase : Optional[int] = None ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def lowercase_ (self : int , __UpperCAmelCase : int , __UpperCAmelCase : Union[str, torch.device] = None ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = num_inference_steps
UpperCAmelCase__ = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
UpperCAmelCase__ = (np.arange(0 , __UpperCAmelCase ) * step_ratio).round()[::-1].copy().astype(np.intaa )
UpperCAmelCase__ = torch.from_numpy(__UpperCAmelCase ).to(__UpperCAmelCase )
def lowercase_ (self : Any , __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[int]=None , __UpperCAmelCase : Tuple=None , __UpperCAmelCase : List[str]=None ) -> Tuple:
"""simple docstring"""
if prev_timestep is None:
UpperCAmelCase__ = t - 1
UpperCAmelCase__ = self.alphas_cumprod[t]
UpperCAmelCase__ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase__ = 1 - alpha_prod_t
UpperCAmelCase__ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase__ = self.betas[t]
else:
UpperCAmelCase__ = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase__ = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
UpperCAmelCase__ = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
UpperCAmelCase__ = torch.log(torch.clamp(__UpperCAmelCase , min=1E-20 ) )
UpperCAmelCase__ = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
UpperCAmelCase__ = variance.log()
UpperCAmelCase__ = beta.log()
UpperCAmelCase__ = (predicted_variance + 1) / 2
UpperCAmelCase__ = frac * max_log + (1 - frac) * min_log
return variance
def lowercase_ (self : Optional[int] , __UpperCAmelCase : torch.FloatTensor , __UpperCAmelCase : int , __UpperCAmelCase : torch.FloatTensor , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : List[str]=None , __UpperCAmelCase : bool = True , ) -> Union[UnCLIPSchedulerOutput, Tuple]:
"""simple docstring"""
UpperCAmelCase__ = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
UpperCAmelCase__ , UpperCAmelCase__ = torch.split(__UpperCAmelCase , sample.shape[1] , dim=1 )
else:
UpperCAmelCase__ = None
# 1. compute alphas, betas
if prev_timestep is None:
UpperCAmelCase__ = t - 1
UpperCAmelCase__ = self.alphas_cumprod[t]
UpperCAmelCase__ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase__ = 1 - alpha_prod_t
UpperCAmelCase__ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase__ = self.betas[t]
UpperCAmelCase__ = self.alphas[t]
else:
UpperCAmelCase__ = 1 - alpha_prod_t / alpha_prod_t_prev
UpperCAmelCase__ = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase__ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase__ = model_output
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
" for the UnCLIPScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase__ = torch.clamp(
__UpperCAmelCase , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
UpperCAmelCase__ = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase__ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
UpperCAmelCase__ = 0
if t > 0:
UpperCAmelCase__ = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=__UpperCAmelCase , device=model_output.device )
UpperCAmelCase__ = self._get_variance(
__UpperCAmelCase , predicted_variance=__UpperCAmelCase , prev_timestep=__UpperCAmelCase , )
if self.variance_type == "fixed_small_log":
UpperCAmelCase__ = variance
elif self.variance_type == "learned_range":
UpperCAmelCase__ = (0.5 * variance).exp()
else:
raise ValueError(
f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
" for the UnCLIPScheduler." )
UpperCAmelCase__ = variance * variance_noise
UpperCAmelCase__ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=__UpperCAmelCase , pred_original_sample=__UpperCAmelCase )
def lowercase_ (self : Union[str, Any] , __UpperCAmelCase : torch.FloatTensor , __UpperCAmelCase : torch.FloatTensor , __UpperCAmelCase : torch.IntTensor , ) -> torch.FloatTensor:
"""simple docstring"""
UpperCAmelCase__ = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
UpperCAmelCase__ = timesteps.to(original_samples.device )
UpperCAmelCase__ = alphas_cumprod[timesteps] ** 0.5
UpperCAmelCase__ = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase__ = sqrt_alpha_prod.unsqueeze(-1 )
UpperCAmelCase__ = (1 - alphas_cumprod[timesteps]) ** 0.5
UpperCAmelCase__ = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase__ = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
UpperCAmelCase__ = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 65 | 0 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase__ :
"""simple docstring"""
def __lowercase ( self : Any ,_a : List[Any] ):
'''simple docstring'''
raise NotImplementedError()
def __lowercase ( self : List[str] ):
'''simple docstring'''
raise NotImplementedError()
class UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
def __init__( self : Tuple ,_a : "AutoTokenizer" ,_a : bool = False ,**_a : Tuple ):
'''simple docstring'''
_a : Union[str, Any] = tokenizer
_a : Any = skip_prompt
_a : List[str] = decode_kwargs
# variables used in the streaming process
_a : Tuple = []
_a : int = 0
_a : List[Any] = True
def __lowercase ( self : List[str] ,_a : Tuple ):
'''simple docstring'''
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('TextStreamer only supports batch size 1' )
elif len(value.shape ) > 1:
_a : int = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
_a : Tuple = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
_a : Any = self.tokenizer.decode(self.token_cache ,**self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('\n' ):
_a : str = text[self.print_len :]
_a : Optional[Any] = []
_a : List[str] = 0
# If the last token is a CJK character, we print the characters.
elif len(_a ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
_a : List[str] = text[self.print_len :]
self.print_len += len(_a )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
_a : int = text[self.print_len : text.rfind(' ' ) + 1]
self.print_len += len(_a )
self.on_finalized_text(_a )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
if len(self.token_cache ) > 0:
_a : Dict = self.tokenizer.decode(self.token_cache ,**self.decode_kwargs )
_a : Tuple = text[self.print_len :]
_a : str = []
_a : str = 0
else:
_a : Tuple = ''
_a : str = True
self.on_finalized_text(_a ,stream_end=_a )
def __lowercase ( self : Dict ,_a : str ,_a : bool = False ):
'''simple docstring'''
print(_a ,flush=_a ,end='' if not stream_end else None )
def __lowercase ( self : List[str] ,_a : Optional[Any] ):
'''simple docstring'''
if (
(cp >= 0X4e00 and cp <= 0X9fff)
or (cp >= 0X3400 and cp <= 0X4dbf) #
or (cp >= 0X2_0000 and cp <= 0X2_a6df) #
or (cp >= 0X2_a700 and cp <= 0X2_b73f) #
or (cp >= 0X2_b740 and cp <= 0X2_b81f) #
or (cp >= 0X2_b820 and cp <= 0X2_ceaf) #
or (cp >= 0Xf900 and cp <= 0Xfaff)
or (cp >= 0X2_f800 and cp <= 0X2_fa1f) #
): #
return True
return False
class UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
def __init__( self : int ,_a : "AutoTokenizer" ,_a : bool = False ,_a : Optional[float] = None ,**_a : Union[str, Any] ):
'''simple docstring'''
super().__init__(_a ,_a ,**_a )
_a : List[str] = Queue()
_a : Union[str, Any] = None
_a : Optional[Any] = timeout
def __lowercase ( self : str ,_a : str ,_a : bool = False ):
'''simple docstring'''
self.text_queue.put(_a ,timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal ,timeout=self.timeout )
def __iter__( self : List[str] ):
'''simple docstring'''
return self
def __lowercase ( self : List[Any] ):
'''simple docstring'''
_a : Tuple = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 5 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def UpperCAmelCase_ (__a : str , __a : Dict=0.999 , __a : List[str]="cosine" , ):
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(__a : Union[str, Any] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__a : int ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
_a : Tuple = []
for i in range(__a ):
_a : Union[str, Any] = i / num_diffusion_timesteps
_a : Any = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__a ) / alpha_bar_fn(__a ) , __a ) )
return torch.tensor(__a , dtype=torch.floataa )
class UpperCAmelCase__ ( lowercase__ , lowercase__ ):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = [e.name for e in KarrasDiffusionSchedulers]
__UpperCAmelCase : Dict = 2
@register_to_config
def __init__( self : str ,_a : int = 1000 ,_a : float = 0.0_0085 ,_a : float = 0.012 ,_a : str = "linear" ,_a : Optional[Union[np.ndarray, List[float]]] = None ,_a : str = "epsilon" ,_a : Optional[bool] = False ,_a : Optional[bool] = False ,_a : float = 1.0 ,_a : str = "linspace" ,_a : int = 0 ,):
'''simple docstring'''
if trained_betas is not None:
_a : List[str] = torch.tensor(_a ,dtype=torch.floataa )
elif beta_schedule == "linear":
_a : Tuple = torch.linspace(_a ,_a ,_a ,dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_a : List[str] = (
torch.linspace(beta_start**0.5 ,beta_end**0.5 ,_a ,dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_a : Dict = betas_for_alpha_bar(_a ,alpha_transform_type='cosine' )
elif beta_schedule == "exp":
_a : Tuple = betas_for_alpha_bar(_a ,alpha_transform_type='exp' )
else:
raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""" )
_a : Optional[Any] = 1.0 - self.betas
_a : Optional[int] = torch.cumprod(self.alphas ,dim=0 )
# set all values
self.set_timesteps(_a ,_a ,_a )
_a : Optional[int] = use_karras_sigmas
def __lowercase ( self : Any ,_a : Union[str, Any] ,_a : Optional[Any]=None ):
'''simple docstring'''
if schedule_timesteps is None:
_a : List[Any] = self.timesteps
_a : Dict = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
_a : int = 1 if len(_a ) > 1 else 0
else:
_a : str = timestep.cpu().item() if torch.is_tensor(_a ) else timestep
_a : str = self._index_counter[timestep_int]
return indices[pos].item()
@property
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def __lowercase ( self : int ,_a : torch.FloatTensor ,_a : Union[float, torch.FloatTensor] ,):
'''simple docstring'''
_a : List[Any] = self.index_for_timestep(_a )
_a : Tuple = self.sigmas[step_index]
_a : Optional[Any] = sample / ((sigma**2 + 1) ** 0.5)
return sample
def __lowercase ( self : Any ,_a : int ,_a : Union[str, torch.device] = None ,_a : Optional[int] = None ,):
'''simple docstring'''
_a : Optional[Any] = num_inference_steps
_a : Dict = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
_a : Optional[Any] = np.linspace(0 ,num_train_timesteps - 1 ,_a ,dtype=_a )[::-1].copy()
elif self.config.timestep_spacing == "leading":
_a : str = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_a : int = (np.arange(0 ,_a ) * step_ratio).round()[::-1].copy().astype(_a )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
_a : Any = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_a : Union[str, Any] = (np.arange(_a ,0 ,-step_ratio )).round().copy().astype(_a )
timesteps -= 1
else:
raise ValueError(
F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
_a : Tuple = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
_a : Union[str, Any] = np.log(_a )
_a : str = np.interp(_a ,np.arange(0 ,len(_a ) ) ,_a )
if self.config.use_karras_sigmas:
_a : List[Any] = self._convert_to_karras(in_sigmas=_a ,num_inference_steps=self.num_inference_steps )
_a : Dict = np.array([self._sigma_to_t(_a ,_a ) for sigma in sigmas] )
_a : int = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
_a : Union[str, Any] = torch.from_numpy(_a ).to(device=_a )
_a : Any = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )
_a : List[Any] = torch.from_numpy(_a )
_a : List[str] = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )
if str(_a ).startswith('mps' ):
# mps does not support float64
_a : Tuple = timesteps.to(_a ,dtype=torch.floataa )
else:
_a : Dict = timesteps.to(device=_a )
# empty dt and derivative
_a : Tuple = None
_a : Optional[Any] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
_a : Union[str, Any] = defaultdict(_a )
def __lowercase ( self : str ,_a : Dict ,_a : Dict ):
'''simple docstring'''
_a : Optional[int] = np.log(_a )
# get distribution
_a : Union[str, Any] = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
_a : List[Any] = np.cumsum((dists >= 0) ,axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
_a : Tuple = low_idx + 1
_a : Union[str, Any] = log_sigmas[low_idx]
_a : Optional[Any] = log_sigmas[high_idx]
# interpolate sigmas
_a : Optional[Any] = (low - log_sigma) / (low - high)
_a : List[str] = np.clip(_a ,0 ,1 )
# transform interpolation to time range
_a : Union[str, Any] = (1 - w) * low_idx + w * high_idx
_a : List[str] = t.reshape(sigma.shape )
return t
def __lowercase ( self : int ,_a : torch.FloatTensor ,_a : Tuple ):
'''simple docstring'''
_a : float = in_sigmas[-1].item()
_a : float = in_sigmas[0].item()
_a : Tuple = 7.0 # 7.0 is the value used in the paper
_a : str = np.linspace(0 ,1 ,_a )
_a : Optional[Any] = sigma_min ** (1 / rho)
_a : Union[str, Any] = sigma_max ** (1 / rho)
_a : str = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
@property
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
return self.dt is None
def __lowercase ( self : int ,_a : Union[torch.FloatTensor, np.ndarray] ,_a : Union[float, torch.FloatTensor] ,_a : Union[torch.FloatTensor, np.ndarray] ,_a : bool = True ,):
'''simple docstring'''
_a : Union[str, Any] = self.index_for_timestep(_a )
# advance index counter by 1
_a : Any = timestep.cpu().item() if torch.is_tensor(_a ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
_a : Tuple = self.sigmas[step_index]
_a : int = self.sigmas[step_index + 1]
else:
# 2nd order / Heun's method
_a : List[str] = self.sigmas[step_index - 1]
_a : List[Any] = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
_a : Optional[int] = 0
_a : Tuple = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
_a : Dict = sigma_hat if self.state_in_first_order else sigma_next
_a : Optional[int] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
_a : List[Any] = sigma_hat if self.state_in_first_order else sigma_next
_a : List[Any] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
_a : Union[str, Any] = model_output
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.config.clip_sample:
_a : Optional[int] = pred_original_sample.clamp(
-self.config.clip_sample_range ,self.config.clip_sample_range )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
_a : Optional[Any] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
_a : Any = sigma_next - sigma_hat
# store for 2nd order step
_a : int = derivative
_a : List[str] = dt
_a : Union[str, Any] = sample
else:
# 2. 2nd order / Heun's method
_a : Dict = (sample - pred_original_sample) / sigma_next
_a : Tuple = (self.prev_derivative + derivative) / 2
# 3. take prev timestep & sample
_a : Optional[Any] = self.dt
_a : Union[str, Any] = self.sample
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
_a : List[Any] = None
_a : Union[str, Any] = None
_a : Dict = None
_a : str = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=_a )
def __lowercase ( self : Optional[int] ,_a : torch.FloatTensor ,_a : torch.FloatTensor ,_a : torch.FloatTensor ,):
'''simple docstring'''
_a : str = self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(_a ):
# mps does not support float64
_a : Dict = self.timesteps.to(original_samples.device ,dtype=torch.floataa )
_a : Optional[Any] = timesteps.to(original_samples.device ,dtype=torch.floataa )
else:
_a : int = self.timesteps.to(original_samples.device )
_a : Optional[Any] = timesteps.to(original_samples.device )
_a : Any = [self.index_for_timestep(_a ,_a ) for t in timesteps]
_a : Optional[int] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
_a : Optional[Any] = sigma.unsqueeze(-1 )
_a : Any = original_samples + noise * sigma
return noisy_samples
def __len__( self : Optional[int] ):
'''simple docstring'''
return self.config.num_train_timesteps
| 5 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
_UpperCAmelCase = 4
_UpperCAmelCase = (1 << p) - 1
for _ in range(p - 2 ):
_UpperCAmelCase = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(1_1))
| 289 | """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 MobileViTImageProcessor
class a ( unittest.TestCase ):
def __init__( self : Dict , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any]=7 , __lowerCAmelCase : Optional[Any]=3 , __lowerCAmelCase : Optional[Any]=18 , __lowerCAmelCase : str=30 , __lowerCAmelCase : List[str]=400 , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : str=None , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : int=None , __lowerCAmelCase : List[str]=True , ):
_UpperCAmelCase = size if size is not None else {"""shortest_edge""": 20}
_UpperCAmelCase = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = image_size
_UpperCAmelCase = min_resolution
_UpperCAmelCase = max_resolution
_UpperCAmelCase = do_resize
_UpperCAmelCase = size
_UpperCAmelCase = do_center_crop
_UpperCAmelCase = crop_size
_UpperCAmelCase = do_flip_channel_order
def lowerCAmelCase_ ( self : List[str] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class a ( lowerCAmelCase_ , unittest.TestCase ):
_snake_case : Optional[int] = MobileViTImageProcessor if is_vision_available() else None
def lowerCAmelCase_ ( self : Optional[Any] ):
_UpperCAmelCase = MobileViTImageProcessingTester(self )
@property
def lowerCAmelCase_ ( self : Tuple ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase_ ( self : Dict ):
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowerCAmelCase , """do_resize""" ) )
self.assertTrue(hasattr(__lowerCAmelCase , """size""" ) )
self.assertTrue(hasattr(__lowerCAmelCase , """do_center_crop""" ) )
self.assertTrue(hasattr(__lowerCAmelCase , """center_crop""" ) )
self.assertTrue(hasattr(__lowerCAmelCase , """do_flip_channel_order""" ) )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 20} )
self.assertEqual(image_processor.crop_size , {"""height""": 18, """width""": 18} )
_UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size , {"""height""": 84, """width""": 84} )
def lowerCAmelCase_ ( self : List[str] ):
pass
def lowerCAmelCase_ ( self : Dict ):
# Initialize image_processing
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , Image.Image )
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_UpperCAmelCase = image_processing(__lowerCAmelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def lowerCAmelCase_ ( self : str ):
# Initialize image_processing
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , numpify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , np.ndarray )
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_UpperCAmelCase = image_processing(__lowerCAmelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def lowerCAmelCase_ ( self : Optional[int] ):
# Initialize image_processing
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , torchify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , torch.Tensor )
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_UpperCAmelCase = image_processing(__lowerCAmelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
| 289 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_SCREAMING_SNAKE_CASE = (720, 1280) # Height, Width
_SCREAMING_SNAKE_CASE = (0.4, 0.6) # if height or width lower than this scale, drop it.
_SCREAMING_SNAKE_CASE = 1 / 100
_SCREAMING_SNAKE_CASE = ""
_SCREAMING_SNAKE_CASE = ""
_SCREAMING_SNAKE_CASE = ""
_SCREAMING_SNAKE_CASE = 250
def __lowerCamelCase ( ) -> None:
snake_case , snake_case = get_dataset(__lowerCAmelCase , __lowerCAmelCase )
for index in range(__lowerCAmelCase ):
snake_case = random.sample(range(len(__lowerCAmelCase ) ) , 4 )
snake_case , snake_case , snake_case = update_image_and_anno(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , filter_scale=__lowerCAmelCase , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
snake_case = random_chars(32 )
snake_case = path.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
snake_case = F'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
cva.imwrite(F'''{file_root}.jpg''' , __lowerCAmelCase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}''' )
snake_case = []
for anno in new_annos:
snake_case = anno[3] - anno[1]
snake_case = anno[4] - anno[2]
snake_case = anno[1] + width / 2
snake_case = anno[2] + height / 2
snake_case = F'''{anno[0]} {x_center} {y_center} {width} {height}'''
annos_list.append(__lowerCAmelCase )
with open(F'''{file_root}.txt''' , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def __lowerCamelCase ( __lowerCAmelCase : str , __lowerCAmelCase : str ) -> tuple[list, list]:
snake_case = []
snake_case = []
for label_file in glob.glob(os.path.join(__lowerCAmelCase , """*.txt""" ) ):
snake_case = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(__lowerCAmelCase ) as in_file:
snake_case = in_file.readlines()
snake_case = os.path.join(__lowerCAmelCase , F'''{label_name}.jpg''' )
snake_case = []
for obj_list in obj_lists:
snake_case = obj_list.rstrip("""\n""" ).split(""" """ )
snake_case = float(obj[1] ) - float(obj[3] ) / 2
snake_case = float(obj[2] ) - float(obj[4] ) / 2
snake_case = float(obj[1] ) + float(obj[3] ) / 2
snake_case = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(__lowerCAmelCase )
labels.append(__lowerCAmelCase )
return img_paths, labels
def __lowerCamelCase ( __lowerCAmelCase : list , __lowerCAmelCase : list , __lowerCAmelCase : list[int] , __lowerCAmelCase : tuple[int, int] , __lowerCAmelCase : tuple[float, float] , __lowerCAmelCase : float = 0.0 , ) -> tuple[list, list, str]:
snake_case = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
snake_case = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case = int(scale_x * output_size[1] )
snake_case = int(scale_y * output_size[0] )
snake_case = []
snake_case = []
for i, index in enumerate(__lowerCAmelCase ):
snake_case = all_img_list[index]
path_list.append(__lowerCAmelCase )
snake_case = all_annos[index]
snake_case = cva.imread(__lowerCAmelCase )
if i == 0: # top-left
snake_case = cva.resize(__lowerCAmelCase , (divid_point_x, divid_point_y) )
snake_case = img
for bbox in img_annos:
snake_case = bbox[1] * scale_x
snake_case = bbox[2] * scale_y
snake_case = bbox[3] * scale_x
snake_case = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
snake_case = cva.resize(__lowerCAmelCase , (output_size[1] - divid_point_x, divid_point_y) )
snake_case = img
for bbox in img_annos:
snake_case = scale_x + bbox[1] * (1 - scale_x)
snake_case = bbox[2] * scale_y
snake_case = scale_x + bbox[3] * (1 - scale_x)
snake_case = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
snake_case = cva.resize(__lowerCAmelCase , (divid_point_x, output_size[0] - divid_point_y) )
snake_case = img
for bbox in img_annos:
snake_case = bbox[1] * scale_x
snake_case = scale_y + bbox[2] * (1 - scale_y)
snake_case = bbox[3] * scale_x
snake_case = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
snake_case = cva.resize(
__lowerCAmelCase , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
snake_case = img
for bbox in img_annos:
snake_case = scale_x + bbox[1] * (1 - scale_x)
snake_case = scale_y + bbox[2] * (1 - scale_y)
snake_case = scale_x + bbox[3] * (1 - scale_x)
snake_case = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
snake_case = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def __lowerCamelCase ( __lowerCAmelCase : int ) -> str:
assert number_char > 1, "The number of character should greater than 1"
snake_case = ascii_lowercase + digits
return "".join(random.choice(__lowerCAmelCase ) for _ in range(__lowerCAmelCase ) )
if __name__ == "__main__":
main()
print("DONE ✅")
| 3 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __lowerCamelCase ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus" ) -> dict:
snake_case = BeautifulSoup(requests.get(__lowerCAmelCase ).text , """html.parser""" )
snake_case = soup.findAll("""h1""" )
snake_case = soup.findAll("""div""" , {"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" , {"""class""": """panel-title"""} )
values += soup.findAll("""div""" , {"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(__lowerCAmelCase , __lowerCAmelCase )}
if __name__ == "__main__":
print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
for key, value in world_covidaa_stats().items():
print(F"""{key}\n{value}\n""")
| 3 | 1 |
def a__ ( A_ ):
'''simple docstring'''
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
__magic_name__ = sorted(string.lower() )
return len(A_ ) == len(set(A_ ) )
if __name__ == "__main__":
__lowerCAmelCase : Dict = input('Enter a string ').strip()
__lowerCAmelCase : Union[str, Any] = is_isogram(input_str)
print(F'''{input_str} is {"an" if isogram else "not an"} isogram.''')
| 88 |
import re
import string
import numpy as np
import datasets
__lowerCAmelCase : Optional[int] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
__lowerCAmelCase : Optional[int] = '\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results["exact_match"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 33.3\n\n'
__lowerCAmelCase : Optional[int] = '\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _lowercase ( self : str ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , reference_urls=[] , )
def _lowercase ( self : Optional[int] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[Any]=None , UpperCamelCase__ : Optional[Any]=False , UpperCamelCase__ : int=False , UpperCamelCase__ : Tuple=False , ) -> Dict:
"""simple docstring"""
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
__magic_name__ = np.array([re.sub(UpperCamelCase__ , """""" , UpperCamelCase__ ) for x in predictions] )
__magic_name__ = np.array([re.sub(UpperCamelCase__ , """""" , UpperCamelCase__ ) for x in references] )
else:
__magic_name__ = np.asarray(UpperCamelCase__ )
__magic_name__ = np.asarray(UpperCamelCase__ )
if ignore_case:
__magic_name__ = np.char.lower(UpperCamelCase__ )
__magic_name__ = np.char.lower(UpperCamelCase__ )
if ignore_punctuation:
__magic_name__ = string.punctuation.maketrans("""""" , """""" , string.punctuation )
__magic_name__ = np.char.translate(UpperCamelCase__ , table=UpperCamelCase__ )
__magic_name__ = np.char.translate(UpperCamelCase__ , table=UpperCamelCase__ )
if ignore_numbers:
__magic_name__ = string.digits.maketrans("""""" , """""" , string.digits )
__magic_name__ = np.char.translate(UpperCamelCase__ , table=UpperCamelCase__ )
__magic_name__ = np.char.translate(UpperCamelCase__ , table=UpperCamelCase__ )
__magic_name__ = predictions == references
return {"exact_match": np.mean(UpperCamelCase__ ) * 100}
| 88 | 1 |
def UpperCamelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : list[list[int]] ):
def update_area_of_max_square(__lowerCamelCase : int , __lowerCamelCase : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
snake_case : List[Any] = update_area_of_max_square(__lowerCamelCase , col + 1 )
snake_case : int = update_area_of_max_square(row + 1 , col + 1 )
snake_case : Union[str, Any] = update_area_of_max_square(row + 1 , __lowerCamelCase )
if mat[row][col]:
snake_case : Optional[Any] = 1 + min([right, diagonal, down] )
snake_case : List[Any] = max(largest_square_area[0] , __lowerCamelCase )
return sub_problem_sol
else:
return 0
snake_case : Union[str, Any] = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def UpperCamelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : list[list[int]] ):
def update_area_of_max_square_using_dp_array(
__lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
snake_case : Optional[Any] = update_area_of_max_square_using_dp_array(__lowerCamelCase , col + 1 , __lowerCamelCase )
snake_case : str = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , __lowerCamelCase )
snake_case : Optional[int] = update_area_of_max_square_using_dp_array(row + 1 , __lowerCamelCase , __lowerCamelCase )
if mat[row][col]:
snake_case : Union[str, Any] = 1 + min([right, diagonal, down] )
snake_case : Dict = max(largest_square_area[0] , __lowerCamelCase )
snake_case : Optional[int] = sub_problem_sol
return sub_problem_sol
else:
return 0
snake_case : Any = [0]
snake_case : Optional[Any] = [[-1] * cols for _ in range(__lowerCamelCase )]
update_area_of_max_square_using_dp_array(0 , 0 , __lowerCamelCase )
return largest_square_area[0]
def UpperCamelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : list[list[int]] ):
snake_case : Optional[Any] = [[0] * (cols + 1) for _ in range(rows + 1 )]
snake_case : Optional[int] = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
snake_case : Optional[int] = dp_array[row][col + 1]
snake_case : Any = dp_array[row + 1][col + 1]
snake_case : Optional[int] = dp_array[row + 1][col]
if mat[row][col] == 1:
snake_case : Tuple = 1 + min(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
snake_case : Tuple = max(dp_array[row][col] , __lowerCamelCase )
else:
snake_case : int = 0
return largest_square_area
def UpperCamelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : list[list[int]] ):
snake_case : Optional[Any] = [0] * (cols + 1)
snake_case : Any = [0] * (cols + 1)
snake_case : int = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
snake_case : Dict = current_row[col + 1]
snake_case : List[str] = next_row[col + 1]
snake_case : Optional[int] = next_row[col]
if mat[row][col] == 1:
snake_case : Optional[Any] = 1 + min(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
snake_case : List[str] = max(current_row[col] , __lowerCamelCase )
else:
snake_case : str = 0
snake_case : List[str] = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 354 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
__lowerCamelCase = ["""text""", """image""", """audio"""]
def UpperCamelCase ( __lowerCamelCase : List[str] ):
snake_case : str = []
for input_type in input_types:
if input_type == "text":
inputs.append("Text input" )
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png" ).resize((512, 512) ) )
elif input_type == "audio":
inputs.append(torch.ones(3000 ) )
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
inputs.append(create_inputs(__lowerCamelCase ) )
else:
raise ValueError(f"""Invalid type requested: {input_type}""" )
return inputs
def UpperCamelCase ( __lowerCamelCase : List ):
snake_case : List[str] = []
for output in outputs:
if isinstance(__lowerCamelCase , (str, AgentText) ):
output_types.append("text" )
elif isinstance(__lowerCamelCase , (Image.Image, AgentImage) ):
output_types.append("image" )
elif isinstance(__lowerCamelCase , (torch.Tensor, AgentAudio) ):
output_types.append("audio" )
else:
raise ValueError(f"""Invalid output: {output}""" )
return output_types
@is_tool_test
class UpperCAmelCase :
def _SCREAMING_SNAKE_CASE (self : List[str] ) -> List[str]:
'''simple docstring'''
self.assertTrue(hasattr(self.tool , "inputs" ) )
self.assertTrue(hasattr(self.tool , "outputs" ) )
snake_case : List[Any] = self.tool.inputs
for _input in inputs:
if isinstance(_input , snake_case__ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
snake_case : str = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def _SCREAMING_SNAKE_CASE (self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case : List[str] = create_inputs(self.tool.inputs )
snake_case : Dict = self.tool(*snake_case__ )
# There is a single output
if len(self.tool.outputs ) == 1:
snake_case : List[Any] = [outputs]
self.assertListEqual(output_types(snake_case__ ) , self.tool.outputs )
def _SCREAMING_SNAKE_CASE (self : List[str] ) -> List[Any]:
'''simple docstring'''
self.assertTrue(hasattr(self.tool , "description" ) )
self.assertTrue(hasattr(self.tool , "default_checkpoint" ) )
self.assertTrue(self.tool.description.startswith("This is a tool that" ) )
def _SCREAMING_SNAKE_CASE (self : int ) -> Union[str, Any]:
'''simple docstring'''
snake_case : str = create_inputs(self.tool.inputs )
snake_case : int = self.tool(*snake_case__ )
if not isinstance(snake_case__ , snake_case__ ):
snake_case : Optional[Any] = [outputs]
self.assertEqual(len(snake_case__ ) , len(self.tool.outputs ) )
for output, output_type in zip(snake_case__ , self.tool.outputs ):
snake_case : Any = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(snake_case__ , snake_case__ ) )
def _SCREAMING_SNAKE_CASE (self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
snake_case : List[Any] = create_inputs(self.tool.inputs )
snake_case : str = []
for _input, input_type in zip(snake_case__ , self.tool.inputs ):
if isinstance(snake_case__ , snake_case__ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
snake_case : Optional[int] = self.tool(*snake_case__ )
if not isinstance(snake_case__ , snake_case__ ):
snake_case : List[str] = [outputs]
self.assertEqual(len(snake_case__ ) , len(self.tool.outputs ) )
| 10 | 0 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class a__ ( UpperCamelCase__ ):
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
return 0.0
def _a ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : int ):
__lowerCAmelCase = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
__lowerCAmelCase = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def _a ( SCREAMING_SNAKE_CASE_ : FilterType , SCREAMING_SNAKE_CASE_ : int ):
__lowerCAmelCase = 5_12
__lowerCAmelCase = [1] + [0] * (size - 1)
__lowerCAmelCase = [filter_type.process(SCREAMING_SNAKE_CASE_ ) for item in inputs]
__lowerCAmelCase = [0] * (samplerate - size) # zero-padding
outputs += filler
__lowerCAmelCase = np.abs(np.fft.fft(SCREAMING_SNAKE_CASE_ ) )
__lowerCAmelCase = 20 * np.logaa(SCREAMING_SNAKE_CASE_ )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
# Display within reasonable bounds
__lowerCAmelCase = get_bounds(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel("Gain (dB)" )
plt.plot(SCREAMING_SNAKE_CASE_ )
plt.show()
def _a ( SCREAMING_SNAKE_CASE_ : FilterType , SCREAMING_SNAKE_CASE_ : int ):
__lowerCAmelCase = 5_12
__lowerCAmelCase = [1] + [0] * (size - 1)
__lowerCAmelCase = [filter_type.process(SCREAMING_SNAKE_CASE_ ) for item in inputs]
__lowerCAmelCase = [0] * (samplerate - size) # zero-padding
outputs += filler
__lowerCAmelCase = np.angle(np.fft.fft(SCREAMING_SNAKE_CASE_ ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel("Phase shift (Radians)" )
plt.plot(np.unwrap(SCREAMING_SNAKE_CASE_ , -2 * pi ) )
plt.show()
| 92 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Dict = logging.get_logger(__name__)
UpperCAmelCase : Tuple = {
"""caidas/swin2sr-classicalsr-x2-64""": (
"""https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
),
}
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : Any = """swin2sr"""
_lowercase : Tuple = {
"""hidden_size""": """embed_dim""",
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self , lowerCAmelCase__=6_4 , lowerCAmelCase__=1 , lowerCAmelCase__=3 , lowerCAmelCase__=1_8_0 , lowerCAmelCase__=[6, 6, 6, 6, 6, 6] , lowerCAmelCase__=[6, 6, 6, 6, 6, 6] , lowerCAmelCase__=8 , lowerCAmelCase__=2.0 , lowerCAmelCase__=True , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.1 , lowerCAmelCase__="gelu" , lowerCAmelCase__=False , lowerCAmelCase__=0.02 , lowerCAmelCase__=1E-5 , lowerCAmelCase__=2 , lowerCAmelCase__=1.0 , lowerCAmelCase__="1conv" , lowerCAmelCase__="pixelshuffle" , **lowerCAmelCase__ , ) -> int:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
a__ : Optional[Any] =image_size
a__ : Dict =patch_size
a__ : Tuple =num_channels
a__ : Union[str, Any] =embed_dim
a__ : Optional[Any] =depths
a__ : List[str] =len(lowerCAmelCase__ )
a__ : Any =num_heads
a__ : Any =window_size
a__ : str =mlp_ratio
a__ : List[str] =qkv_bias
a__ : Dict =hidden_dropout_prob
a__ : List[str] =attention_probs_dropout_prob
a__ : Dict =drop_path_rate
a__ : Optional[Any] =hidden_act
a__ : Union[str, Any] =use_absolute_embeddings
a__ : Optional[Any] =layer_norm_eps
a__ : List[Any] =initializer_range
a__ : int =upscale
a__ : Optional[int] =img_range
a__ : Any =resi_connection
a__ : Optional[Any] =upsampler
| 95 | 0 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : List[str]=None , lowercase_ : Tuple=None):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[Any] = start
SCREAMING_SNAKE_CASE_ : int = end
SCREAMING_SNAKE_CASE_ : Any = val
SCREAMING_SNAKE_CASE_ : List[Any] = (start + end) // 2
SCREAMING_SNAKE_CASE_ : Dict = left
SCREAMING_SNAKE_CASE_ : List[str] = right
def __repr__( self : Union[str, Any]):
'''simple docstring'''
return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : List[str] , lowercase_ : Sequence , lowercase_ : int):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : str = collection
SCREAMING_SNAKE_CASE_ : Any = function
if self.collection:
SCREAMING_SNAKE_CASE_ : List[Any] = self._build_tree(0 , len(lowercase_) - 1)
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowercase_ : List[str] , lowercase_ : Optional[int]):
'''simple docstring'''
self._update_tree(self.root , lowercase_ , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowercase_ : Any , lowercase_ : List[Any]):
'''simple docstring'''
return self._query_range(self.root , lowercase_ , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : int , lowercase_ : Any , lowercase_ : Any):
'''simple docstring'''
if start == end:
return SegmentTreeNode(lowercase_ , lowercase_ , self.collection[start])
SCREAMING_SNAKE_CASE_ : Optional[int] = (start + end) // 2
SCREAMING_SNAKE_CASE_ : Tuple = self._build_tree(lowercase_ , lowercase_)
SCREAMING_SNAKE_CASE_ : str = self._build_tree(mid + 1 , lowercase_)
return SegmentTreeNode(lowercase_ , lowercase_ , self.fn(left.val , right.val) , lowercase_ , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Any , lowercase_ : Optional[Any]):
'''simple docstring'''
if node.start == i and node.end == i:
SCREAMING_SNAKE_CASE_ : Dict = val
return
if i <= node.mid:
self._update_tree(node.left , lowercase_ , lowercase_)
else:
self._update_tree(node.right , lowercase_ , lowercase_)
SCREAMING_SNAKE_CASE_ : Optional[int] = self.fn(node.left.val , node.right.val)
def _SCREAMING_SNAKE_CASE ( self : Dict , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : Optional[Any]):
'''simple docstring'''
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left , lowercase_ , lowercase_)
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left , lowercase_ , node.mid) , self._query_range(node.right , node.mid + 1 , lowercase_) , )
else:
# range in right child tree
return self._query_range(node.right , lowercase_ , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Tuple):
'''simple docstring'''
if self.root is not None:
SCREAMING_SNAKE_CASE_ : Dict = Queue()
queue.put(self.root)
while not queue.empty():
SCREAMING_SNAKE_CASE_ : Tuple = queue.get()
yield node
if node.left is not None:
queue.put(node.left)
if node.right is not None:
queue.put(node.right)
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print("""*""" * 50)
UpperCAmelCase_ : Any = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 318 |
"""simple docstring"""
from __future__ import annotations
import queue
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Tuple , lowercase_ : Optional[int]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[int] = data
SCREAMING_SNAKE_CASE_ : Tuple = None
SCREAMING_SNAKE_CASE_ : Dict = None
def _A () -> TreeNode:
"""simple docstring"""
print('''\n********Press N to stop entering at any point of time********\n''' )
SCREAMING_SNAKE_CASE_ : List[Any] = input('''Enter the value of the root node: ''' ).strip().lower()
SCREAMING_SNAKE_CASE_ : queue.Queue = queue.Queue()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = TreeNode(int(__a ) )
q.put(__a )
while not q.empty():
SCREAMING_SNAKE_CASE_ : Optional[int] = q.get()
SCREAMING_SNAKE_CASE_ : List[str] = f'Enter the left node of {node_found.data}: '
SCREAMING_SNAKE_CASE_ : Optional[int] = input(__a ).strip().lower() or '''n'''
if check == "n":
return tree_node
SCREAMING_SNAKE_CASE_ : List[str] = TreeNode(int(__a ) )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = left_node
q.put(__a )
SCREAMING_SNAKE_CASE_ : str = f'Enter the right node of {node_found.data}: '
SCREAMING_SNAKE_CASE_ : str = input(__a ).strip().lower() or '''n'''
if check == "n":
return tree_node
SCREAMING_SNAKE_CASE_ : Any = TreeNode(int(__a ) )
SCREAMING_SNAKE_CASE_ : int = right_node
q.put(__a )
raise
def _A (__a ) -> None:
"""simple docstring"""
if not isinstance(__a , __a ) or not node:
return
print(node.data , end=''',''' )
pre_order(node.left )
pre_order(node.right )
def _A (__a ) -> None:
"""simple docstring"""
if not isinstance(__a , __a ) or not node:
return
in_order(node.left )
print(node.data , end=''',''' )
in_order(node.right )
def _A (__a ) -> None:
"""simple docstring"""
if not isinstance(__a , __a ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=''',''' )
def _A (__a ) -> None:
"""simple docstring"""
if not isinstance(__a , __a ) or not node:
return
SCREAMING_SNAKE_CASE_ : queue.Queue = queue.Queue()
q.put(__a )
while not q.empty():
SCREAMING_SNAKE_CASE_ : Tuple = q.get()
print(node_dequeued.data , end=''',''' )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def _A (__a ) -> None:
"""simple docstring"""
if not isinstance(__a , __a ) or not node:
return
SCREAMING_SNAKE_CASE_ : queue.Queue = queue.Queue()
q.put(__a )
while not q.empty():
SCREAMING_SNAKE_CASE_ : str = []
while not q.empty():
SCREAMING_SNAKE_CASE_ : List[str] = q.get()
print(node_dequeued.data , end=''',''' )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(__a )
def _A (__a ) -> None:
"""simple docstring"""
if not isinstance(__a , __a ) or not node:
return
SCREAMING_SNAKE_CASE_ : list[TreeNode] = []
SCREAMING_SNAKE_CASE_ : Union[str, Any] = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=''',''' )
stack.append(__a )
SCREAMING_SNAKE_CASE_ : Optional[Any] = n.left
# end of while means current node doesn't have left child
SCREAMING_SNAKE_CASE_ : Tuple = stack.pop()
# start to traverse its right child
SCREAMING_SNAKE_CASE_ : str = n.right
def _A (__a ) -> None:
"""simple docstring"""
if not isinstance(__a , __a ) or not node:
return
SCREAMING_SNAKE_CASE_ : list[TreeNode] = []
SCREAMING_SNAKE_CASE_ : Any = node
while n or stack:
while n:
stack.append(__a )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = n.left
SCREAMING_SNAKE_CASE_ : Any = stack.pop()
print(n.data , end=''',''' )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = n.right
def _A (__a ) -> None:
"""simple docstring"""
if not isinstance(__a , __a ) or not node:
return
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = [], []
SCREAMING_SNAKE_CASE_ : List[Any] = node
stacka.append(__a )
while stacka: # to find the reversed order of post order, store it in stack2
SCREAMING_SNAKE_CASE_ : List[str] = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(__a )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=''',''' )
def _A (__a = "" , __a=50 , __a="*" ) -> str:
"""simple docstring"""
if not s:
return "\n" + width * char
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict = divmod(width - len(__a ) - 2 , 2 )
return f'{left * char} {s} {(left + extra) * char}'
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt("""Binary Tree Traversals"""))
UpperCAmelCase_ : TreeNode = build_tree()
print(prompt("""Pre Order Traversal"""))
pre_order(node)
print(prompt() + """\n""")
print(prompt("""In Order Traversal"""))
in_order(node)
print(prompt() + """\n""")
print(prompt("""Post Order Traversal"""))
post_order(node)
print(prompt() + """\n""")
print(prompt("""Level Order Traversal"""))
level_order(node)
print(prompt() + """\n""")
print(prompt("""Actual Level Order Traversal"""))
level_order_actual(node)
print("""*""" * 50 + """\n""")
print(prompt("""Pre Order Traversal - Iteration Version"""))
pre_order_iter(node)
print(prompt() + """\n""")
print(prompt("""In Order Traversal - Iteration Version"""))
in_order_iter(node)
print(prompt() + """\n""")
print(prompt("""Post Order Traversal - Iteration Version"""))
post_order_iter(node)
print(prompt())
| 318 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class A_ :
"""simple docstring"""
def __init__( self :str , lowercase_ :Tuple , lowercase_ :Tuple=14 , lowercase_ :str=7 , lowercase_ :int=True , lowercase_ :Optional[Any]=True , lowercase_ :Union[str, Any]=False , lowercase_ :str=True , lowercase_ :int=99 , lowercase_ :List[Any]=32 , lowercase_ :Any=4 , lowercase_ :int=4 , lowercase_ :Any=4 , lowercase_ :Optional[Any]=37 , lowercase_ :Dict="gelu" , lowercase_ :Optional[Any]=0.1 , lowercase_ :Tuple=0.1 , lowercase_ :int=5_12 , lowercase_ :Optional[Any]=0.02 , ) -> Optional[int]:
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = seq_length
UpperCAmelCase = is_training
UpperCAmelCase = use_input_mask
UpperCAmelCase = use_token_type_ids
UpperCAmelCase = use_labels
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = rotary_dim
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = initializer_range
UpperCAmelCase = None
UpperCAmelCase = vocab_size - 1
UpperCAmelCase = vocab_size - 1
UpperCAmelCase = vocab_size - 1
def UpperCAmelCase__ ( self :Optional[int] ) -> str:
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase = None
if self.use_input_mask:
UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase = GPTJConfig(
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 , use_cache=a_ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def UpperCAmelCase__ ( self :Optional[Any] ) -> List[str]:
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def UpperCAmelCase__ ( self :Dict , lowercase_ :Any , lowercase_ :Optional[Any] , lowercase_ :Optional[Any] , lowercase_ :Optional[Any] ) -> Dict:
UpperCAmelCase = 20
UpperCAmelCase = model_class_name(a_ )
UpperCAmelCase = model.init_cache(input_ids.shape[0] , a_ )
UpperCAmelCase = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='i4' )
UpperCAmelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
UpperCAmelCase = model(
input_ids[:, :-1] , attention_mask=a_ , past_key_values=a_ , position_ids=a_ , )
UpperCAmelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
UpperCAmelCase = model(
input_ids[:, -1:] , attention_mask=a_ , past_key_values=outputs_cache.past_key_values , position_ids=a_ , )
UpperCAmelCase = model(a_ )
UpperCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f"""Max diff is {diff}""" )
def UpperCAmelCase__ ( self :str , lowercase_ :Any , lowercase_ :Optional[Any] , lowercase_ :Optional[Any] , lowercase_ :Union[str, Any] ) -> Tuple:
UpperCAmelCase = 20
UpperCAmelCase = model_class_name(a_ )
UpperCAmelCase = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
UpperCAmelCase = model.init_cache(input_ids.shape[0] , a_ )
UpperCAmelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
UpperCAmelCase = model(
input_ids[:, :-1] , attention_mask=a_ , past_key_values=a_ , position_ids=a_ , )
UpperCAmelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
UpperCAmelCase = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=a_ , position_ids=a_ , )
UpperCAmelCase = model(a_ , attention_mask=a_ )
UpperCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f"""Max diff is {diff}""" )
@require_flax
class A_ ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
__UpperCamelCase = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def UpperCAmelCase__ ( self :int ) -> str:
UpperCAmelCase = FlaxGPTJModelTester(self )
def UpperCAmelCase__ ( self :Union[str, Any] ) -> str:
for model_class_name in self.all_model_classes:
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(a_ , a_ , a_ , a_ )
def UpperCAmelCase__ ( self :Tuple ) -> Optional[Any]:
for model_class_name in self.all_model_classes:
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
a_ , a_ , a_ , a_ )
@tooslow
def UpperCAmelCase__ ( self :Optional[int] ) -> Tuple:
UpperCAmelCase = GPTaTokenizer.from_pretrained('gpt2' , pad_token='<|endoftext|>' , padding_side='left' )
UpperCAmelCase = tokenizer(['Hello this is a long string', 'Hey'] , return_tensors='np' , padding=a_ , truncation=a_ )
UpperCAmelCase = FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' )
UpperCAmelCase = False
UpperCAmelCase = model.config.eos_token_id
UpperCAmelCase = jax.jit(model.generate )
UpperCAmelCase = jit_generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , pad_token_id=tokenizer.pad_token_id ).sequences
UpperCAmelCase = tokenizer.batch_decode(a_ , skip_special_tokens=a_ )
UpperCAmelCase = [
'''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
'''Hey, I\'m a little late to the party. I\'m going to''',
]
self.assertListEqual(a_ , a_ )
@is_pt_flax_cross_test
def UpperCAmelCase__ ( self :Any ) -> Optional[Any]:
UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
UpperCAmelCase = self._prepare_for_class(a_ , a_ )
UpperCAmelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
UpperCAmelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase = getattr(a_ , a_ )
UpperCAmelCase = pt_inputs['''input_ids'''].shape
UpperCAmelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(a_ ):
UpperCAmelCase = 0
UpperCAmelCase = 1
UpperCAmelCase = 0
UpperCAmelCase = 1
UpperCAmelCase = pt_model_class(a_ ).eval()
UpperCAmelCase = model_class(a_ , dtype=jnp.floataa )
UpperCAmelCase = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , a_ )
UpperCAmelCase = fx_state
with torch.no_grad():
UpperCAmelCase = pt_model(**a_ ).to_tuple()
UpperCAmelCase = fx_model(**a_ ).to_tuple()
self.assertEqual(len(a_ ) , len(a_ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(a_ , a_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(a_ )
UpperCAmelCase = model_class.from_pretrained(a_ , from_pt=a_ )
UpperCAmelCase = fx_model_loaded(**a_ ).to_tuple()
self.assertEqual(
len(a_ ) , len(a_ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output_loaded, pt_output in zip(a_ , a_ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@is_pt_flax_cross_test
def UpperCAmelCase__ ( self :Any ) -> int:
UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
UpperCAmelCase = self._prepare_for_class(a_ , a_ )
UpperCAmelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
UpperCAmelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase = getattr(a_ , a_ )
UpperCAmelCase = pt_model_class(a_ ).eval()
UpperCAmelCase = model_class(a_ , dtype=jnp.floataa )
UpperCAmelCase = load_flax_weights_in_pytorch_model(a_ , fx_model.params )
UpperCAmelCase = pt_inputs['''input_ids'''].shape
UpperCAmelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(a_ ):
UpperCAmelCase = 0
UpperCAmelCase = 1
UpperCAmelCase = 0
UpperCAmelCase = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
UpperCAmelCase = pt_model(**a_ ).to_tuple()
UpperCAmelCase = fx_model(**a_ ).to_tuple()
self.assertEqual(len(a_ ) , len(a_ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(a_ , a_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(a_ )
UpperCAmelCase = pt_model_class.from_pretrained(a_ , from_flax=a_ )
with torch.no_grad():
UpperCAmelCase = pt_model_loaded(**a_ ).to_tuple()
self.assertEqual(
len(a_ ) , len(a_ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(a_ , a_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@tooslow
def UpperCAmelCase__ ( self :Union[str, Any] ) -> Any:
for model_class_name in self.all_model_classes:
UpperCAmelCase = model_class_name.from_pretrained('EleutherAI/gpt-j-6B' )
UpperCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(a_ )
| 78 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
SCREAMING_SNAKE_CASE : Union[str, Any] = """\
"""
SCREAMING_SNAKE_CASE : Any = """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
"""
SCREAMING_SNAKE_CASE : Dict = """
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to 'cuda' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
>>> results = perplexity.compute(model_id='gpt2',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
78.22
>>> print(round(results[\"perplexities\"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = datasets.load_dataset(\"wikitext\",
... \"wikitext-2-raw-v1\",
... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!='']
>>> results = perplexity.compute(model_id='gpt2',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
60.35
>>> print(round(results[\"perplexities\"][0], 2))
81.12
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''input_texts''': datasets.Value('''string''' ),
} ) , reference_urls=['''https://huggingface.co/docs/transformers/perplexity'''] , )
def SCREAMING_SNAKE_CASE (self , a_ , a_ , a_ = 16 , a_ = True , a_=None ):
'''simple docstring'''
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
__snake_case : Optional[Any] = '''cuda'''
else:
__snake_case : Tuple = '''cuda''' if torch.cuda.is_available() else '''cpu'''
__snake_case : int = AutoModelForCausalLM.from_pretrained(a_ )
__snake_case : Optional[int] = model.to(a_ )
__snake_case : Optional[int] = AutoTokenizer.from_pretrained(a_ )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
__snake_case : List[Any] = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(a_ ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
__snake_case : List[Any] = model.config.max_length - 1
else:
__snake_case : Dict = model.config.max_length
__snake_case : Tuple = tokenizer(
a_ , add_special_tokens=a_ , padding=a_ , truncation=a_ , max_length=a_ , return_tensors='''pt''' , return_attention_mask=a_ , ).to(a_ )
__snake_case : List[Any] = encodings['''input_ids''']
__snake_case : str = encodings['''attention_mask''']
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
__snake_case : Union[str, Any] = []
__snake_case : str = CrossEntropyLoss(reduction='''none''' )
for start_index in logging.tqdm(range(0 , len(a_ ) , a_ ) ):
__snake_case : Optional[int] = min(start_index + batch_size , len(a_ ) )
__snake_case : int = encoded_texts[start_index:end_index]
__snake_case : Optional[int] = attn_masks[start_index:end_index]
if add_start_token:
__snake_case : List[Any] = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(a_ )
__snake_case : Union[str, Any] = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
__snake_case : int = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(a_ ), attn_mask] , dim=1 )
__snake_case : List[Any] = encoded_batch
with torch.no_grad():
__snake_case : List[str] = model(a_ , attention_mask=a_ ).logits
__snake_case : List[str] = out_logits[..., :-1, :].contiguous()
__snake_case : int = labels[..., 1:].contiguous()
__snake_case : int = attn_mask[..., 1:].contiguous()
__snake_case : Tuple = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , a_ ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(a_ )}
| 102 | 0 |
from maths.prime_check import is_prime
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : Dict = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE )
if is_prime(SCREAMING_SNAKE_CASE ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )->None:
'''simple docstring'''
warnings.warn(
'''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use YolosImageProcessor instead.''' , _SCREAMING_SNAKE_CASE , )
super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
| 65 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ = {"configuration_vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMAEConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST",
"ViTMAEForPreTraining",
"ViTMAELayer",
"ViTMAEModel",
"ViTMAEPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"TFViTMAEForPreTraining",
"TFViTMAEModel",
"TFViTMAEPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 241 | import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A : Tuple = "src/transformers"
A : Optional[Any] = "docs/source/en/tasks"
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
with open(__UpperCamelCase , "r" , encoding="utf-8" , newline="\n" ) as f:
SCREAMING_SNAKE_CASE_ = f.readlines()
# Find the start prompt.
SCREAMING_SNAKE_CASE_ = 0
while not lines[start_index].startswith(__UpperCamelCase ):
start_index += 1
start_index += 1
SCREAMING_SNAKE_CASE_ = start_index
while not lines[end_index].startswith(__UpperCamelCase ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
A : List[str] = direct_transformers_import(TRANSFORMERS_PATH)
A : List[Any] = {
"asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
"audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
"language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
"image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
"masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
"multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
"object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
"question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
"semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
"sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
"summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
"translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
"document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
"monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
A : Any = {
"summarization.md": ("nllb",),
"translation.md": ("nllb",),
}
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = TASK_GUIDE_TO_MODELS[task_guide]
SCREAMING_SNAKE_CASE_ = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__UpperCamelCase , set() )
SCREAMING_SNAKE_CASE_ = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([F'''[{name}](../model_doc/{code})''' for code, name in model_names.items()] ) + "\n"
def a__ ( __UpperCamelCase , __UpperCamelCase=False ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = _find_text_in_file(
filename=os.path.join(__UpperCamelCase , __UpperCamelCase ) , start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->" , end_prompt="<!--End of the generated tip-->" , )
SCREAMING_SNAKE_CASE_ = get_model_list_for_task(__UpperCamelCase )
if current_list != new_list:
if overwrite:
with open(os.path.join(__UpperCamelCase , __UpperCamelCase ) , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
F'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
" to fix this." )
if __name__ == "__main__":
A : Tuple = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
A : Dict = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite)
| 118 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __UpperCamelCase ( *_lowerCAmelCase ) -> List[str]:
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
A : List[str] = list(_lowerCAmelCase )
for i in range(len(_lowerCAmelCase ) ):
A : Optional[int] = None
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
return objects
def __UpperCamelCase ( _lowerCAmelCase ) -> bool:
"""simple docstring"""
A : Dict = [
"""CUDA out of memory.""", # CUDA OOM
"""cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""", # CUDNN SNAFU
"""DefaultCPUAllocator: can't allocate memory""", # CPU OOM
]
if isinstance(_lowerCAmelCase , _lowerCAmelCase ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def __UpperCamelCase ( _lowerCAmelCase = None , _lowerCAmelCase = 128 ) -> List[str]:
"""simple docstring"""
if function is None:
return functools.partial(_lowerCAmelCase , starting_batch_size=_lowerCAmelCase )
A : Any = starting_batch_size
def decorator(*_lowerCAmelCase , **_lowerCAmelCase ):
nonlocal batch_size
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
A : Tuple = list(inspect.signature(_lowerCAmelCase ).parameters.keys() )
# Guard against user error
if len(_lowerCAmelCase ) < (len(_lowerCAmelCase ) + 1):
A : str = """, """.join([f'''{arg}={value}''' for arg, value in zip(params[1:] , args[1:] )] )
raise TypeError(
f'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
f'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`''' )
while True:
if batch_size == 0:
raise RuntimeError("""No executable batch size found, reached zero.""" )
try:
return function(_lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase )
except Exception as e:
if should_reduce_batch_size(_lowerCAmelCase ):
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
batch_size //= 2
else:
raise
return decorator | 368 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import OwlViTImageProcessor, OwlViTProcessor
@require_vision
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase ( self ):
A : str = tempfile.mkdtemp()
# fmt: off
A : List[Any] = ["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
A : Optional[int] = dict(zip(lowerCamelCase__, range(len(lowerCamelCase__ ) ) ) )
A : Optional[Any] = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
A : Union[str, Any] = {"""unk_token""": """<unk>"""}
A : List[Any] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["""vocab_file"""] )
A : int = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file, """w""", encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) + """\n""" )
with open(self.merges_file, """w""", encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowerCamelCase__ ) )
A : int = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
"""image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
A : List[Any] = os.path.join(self.tmpdirname, lowerCamelCase__ )
with open(self.image_processor_file, """w""", encoding="""utf-8""" ) as fp:
json.dump(lowerCamelCase__, lowerCamelCase__ )
def _lowerCAmelCase ( self, **lowerCamelCase__ ):
return CLIPTokenizer.from_pretrained(self.tmpdirname, pad_token="""!""", **lowerCamelCase__ )
def _lowerCAmelCase ( self, **lowerCamelCase__ ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname, pad_token="""!""", **lowerCamelCase__ )
def _lowerCAmelCase ( self, **lowerCamelCase__ ):
return OwlViTImageProcessor.from_pretrained(self.tmpdirname, **lowerCamelCase__ )
def _lowerCAmelCase ( self ):
shutil.rmtree(self.tmpdirname )
def _lowerCAmelCase ( self ):
A : str = [np.random.randint(255, size=(3, 30, 400), dtype=np.uinta )]
A : Optional[int] = [Image.fromarray(np.moveaxis(lowerCamelCase__, 0, -1 ) ) for x in image_inputs]
return image_inputs
def _lowerCAmelCase ( self ):
A : Optional[Any] = self.get_tokenizer()
A : Optional[Any] = self.get_rust_tokenizer()
A : Optional[int] = self.get_image_processor()
A : List[str] = OwlViTProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
processor_slow.save_pretrained(self.tmpdirname )
A : Optional[int] = OwlViTProcessor.from_pretrained(self.tmpdirname, use_fast=lowerCamelCase__ )
A : Tuple = OwlViTProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
processor_fast.save_pretrained(self.tmpdirname )
A : str = OwlViTProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer, lowerCamelCase__ )
self.assertIsInstance(processor_fast.tokenizer, lowerCamelCase__ )
self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor, lowerCamelCase__ )
self.assertIsInstance(processor_fast.image_processor, lowerCamelCase__ )
def _lowerCAmelCase ( self ):
A : List[str] = OwlViTProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A : Optional[int] = self.get_tokenizer(bos_token="""(BOS)""", eos_token="""(EOS)""" )
A : Tuple = self.get_image_processor(do_normalize=lowerCamelCase__ )
A : Optional[Any] = OwlViTProcessor.from_pretrained(
self.tmpdirname, bos_token="""(BOS)""", eos_token="""(EOS)""", do_normalize=lowerCamelCase__ )
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer, lowerCamelCase__ )
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor, lowerCamelCase__ )
def _lowerCAmelCase ( self ):
A : List[Any] = self.get_image_processor()
A : str = self.get_tokenizer()
A : List[str] = OwlViTProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : Optional[Any] = self.prepare_image_inputs()
A : Optional[Any] = image_processor(lowerCamelCase__, return_tensors="""np""" )
A : Any = processor(images=lowerCamelCase__, return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum(), input_processor[key].sum(), delta=1e-2 )
def _lowerCAmelCase ( self ):
A : int = self.get_image_processor()
A : Optional[Any] = self.get_tokenizer()
A : Optional[int] = OwlViTProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : Any = """lower newer"""
A : Union[str, Any] = processor(text=lowerCamelCase__, return_tensors="""np""" )
A : str = tokenizer(lowerCamelCase__, return_tensors="""np""" )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key][0].tolist(), encoded_processor[key][0].tolist() )
def _lowerCAmelCase ( self ):
A : Tuple = self.get_image_processor()
A : int = self.get_tokenizer()
A : str = OwlViTProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : List[str] = """lower newer"""
A : Any = self.prepare_image_inputs()
A : Tuple = processor(text=lowerCamelCase__, images=lowerCamelCase__ )
self.assertListEqual(list(inputs.keys() ), ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase__ ):
processor()
def _lowerCAmelCase ( self ):
A : str = """google/owlvit-base-patch32"""
A : Dict = OwlViTProcessor.from_pretrained(lowerCamelCase__ )
A : str = ["""cat""", """nasa badge"""]
A : Optional[int] = processor(text=lowerCamelCase__ )
A : Any = 16
self.assertListEqual(list(inputs.keys() ), ["""input_ids""", """attention_mask"""] )
self.assertEqual(inputs["""input_ids"""].shape, (2, seq_length) )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase__ ):
processor()
def _lowerCAmelCase ( self ):
A : Tuple = """google/owlvit-base-patch32"""
A : Any = OwlViTProcessor.from_pretrained(lowerCamelCase__ )
A : int = [["""cat""", """nasa badge"""], ["""person"""]]
A : List[Any] = processor(text=lowerCamelCase__ )
A : Dict = 16
A : List[str] = len(lowerCamelCase__ )
A : List[str] = max([len(lowerCamelCase__ ) for texts in input_texts] )
self.assertListEqual(list(inputs.keys() ), ["""input_ids""", """attention_mask"""] )
self.assertEqual(inputs["""input_ids"""].shape, (batch_size * num_max_text_queries, seq_length) )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase__ ):
processor()
def _lowerCAmelCase ( self ):
A : Dict = """google/owlvit-base-patch32"""
A : int = OwlViTProcessor.from_pretrained(lowerCamelCase__ )
A : str = ["""cat""", """nasa badge"""]
A : Optional[Any] = processor(text=lowerCamelCase__ )
A : int = 16
A : Optional[Any] = inputs["""input_ids"""]
A : Optional[int] = [
[4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[4_9406, 6841, 1_1301, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
self.assertListEqual(list(inputs.keys() ), ["""input_ids""", """attention_mask"""] )
self.assertEqual(inputs["""input_ids"""].shape, (2, seq_length) )
self.assertListEqual(list(input_ids[0] ), predicted_ids[0] )
self.assertListEqual(list(input_ids[1] ), predicted_ids[1] )
def _lowerCAmelCase ( self ):
A : Tuple = self.get_image_processor()
A : Dict = self.get_tokenizer()
A : Optional[Any] = OwlViTProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : Any = self.prepare_image_inputs()
A : Optional[Any] = self.prepare_image_inputs()
A : List[str] = processor(images=lowerCamelCase__, query_images=lowerCamelCase__ )
self.assertListEqual(list(inputs.keys() ), ["""query_pixel_values""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase__ ):
processor()
def _lowerCAmelCase ( self ):
A : Any = self.get_image_processor()
A : Optional[Any] = self.get_tokenizer()
A : List[str] = OwlViTProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : Any = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A : Optional[Any] = processor.batch_decode(lowerCamelCase__ )
A : Union[str, Any] = tokenizer.batch_decode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__, lowerCamelCase__ )
| 115 | 0 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class lowercase_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__( self , a , a , a , a = 1.0 , a = None , ):
super().__init__()
UpperCamelCase__ = initial_learning_rate
UpperCamelCase__ = warmup_steps
UpperCamelCase__ = power
UpperCamelCase__ = decay_schedule_fn
UpperCamelCase__ = name
def __call__( self , a ):
with tf.name_scope(self.name or "WarmUp" ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
UpperCamelCase__ = tf.cast(A__ , tf.floataa )
UpperCamelCase__ = tf.cast(self.warmup_steps , tf.floataa )
UpperCamelCase__ = global_step_float / warmup_steps_float
UpperCamelCase__ = self.initial_learning_rate * tf.math.pow(A__ , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=A__ , )
def __a ( self ):
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def _UpperCamelCase ( __A , __A , __A , __A = 0.0 , __A = 0.9 , __A = 0.999 , __A = 1E-8 , __A = None , __A = None , __A = 0.0 , __A = 1.0 , __A = None , ) -> str:
'''simple docstring'''
UpperCamelCase__ = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=lowerCAmelCase__ , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=lowerCAmelCase__ , )
if num_warmup_steps:
UpperCamelCase__ = WarmUp(
initial_learning_rate=lowerCAmelCase__ , decay_schedule_fn=lowerCAmelCase__ , warmup_steps=lowerCAmelCase__ , )
if weight_decay_rate > 0.0:
UpperCamelCase__ = AdamWeightDecay(
learning_rate=lowerCAmelCase__ , weight_decay_rate=lowerCAmelCase__ , beta_a=lowerCAmelCase__ , beta_a=lowerCAmelCase__ , epsilon=lowerCAmelCase__ , clipnorm=lowerCAmelCase__ , global_clipnorm=lowerCAmelCase__ , exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"] , include_in_weight_decay=lowerCAmelCase__ , )
else:
UpperCamelCase__ = tf.keras.optimizers.Adam(
learning_rate=lowerCAmelCase__ , beta_a=lowerCAmelCase__ , beta_a=lowerCAmelCase__ , epsilon=lowerCAmelCase__ , clipnorm=lowerCAmelCase__ , global_clipnorm=lowerCAmelCase__ , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class lowercase_ ( SCREAMING_SNAKE_CASE__ ):
def __init__( self , a = 0.001 , a = 0.9 , a = 0.999 , a = 1e-7 , a = False , a = 0.0 , a = None , a = None , a = "AdamWeightDecay" , **a , ):
super().__init__(A__ , A__ , A__ , A__ , A__ , A__ , **A__ )
UpperCamelCase__ = weight_decay_rate
UpperCamelCase__ = include_in_weight_decay
UpperCamelCase__ = exclude_from_weight_decay
@classmethod
def __a ( cls , a ):
UpperCamelCase__ = {"WarmUp": WarmUp}
return super(A__ , cls ).from_config(A__ , custom_objects=A__ )
def __a ( self , a , a , a ):
super(A__ , self )._prepare_local(A__ , A__ , A__ )
UpperCamelCase__ = tf.constant(
self.weight_decay_rate , name="adam_weight_decay_rate" )
def __a ( self , a , a , a ):
UpperCamelCase__ = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"] , use_locking=self._use_locking , )
return tf.no_op()
def __a ( self , a , a=None , **a ):
UpperCamelCase__ , UpperCamelCase__ = list(zip(*A__ ) )
return super(A__ , self ).apply_gradients(zip(A__ , A__ ) , name=A__ , **A__ )
def __a ( self , a , a , a ):
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
UpperCamelCase__ = apply_state or {}
UpperCamelCase__ = apply_state.get((var_device, var_dtype) )
if coefficients is None:
UpperCamelCase__ = self._fallback_apply_state(A__ , A__ )
UpperCamelCase__ = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def __a ( self , a , a , a=None ):
UpperCamelCase__ , UpperCamelCase__ = self._get_lr(var.device , var.dtype.base_dtype , A__ )
UpperCamelCase__ = self._decay_weights_op(A__ , A__ , A__ )
with tf.control_dependencies([decay] ):
return super(A__ , self )._resource_apply_dense(A__ , A__ , **A__ )
def __a ( self , a , a , a , a=None ):
UpperCamelCase__ , UpperCamelCase__ = self._get_lr(var.device , var.dtype.base_dtype , A__ )
UpperCamelCase__ = self._decay_weights_op(A__ , A__ , A__ )
with tf.control_dependencies([decay] ):
return super(A__ , self )._resource_apply_sparse(A__ , A__ , A__ , **A__ )
def __a ( self ):
UpperCamelCase__ = super().get_config()
config.update({"weight_decay_rate": self.weight_decay_rate} )
return config
def __a ( self , a ):
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(A__ , A__ ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(A__ , A__ ) is not None:
return False
return True
class lowercase_ ( SCREAMING_SNAKE_CASE__ ):
def __init__( self ):
UpperCamelCase__ = []
UpperCamelCase__ = None
@property
def __a ( self ):
if self._accum_steps is None:
UpperCamelCase__ = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=A__ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def __a ( self ):
if not self._gradients:
raise ValueError("The accumulator should be called first to initialize the gradients" )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self , a ):
if not self._gradients:
UpperCamelCase__ = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(A__ ) , trainable=A__ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(A__ ) != len(self._gradients ):
raise ValueError(f'''Expected {len(self._gradients )} gradients, but got {len(A__ )}''' )
for accum_gradient, gradient in zip(self._gradients , A__ ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(A__ )
self._accum_steps.assign_add(1 )
def __a ( self ):
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(A__ ) )
| 80 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ :str = logging.get_logger(__name__)
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = '''huggingface/label-files'''
lowercase = '''imagenet-1k-id2label.json'''
lowercase = json.load(open(hf_hub_download(lowerCAmelCase__ , lowerCAmelCase__ , repo_type='''dataset''' ) , '''r''' ) )
lowercase = {int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
lowercase = {v: k for k, v in idalabel.items()}
lowercase = '''std_conv''' if '''bit''' in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
lowercase = BitConfig(
conv_layer=lowerCAmelCase__ , num_labels=1000 , idalabel=lowerCAmelCase__ , labelaid=lowerCAmelCase__ , )
return config
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if "stem.conv" in name:
lowercase = name.replace('''stem.conv''' , '''bit.embedder.convolution''' )
if "blocks" in name:
lowercase = name.replace('''blocks''' , '''layers''' )
if "head.fc" in name:
lowercase = name.replace('''head.fc''' , '''classifier.1''' )
if name.startswith('''norm''' ):
lowercase = '''bit.''' + name
if "bit" not in name and "classifier" not in name:
lowercase = '''bit.encoder.''' + name
return name
def UpperCamelCase ( ):
'''simple docstring'''
lowercase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw )
return im
@torch.no_grad()
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=False ):
'''simple docstring'''
lowercase = get_config(lowerCAmelCase__ )
# load original model from timm
lowercase = create_model(lowerCAmelCase__ , pretrained=lowerCAmelCase__ )
timm_model.eval()
# load state_dict of original model
lowercase = timm_model.state_dict()
for key in state_dict.copy().keys():
lowercase = state_dict.pop(lowerCAmelCase__ )
lowercase = val.squeeze() if '''head''' in key else val
# load HuggingFace model
lowercase = BitForImageClassification(lowerCAmelCase__ )
model.eval()
model.load_state_dict(lowerCAmelCase__ )
# create image processor
lowercase = create_transform(**resolve_data_config({} , model=lowerCAmelCase__ ) )
lowercase = transform.transforms
lowercase = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
lowercase = BitImageProcessor(
do_resize=lowerCAmelCase__ , size={'''shortest_edge''': timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=lowerCAmelCase__ , crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} , do_normalize=lowerCAmelCase__ , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
lowercase = prepare_img()
lowercase = transform(lowerCAmelCase__ ).unsqueeze(0 )
lowercase = processor(lowerCAmelCase__ , return_tensors='''pt''' ).pixel_values
# verify pixel values
assert torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ )
# verify logits
with torch.no_grad():
lowercase = model(lowerCAmelCase__ )
lowercase = outputs.logits
print('''Logits:''' , logits[0, :3] )
print('''Predicted class:''' , model.config.idalabel[logits.argmax(-1 ).item()] )
lowercase = timm_model(lowerCAmelCase__ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(lowerCAmelCase__ , outputs.logits , atol=1E-3 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )
print(f'Saving model {model_name} and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCAmelCase__ )
processor.save_pretrained(lowerCAmelCase__ )
if push_to_hub:
print(f'Pushing model {model_name} and processor to the hub' )
model.push_to_hub(f'ybelkada/{model_name}' )
processor.push_to_hub(f'ybelkada/{model_name}' )
if __name__ == "__main__":
lowercase__ :List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="resnetv2_50x1_bitm",
type=str,
help="Name of the BiT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model to the hub.",
)
lowercase__ :List[str] = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 101 | 0 |
'''simple docstring'''
from math import factorial
lowerCAmelCase_ : Any = {str(d): factorial(d) for d in range(10)}
def _lowerCamelCase ( lowercase : List[str] ) -> Dict:
return sum(DIGIT_FACTORIAL[d] for d in str(lowercase ) )
def _lowerCamelCase ( ) -> Optional[int]:
_a = 7 * factorial(9 ) + 1
return sum(i for i in range(3 , lowercase ) if sum_of_digit_factorial(lowercase ) == i )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 367 |
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer
logging.basicConfig(
format='%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=os.environ.get('LOGLEVEL', 'INFO').upper(),
stream=sys.stdout,
)
lowerCAmelCase_ : List[Any] = logging.getLogger(__name__)
lowerCAmelCase_ : List[Any] = {'facebook/bart-base': BartForConditionalGeneration}
lowerCAmelCase_ : int = {'facebook/bart-base': BartTokenizer}
def _lowerCamelCase ( ) -> Union[str, Any]:
_a = argparse.ArgumentParser(description="Export Bart model + Beam Search to ONNX graph." )
parser.add_argument(
"--validation_file" , type=lowercase , default=lowercase , help="A csv or a json file containing the validation data." )
parser.add_argument(
"--max_length" , type=lowercase , default=5 , help="The maximum total input sequence length after tokenization." , )
parser.add_argument(
"--num_beams" , type=lowercase , default=lowercase , help=(
"Number of beams to use for evaluation. This argument will be "
"passed to ``model.generate``, which is used during ``evaluate`` and ``predict``."
) , )
parser.add_argument(
"--model_name_or_path" , type=lowercase , help="Path to pretrained model or model identifier from huggingface.co/models." , required=lowercase , )
parser.add_argument(
"--config_name" , type=lowercase , default=lowercase , help="Pretrained config name or path if not the same as model_name" , )
parser.add_argument(
"--device" , type=lowercase , default="cpu" , help="Device where the model will be run" , )
parser.add_argument("--output_file_path" , type=lowercase , default=lowercase , help="Where to store the final ONNX file." )
_a = parser.parse_args()
return args
def _lowerCamelCase ( lowercase : Any , lowercase : Tuple="cpu" ) -> Optional[Any]:
_a = model_dict[model_name].from_pretrained(lowercase ).to(lowercase )
_a = tokenizer_dict[model_name].from_pretrained(lowercase )
if model_name in ["facebook/bart-base"]:
_a = 0
_a = None
_a = 0
return huggingface_model, tokenizer
def _lowerCamelCase ( lowercase : List[str] , lowercase : Tuple , lowercase : int , lowercase : Any , lowercase : Dict ) -> Any:
model.eval()
_a = None
_a = torch.jit.script(BARTBeamSearchGenerator(lowercase ) )
with torch.no_grad():
_a = "My friends are cool but they eat too many carbs."
_a = tokenizer([ARTICLE_TO_SUMMARIZE] , max_length=1024 , return_tensors="pt" ).to(model.device )
_a = model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , num_beams=lowercase , max_length=lowercase , early_stopping=lowercase , decoder_start_token_id=model.config.decoder_start_token_id , )
torch.onnx.export(
lowercase , (
inputs["input_ids"],
inputs["attention_mask"],
num_beams,
max_length,
model.config.decoder_start_token_id,
) , lowercase , opset_version=14 , input_names=["input_ids", "attention_mask", "num_beams", "max_length", "decoder_start_token_id"] , output_names=["output_ids"] , dynamic_axes={
"input_ids": {0: "batch", 1: "seq"},
"output_ids": {0: "batch", 1: "seq_out"},
} , example_outputs=lowercase , )
logger.info("Model exported to {}".format(lowercase ) )
_a = remove_dup_initializers(os.path.abspath(lowercase ) )
logger.info("Deduplicated and optimized model written to {}".format(lowercase ) )
_a = onnxruntime.InferenceSession(lowercase )
_a = ort_sess.run(
lowercase , {
"input_ids": inputs["input_ids"].cpu().numpy(),
"attention_mask": inputs["attention_mask"].cpu().numpy(),
"num_beams": np.array(lowercase ),
"max_length": np.array(lowercase ),
"decoder_start_token_id": np.array(model.config.decoder_start_token_id ),
} , )
np.testing.assert_allclose(summary_ids.cpu().numpy() , ort_out[0] , rtol=1E-3 , atol=1E-3 )
logger.info("Model outputs from torch and ONNX Runtime are similar." )
logger.info("Success." )
def _lowerCamelCase ( ) -> Any:
_a = parse_args()
_a = 5
_a = 4
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO , )
logger.setLevel(logging.INFO )
transformers.utils.logging.set_verbosity_error()
_a = torch.device(args.device )
_a , _a = load_model_tokenizer(args.model_name_or_path , lowercase )
if model.config.decoder_start_token_id is None:
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined" )
model.to(lowercase )
if args.max_length:
_a = args.max_length
if args.num_beams:
_a = args.num_beams
if args.output_file_path:
_a = args.output_file_path
else:
_a = "BART.onnx"
logger.info("Exporting model to ONNX" )
export_and_validate_model(lowercase , lowercase , lowercase , lowercase , lowercase )
if __name__ == "__main__":
main()
| 346 | 0 |
# 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.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def UpperCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
_lowercase =ArgumentParser('''Accelerate CLI tool''' , usage='''accelerate <command> [<args>]''' , allow_abbrev=__snake_case )
_lowercase =parser.add_subparsers(help='''accelerate command helpers''' )
# Register commands
get_config_parser(subparsers=__snake_case )
env_command_parser(subparsers=__snake_case )
launch_command_parser(subparsers=__snake_case )
tpu_command_parser(subparsers=__snake_case )
test_command_parser(subparsers=__snake_case )
# Let's go
_lowercase =parser.parse_args()
if not hasattr(__snake_case , '''func''' ):
parser.print_help()
exit(1 )
# Run
args.func(__snake_case )
if __name__ == "__main__":
main()
| 5 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
"""
lowerCAmelCase__ = """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""
lowerCAmelCase__ = """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair as given in the references (see below)
- 'prediction_text': list of possible texts for the answer, as a list of strings
depending on a threshold on the confidence probability of each prediction.
references: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair (see above),
- 'answers': a Dict in the CUAD dataset format
{
'text': list of possible texts for the answer, as a list of strings
'answer_start': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
'exact_match': Exact match (the normalized answer exactly match the gold answer)
'f1': The F-score of predicted tokens versus the gold answer
'aupr': Area Under the Precision-Recall curve
'prec_at_80_recall': Precision at 80% recall
'prec_at_90_recall': Precision at 90% recall
Examples:
>>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> cuad_metric = datasets.load_metric(\"cuad\")
>>> results = cuad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": {
"id": datasets.Value("string" ),
"prediction_text": datasets.features.Sequence(datasets.Value("string" ) ),
},
"references": {
"id": datasets.Value("string" ),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
},
} ) , codebase_urls=["https://www.atticusprojectai.org/cuad"] , reference_urls=["https://www.atticusprojectai.org/cuad"] , )
def UpperCamelCase ( self , lowercase , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
A__ = [
{
"paragraphs": [
{
"qas": [
{
"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
"id": ref["id"],
}
for ref in references
]
}
]
}
]
A__ = evaluate(dataset=lowercase , predictions=lowercase )
return score
| 68 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : str , UpperCAmelCase_ : str ) -> str:
'''simple docstring'''
__snake_case : int = len(UpperCAmelCase_ )
__snake_case : int = len(UpperCAmelCase_ )
__snake_case : int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
__snake_case : 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=" ")
| 95 | """simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
while a != 0:
__snake_case , __snake_case : Union[str, Any] = b % a, a
return b
def __UpperCAmelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
if gcd(UpperCAmelCase_ , UpperCAmelCase_ ) != 1:
__snake_case : Union[str, Any] = F"mod inverse of {a!r} and {m!r} does not exist"
raise ValueError(UpperCAmelCase_ )
__snake_case , __snake_case , __snake_case : List[str] = 1, 0, a
__snake_case , __snake_case , __snake_case : Dict = 0, 1, m
while va != 0:
__snake_case : List[str] = ua // va
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case : List[str] = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 95 | 1 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def __lowerCamelCase ( UpperCAmelCase_ : Union[str, Any] ):
"""simple docstring"""
return 1.0 / (1.0 + np.exp(-_outputs ))
def __lowerCamelCase ( UpperCAmelCase_ : Tuple ):
"""simple docstring"""
a :str = np.max(_outputs , axis=-1 , keepdims=UpperCAmelCase_ )
a :Optional[int] = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=UpperCAmelCase_ )
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 'sigmoid'
SCREAMING_SNAKE_CASE__ = 'softmax'
SCREAMING_SNAKE_CASE__ = 'none'
@add_end_docstrings(
_snake_case , r'\n return_all_scores (`bool`, *optional*, defaults to `False`):\n Whether to return all prediction scores or just the one of the predicted class.\n function_to_apply (`str`, *optional*, defaults to `"default"`):\n The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n has several labels, will apply the softmax function on the output.\n - `"sigmoid"`: Applies the sigmoid function on the output.\n - `"softmax"`: Applies the softmax function on the output.\n - `"none"`: Does not apply any function on the output.\n ' , )
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = ClassificationFunction.NONE
def __init__( self , **_lowerCamelCase ):
super().__init__(**_lowerCamelCase )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase="" , **_lowerCamelCase ):
# Using "" as default argument because we're going to use `top_k=None` in user code to declare
# "No top_k"
a :int = tokenizer_kwargs
a :Optional[int] = {}
if hasattr(self.model.config , '''return_all_scores''' ) and return_all_scores is None:
a :List[Any] = self.model.config.return_all_scores
if isinstance(_lowerCamelCase , _lowerCamelCase ) or top_k is None:
a :List[str] = top_k
a :int = False
elif return_all_scores is not None:
warnings.warn(
'''`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of'''
''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.''' , _lowerCamelCase , )
if return_all_scores:
a :int = None
else:
a :Optional[Any] = 1
if isinstance(_lowerCamelCase , _lowerCamelCase ):
a :Any = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
a :Dict = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self , *_lowerCamelCase , **_lowerCamelCase ):
a :str = super().__call__(*_lowerCamelCase , **_lowerCamelCase )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
a :Union[str, Any] = '''top_k''' not in kwargs
if isinstance(args[0] , _lowerCamelCase ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , **_lowerCamelCase ):
a :Optional[Any] = self.framework
if isinstance(_lowerCamelCase , _lowerCamelCase ):
return self.tokenizer(**_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
elif isinstance(_lowerCamelCase , _lowerCamelCase ) and len(_lowerCamelCase ) == 1 and isinstance(inputs[0] , _lowerCamelCase ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=_lowerCamelCase , **_lowerCamelCase )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
'''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.''' )
return self.tokenizer(_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
return self.model(**_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase=None , _lowerCamelCase=1 , _lowerCamelCase=True ):
# `_legacy` is used to determine if we're running the naked pipeline and in backward
# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
# the more natural result containing the list.
# Default value before `set_parameters`
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
a :Optional[int] = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
a :Tuple = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , '''function_to_apply''' ) and function_to_apply is None:
a :List[Any] = self.model.config.function_to_apply
else:
a :Any = ClassificationFunction.NONE
a :List[Any] = model_outputs['''logits'''][0]
a :Dict = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
a :List[Any] = sigmoid(_lowerCamelCase )
elif function_to_apply == ClassificationFunction.SOFTMAX:
a :Optional[Any] = softmax(_lowerCamelCase )
elif function_to_apply == ClassificationFunction.NONE:
a :List[str] = outputs
else:
raise ValueError(F'''Unrecognized `function_to_apply` argument: {function_to_apply}''' )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
a :List[str] = [
{'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(_lowerCamelCase )
]
if not _legacy:
dict_scores.sort(key=lambda _lowerCamelCase : x["score"] , reverse=_lowerCamelCase )
if top_k is not None:
a :Any = dict_scores[:top_k]
return dict_scores
| 94 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Union[str, Any]="attention" ):
"""simple docstring"""
a :Optional[int] = params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
a :Optional[Any] = params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
a :int = params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
a :Optional[Any] = params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
return k, o, q, v
def __lowerCamelCase ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : str , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int=False ):
"""simple docstring"""
if split_mlp_wi:
a :int = params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
a :Optional[Any] = params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
a :Dict = (wi_a, wi_a)
else:
a :Optional[Any] = params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']
a :Dict = params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
return wi, wo
def __lowerCamelCase ( UpperCAmelCase_ : str , UpperCAmelCase_ : int , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[int] ):
"""simple docstring"""
return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def __lowerCamelCase ( UpperCAmelCase_ : dict , *, UpperCAmelCase_ : int , UpperCAmelCase_ : bool ):
"""simple docstring"""
a :str = traverse_util.flatten_dict(variables['''target'''] )
a :Any = {'''/'''.join(UpperCAmelCase_ ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
a :Any = '''encoder/layers_0/mlp/wi_0/kernel''' in old
print('''Split MLP:''' , UpperCAmelCase_ )
a :Optional[Any] = collections.OrderedDict()
# Shared embeddings.
a :Union[str, Any] = old['''token_embedder/embedding''']
# Encoder.
for i in range(UpperCAmelCase_ ):
# Block i, layer 0 (Self Attention).
a :Optional[Any] = tax_layer_norm_lookup(UpperCAmelCase_ , UpperCAmelCase_ , '''encoder''' , '''pre_attention_layer_norm''' )
a , a , a , a :Optional[int] = tax_attention_lookup(UpperCAmelCase_ , UpperCAmelCase_ , '''encoder''' , '''attention''' )
a :List[Any] = layer_norm
a :str = k.T
a :Dict = o.T
a :int = q.T
a :Optional[Any] = v.T
# Block i, layer 1 (MLP).
a :Tuple = tax_layer_norm_lookup(UpperCAmelCase_ , UpperCAmelCase_ , '''encoder''' , '''pre_mlp_layer_norm''' )
a , a :List[Any] = tax_mlp_lookup(UpperCAmelCase_ , UpperCAmelCase_ , '''encoder''' , UpperCAmelCase_ )
a :Any = layer_norm
if split_mlp_wi:
a :Any = wi[0].T
a :Tuple = wi[1].T
else:
a :List[str] = wi.T
a :List[Any] = wo.T
a :Union[str, Any] = old[
'''encoder/relpos_bias/rel_embedding'''
].T
a :Optional[Any] = old['''encoder/encoder_norm/scale''']
if not is_encoder_only:
# Decoder.
for i in range(UpperCAmelCase_ ):
# Block i, layer 0 (Self Attention).
a :List[str] = tax_layer_norm_lookup(UpperCAmelCase_ , UpperCAmelCase_ , '''decoder''' , '''pre_self_attention_layer_norm''' )
a , a , a , a :List[Any] = tax_attention_lookup(UpperCAmelCase_ , UpperCAmelCase_ , '''decoder''' , '''self_attention''' )
a :List[Any] = layer_norm
a :Tuple = k.T
a :int = o.T
a :Any = q.T
a :Optional[int] = v.T
# Block i, layer 1 (Cross Attention).
a :str = tax_layer_norm_lookup(UpperCAmelCase_ , UpperCAmelCase_ , '''decoder''' , '''pre_cross_attention_layer_norm''' )
a , a , a , a :Any = tax_attention_lookup(UpperCAmelCase_ , UpperCAmelCase_ , '''decoder''' , '''encoder_decoder_attention''' )
a :str = layer_norm
a :Optional[Any] = k.T
a :Any = o.T
a :Dict = q.T
a :Optional[Any] = v.T
# Block i, layer 2 (MLP).
a :Optional[int] = tax_layer_norm_lookup(UpperCAmelCase_ , UpperCAmelCase_ , '''decoder''' , '''pre_mlp_layer_norm''' )
a , a :List[Any] = tax_mlp_lookup(UpperCAmelCase_ , UpperCAmelCase_ , '''decoder''' , UpperCAmelCase_ )
a :Optional[int] = layer_norm
if split_mlp_wi:
a :int = wi[0].T
a :Tuple = wi[1].T
else:
a :str = wi.T
a :Dict = wo.T
a :Any = old['''decoder/decoder_norm/scale''']
a :Optional[Any] = old[
'''decoder/relpos_bias/rel_embedding'''
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
a :Union[str, Any] = old['''decoder/logits_dense/kernel'''].T
return new
def __lowerCamelCase ( UpperCAmelCase_ : Any , UpperCAmelCase_ : bool ):
"""simple docstring"""
a :List[Any] = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
a :Optional[Any] = state_dict['''shared.weight''']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
a :Tuple = state_dict['''shared.weight''']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('''Using shared word embeddings as lm_head.''' )
a :Optional[Any] = state_dict['''shared.weight''']
return state_dict
def __lowerCamelCase ( UpperCAmelCase_ : Any , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[int] ):
"""simple docstring"""
a :Tuple = checkpoints.load_tax_checkpoint(UpperCAmelCase_ )
a :Optional[int] = convert_tax_to_pytorch(UpperCAmelCase_ , num_layers=config.num_layers , is_encoder_only=UpperCAmelCase_ )
a :Tuple = make_state_dict(UpperCAmelCase_ , UpperCAmelCase_ )
model.load_state_dict(UpperCAmelCase_ , strict=UpperCAmelCase_ )
def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : bool = False ):
"""simple docstring"""
a :List[Any] = TaConfig.from_json_file(UpperCAmelCase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
a :Any = TaEncoderModel(UpperCAmelCase_ )
else:
a :List[str] = TaForConditionalGeneration(UpperCAmelCase_ )
# Load weights from tf checkpoint
load_tax_weights_in_ta(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(UpperCAmelCase_ )
# Verify that we can load the checkpoint.
model.from_pretrained(UpperCAmelCase_ )
print('''Done''' )
if __name__ == "__main__":
snake_case : Any = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
snake_case : Optional[Any] = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 94 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
__A = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
__A = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
__A = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('int32' ),
'references': datasets.Value('int32' ),
} ) , reference_urls=[
'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'
] , )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ) -> Optional[Any]:
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(lowerCamelCase__ , lowerCamelCase__ , sample_weight=lowerCamelCase__ ) ),
}
| 350 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def lowercase_ ( self , lowerCamelCase__=0 ) -> int:
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, 3, 128, 128) , rng=random.Random(lowerCamelCase__ ) )
__lowerCamelCase = np.random.RandomState(lowerCamelCase__ )
__lowerCamelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'strength': 0.75,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
# warmup pass to apply optimizations
__lowerCamelCase = pipe(**self.get_dummy_inputs() )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> int:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = ort.SessionOptions()
__lowerCamelCase = False
return options
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((768, 512) )
# using the PNDM scheduler by default
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='onnx' , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__lowerCamelCase = np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((768, 512) )
__lowerCamelCase = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-v1-5' , subfolder='scheduler' , revision='onnx' )
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , revision='onnx' , scheduler=lowerCamelCase__ , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=20 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__lowerCamelCase = np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 348 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
SCREAMING_SNAKE_CASE :Union[str, Any] = get_logger()
SCREAMING_SNAKE_CASE :Optional[dict] = None
class UpperCAmelCase ( TensorFormatter[Mapping, "jax.Array", Mapping] ):
'''simple docstring'''
def __init__( self : List[str] ,A : Tuple=None ,A : List[Any]=None ,**A : str ):
super().__init__(features=A )
import jax
from jaxlib.xla_client import Device
if isinstance(A ,A ):
raise ValueError(
f'''Expected {device} to be a `str` not {type(A )}, as `jaxlib.xla_extension.Device` '''
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
__A = device if isinstance(A ,A ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
__A = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f'''Device with string identifier {self.device} not listed among the available '''
f'''devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default '''
f'''device: {str(jax.devices()[0] )}.''' )
__A = str(jax.devices()[0] )
__A = jnp_array_kwargs
@staticmethod
def UpperCamelCase_ ( ):
import jax
return {str(A ): device for device in jax.devices()}
def UpperCamelCase_ ( self : Any ,A : Tuple ):
import jax
import jax.numpy as jnp
if isinstance(A ,A ) and column:
if all(
isinstance(A ,jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(A ,axis=0 )
return column
def UpperCamelCase_ ( self : int ,A : int ):
import jax
import jax.numpy as jnp
if isinstance(A ,(str, bytes, type(A )) ):
return value
elif isinstance(A ,(np.character, np.ndarray) ) and np.issubdtype(value.dtype ,np.character ):
return value.tolist()
__A = {}
if isinstance(A ,(np.number, np.ndarray) ) and np.issubdtype(value.dtype ,np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
__A = {"dtype": jnp.intaa}
else:
__A = {"dtype": jnp.intaa}
elif isinstance(A ,(np.number, np.ndarray) ) and np.issubdtype(value.dtype ,np.floating ):
__A = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(A ,PIL.Image.Image ):
__A = np.asarray(A )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
__A = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(A ,**{**default_dtype, **self.jnp_array_kwargs} )
def UpperCamelCase_ ( self : Union[str, Any] ,A : Union[str, Any] ):
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(A ,torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(A ,"__array__" ) and not isinstance(A ,jax.Array ):
__A = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(A ,np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
elif isinstance(A ,(list, tuple) ):
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
return self._tensorize(A )
def UpperCamelCase_ ( self : Any ,A : dict ):
return map_nested(self._recursive_tensorize ,A ,map_list=A )
def UpperCamelCase_ ( self : List[str] ,A : pa.Table ):
__A = self.numpy_arrow_extractor().extract_row(A )
__A = self.python_features_decoder.decode_row(A )
return self.recursive_tensorize(A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : pa.Table ):
__A = self.numpy_arrow_extractor().extract_column(A )
__A = self.python_features_decoder.decode_column(A ,pa_table.column_names[0] )
__A = self.recursive_tensorize(A )
__A = self._consolidate(A )
return column
def UpperCamelCase_ ( self : Dict ,A : pa.Table ):
__A = self.numpy_arrow_extractor().extract_batch(A )
__A = self.python_features_decoder.decode_batch(A )
__A = self.recursive_tensorize(A )
for column_name in batch:
__A = self._consolidate(batch[column_name] )
return batch
| 15 |
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = [0] * len(a_ )
__A = []
__A = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
if indegree[i] == 0:
queue.append(a_ )
while queue:
__A = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
__A = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(a_ )
print(max(a_ ) )
# Adjacency list of Graph
SCREAMING_SNAKE_CASE :List[Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 15 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class lowerCAmelCase__ ( a ):
"""simple docstring"""
def __init__( self : int ) -> Dict:
"""simple docstring"""
self.test()
def UpperCAmelCase__ ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = False
while not completed:
if counter == 1:
self.reset()
__SCREAMING_SNAKE_CASE = self.advance()
if not self.does_advance(__SCREAMING_SNAKE_CASE ):
raise Exception(
"""Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.""" )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.update(__SCREAMING_SNAKE_CASE )
counter += 1
if counter > 10_000:
raise Exception("""update() does not fulfill the constraint.""" )
if self.remaining() != 0:
raise Exception("""Custom Constraint is not defined correctly.""" )
@abstractmethod
def UpperCAmelCase__ ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def UpperCAmelCase__ ( self : str , __SCREAMING_SNAKE_CASE : int ) -> Union[str, Any]:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def UpperCAmelCase__ ( self : List[str] , __SCREAMING_SNAKE_CASE : int ) -> List[str]:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def UpperCAmelCase__ ( self : Dict ) -> Optional[int]:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def UpperCAmelCase__ ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def UpperCAmelCase__ ( self : int , __SCREAMING_SNAKE_CASE : List[str]=False ) -> Any:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
class lowerCAmelCase__ ( a ):
"""simple docstring"""
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[int] ) -> Optional[int]:
"""simple docstring"""
super(__SCREAMING_SNAKE_CASE , self ).__init__()
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) or len(__SCREAMING_SNAKE_CASE ) == 0:
raise ValueError(f'`token_ids` has to be a non-empty list, but is {token_ids}.' )
if any((not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) or token_id < 0) for token_id in token_ids ):
raise ValueError(f'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.' )
__SCREAMING_SNAKE_CASE = token_ids
__SCREAMING_SNAKE_CASE = len(self.token_ids )
__SCREAMING_SNAKE_CASE = -1 # the index of the currently fulfilled step
__SCREAMING_SNAKE_CASE = False
def UpperCAmelCase__ ( self : str ) -> Optional[int]:
"""simple docstring"""
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def UpperCAmelCase__ ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : int ) -> Optional[Any]:
"""simple docstring"""
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(__SCREAMING_SNAKE_CASE )}' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def UpperCAmelCase__ ( self : Tuple , __SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(__SCREAMING_SNAKE_CASE )}' )
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
if self.does_advance(__SCREAMING_SNAKE_CASE ):
self.fulfilled_idx += 1
__SCREAMING_SNAKE_CASE = True
if self.fulfilled_idx == (self.seqlen - 1):
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = completed
else:
# failed to make progress.
__SCREAMING_SNAKE_CASE = True
self.reset()
return stepped, completed, reset
def UpperCAmelCase__ ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = 0
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return self.seqlen - (self.fulfilled_idx + 1)
def UpperCAmelCase__ ( self : Dict , __SCREAMING_SNAKE_CASE : Tuple=False ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = PhrasalConstraint(self.token_ids )
if stateful:
__SCREAMING_SNAKE_CASE = self.seqlen
__SCREAMING_SNAKE_CASE = self.fulfilled_idx
__SCREAMING_SNAKE_CASE = self.completed
return new_constraint
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : List[List[int]] , __SCREAMING_SNAKE_CASE : int=True ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = max([len(__SCREAMING_SNAKE_CASE ) for one in nested_token_ids] )
__SCREAMING_SNAKE_CASE = {}
for token_ids in nested_token_ids:
__SCREAMING_SNAKE_CASE = root
for tidx, token_id in enumerate(__SCREAMING_SNAKE_CASE ):
if token_id not in level:
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = level[token_id]
if no_subsets and self.has_subsets(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise ValueError(
"""Each list in `nested_token_ids` can't be a complete subset of another list, but is"""
f' {nested_token_ids}.' )
__SCREAMING_SNAKE_CASE = root
def UpperCAmelCase__ ( self : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.trie
for current_token in current_seq:
__SCREAMING_SNAKE_CASE = start[current_token]
__SCREAMING_SNAKE_CASE = list(start.keys() )
return next_tokens
def UpperCAmelCase__ ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.next_tokens(__SCREAMING_SNAKE_CASE )
return len(__SCREAMING_SNAKE_CASE ) == 0
def UpperCAmelCase__ ( self : Dict , __SCREAMING_SNAKE_CASE : Optional[int] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = list(root.values() )
if len(__SCREAMING_SNAKE_CASE ) == 0:
return 1
else:
return sum([self.count_leaves(__SCREAMING_SNAKE_CASE ) for nn in next_nodes] )
def UpperCAmelCase__ ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Tuple ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.count_leaves(__SCREAMING_SNAKE_CASE )
return len(__SCREAMING_SNAKE_CASE ) != leaf_count
class lowerCAmelCase__ ( a ):
"""simple docstring"""
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[List[int]] ) -> int:
"""simple docstring"""
super(__SCREAMING_SNAKE_CASE , self ).__init__()
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) or len(__SCREAMING_SNAKE_CASE ) == 0:
raise ValueError(f'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.' )
if any(not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) for token_ids in nested_token_ids ):
raise ValueError(f'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.' )
if any(
any((not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
f'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.' )
__SCREAMING_SNAKE_CASE = DisjunctiveTrie(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = nested_token_ids
__SCREAMING_SNAKE_CASE = self.trie.max_height
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = False
def UpperCAmelCase__ ( self : Optional[int] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.trie.next_tokens(self.current_seq )
if len(__SCREAMING_SNAKE_CASE ) == 0:
return None
else:
return token_list
def UpperCAmelCase__ ( self : List[Any] , __SCREAMING_SNAKE_CASE : int ) -> Optional[Any]:
"""simple docstring"""
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(__SCREAMING_SNAKE_CASE )}' )
__SCREAMING_SNAKE_CASE = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def UpperCAmelCase__ ( self : str , __SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(__SCREAMING_SNAKE_CASE )}' )
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
if self.does_advance(__SCREAMING_SNAKE_CASE ):
self.current_seq.append(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = True
else:
__SCREAMING_SNAKE_CASE = True
self.reset()
__SCREAMING_SNAKE_CASE = self.trie.reached_leaf(self.current_seq )
__SCREAMING_SNAKE_CASE = completed
return stepped, completed, reset
def UpperCAmelCase__ ( self : Optional[Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = []
def UpperCAmelCase__ ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def UpperCAmelCase__ ( self : int , __SCREAMING_SNAKE_CASE : Optional[Any]=False ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = DisjunctiveConstraint(self.token_ids )
if stateful:
__SCREAMING_SNAKE_CASE = self.seqlen
__SCREAMING_SNAKE_CASE = self.current_seq
__SCREAMING_SNAKE_CASE = self.completed
return new_constraint
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Any , __SCREAMING_SNAKE_CASE : List[Constraint] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = constraints
# max # of steps required to fulfill a given constraint
__SCREAMING_SNAKE_CASE = max([c.seqlen for c in constraints] )
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = False
self.init_state()
def UpperCAmelCase__ ( self : Dict ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = [constraint.copy(stateful=__SCREAMING_SNAKE_CASE ) for constraint in self.constraints]
def UpperCAmelCase__ ( self : int ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def UpperCAmelCase__ ( self : Dict ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
__SCREAMING_SNAKE_CASE = constraint.advance()
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
token_list.append(__SCREAMING_SNAKE_CASE )
elif isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
token_list.extend(__SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE = self.inprogress_constraint.advance()
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
token_list.append(__SCREAMING_SNAKE_CASE )
elif isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
token_list.extend(__SCREAMING_SNAKE_CASE )
if len(__SCREAMING_SNAKE_CASE ) == 0:
return None
else:
return token_list
def UpperCAmelCase__ ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[List[int]] ) -> str:
"""simple docstring"""
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.add(__SCREAMING_SNAKE_CASE )
# the entire list of constraints are fulfilled
if self.completed:
break
def UpperCAmelCase__ ( self : Any , __SCREAMING_SNAKE_CASE : int ) -> Dict:
"""simple docstring"""
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise ValueError(f'`token_id` should be an `int`, but is `{token_id}`.' )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = False, False
if self.completed:
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.inprogress_constraint.update(__SCREAMING_SNAKE_CASE )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
__SCREAMING_SNAKE_CASE = None
if len(self.pending_constraints ) == 0:
# we're done!
__SCREAMING_SNAKE_CASE = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(__SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = pending_constraint.update(__SCREAMING_SNAKE_CASE )
if not stepped:
raise Exception(
"""`constraint.update(token_id)` is not yielding incremental progress, """
"""even though `constraint.does_advance(token_id)` is true.""" )
if complete:
self.complete_constraints.append(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = None
if not complete and stepped:
__SCREAMING_SNAKE_CASE = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
__SCREAMING_SNAKE_CASE = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
__SCREAMING_SNAKE_CASE = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def UpperCAmelCase__ ( self : List[Any] , __SCREAMING_SNAKE_CASE : List[str]=True ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
__SCREAMING_SNAKE_CASE = [
constraint.copy(stateful=__SCREAMING_SNAKE_CASE ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
__SCREAMING_SNAKE_CASE = self.inprogress_constraint.copy(stateful=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 331 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Tuple = {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : List[str] = ['ReformerTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Tuple = ['ReformerTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : List[Any] = [
'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'ReformerAttention',
'ReformerForMaskedLM',
'ReformerForQuestionAnswering',
'ReformerForSequenceClassification',
'ReformerLayer',
'ReformerModel',
'ReformerModelWithLMHead',
'ReformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 331 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _A ( snake_case ) -> str:
_lowercase , _lowercase : Optional[int] = np.shape(lowercase__ )
if rows != columns:
_lowercase : str = (
"\'table\' has to be of square shaped array but got a "
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(lowercase__ )
_lowercase : Optional[Any] = np.zeros((rows, columns) )
_lowercase : int = np.zeros((rows, columns) )
for i in range(lowercase__ ):
for j in range(lowercase__ ):
_lowercase : Any = sum(lower[i][k] * upper[k][j] for k in range(lowercase__ ) )
if upper[j][j] == 0:
raise ArithmeticError("No LU decomposition exists" )
_lowercase : Tuple = (table[i][j] - total) / upper[j][j]
_lowercase : Dict = 1
for j in range(lowercase__ , lowercase__ ):
_lowercase : Union[str, Any] = sum(lower[i][k] * upper[k][j] for k in range(lowercase__ ) )
_lowercase : Tuple = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 250 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class lowerCAmelCase ( A ):
def __init__( self : Optional[Any] , *__lowercase : str , **__lowercase : Union[str, Any] ):
"""simple docstring"""
super().__init__(*__lowercase , **__lowercase )
__lowercase ={}
def snake_case ( self : Union[str, Any] , __lowercase : List[Any] , *__lowercase : Optional[int] , **__lowercase : int ):
"""simple docstring"""
__lowercase =super().add_tokens(__lowercase , *__lowercase , **__lowercase )
if num_added_tokens == 0:
raise ValueError(
f'''The tokenizer already contains the token {placeholder_token}. Please pass a different'''
' `placeholder_token` that is not already in the tokenizer.' )
def snake_case ( self : int , __lowercase : List[Any] , *__lowercase : Union[str, Any] , __lowercase : Dict=1 , **__lowercase : Dict ):
"""simple docstring"""
__lowercase =[]
if num_vec_per_token == 1:
self.try_adding_tokens(__lowercase , *__lowercase , **__lowercase )
output.append(__lowercase )
else:
__lowercase =[]
for i in range(__lowercase ):
__lowercase =placeholder_token + f'''_{i}'''
self.try_adding_tokens(__lowercase , *__lowercase , **__lowercase )
output.append(__lowercase )
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
f'''The tokenizer already has placeholder token {token} that can get confused with'''
f''' {placeholder_token}keep placeholder tokens independent''' )
__lowercase =output
def snake_case ( self : Tuple , __lowercase : Optional[int] , __lowercase : Optional[int]=False , __lowercase : Optional[int]=1.0 ):
"""simple docstring"""
if isinstance(__lowercase , __lowercase ):
__lowercase =[]
for i in range(len(__lowercase ) ):
output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=__lowercase ) )
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
__lowercase =self.token_map[placeholder_token]
__lowercase =tokens[: 1 + int(len(__lowercase ) * prop_tokens_to_load )]
if vector_shuffle:
__lowercase =copy.copy(__lowercase )
random.shuffle(__lowercase )
__lowercase =text.replace(__lowercase , ' '.join(__lowercase ) )
return text
def __call__( self : int , __lowercase : List[Any] , *__lowercase : Tuple , __lowercase : Optional[Any]=False , __lowercase : Dict=1.0 , **__lowercase : List[Any] ):
"""simple docstring"""
return super().__call__(
self.replace_placeholder_tokens_in_text(
__lowercase , vector_shuffle=__lowercase , prop_tokens_to_load=__lowercase ) , *__lowercase , **__lowercase , )
def snake_case ( self : Dict , __lowercase : List[str] , *__lowercase : Tuple , __lowercase : Dict=False , __lowercase : List[str]=1.0 , **__lowercase : Optional[int] ):
"""simple docstring"""
return super().encode(
self.replace_placeholder_tokens_in_text(
__lowercase , vector_shuffle=__lowercase , prop_tokens_to_load=__lowercase ) , *__lowercase , **__lowercase , )
| 141 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def lowerCAmelCase_ (lowerCAmelCase__: List[Any] ):
"""simple docstring"""
UpperCAmelCase_: int = 3_8_4
if "tiny" in model_name:
UpperCAmelCase_: Optional[Any] = [3, 3, 9, 3]
UpperCAmelCase_: Any = [9_6, 1_9_2, 3_8_4, 7_6_8]
if "small" in model_name:
UpperCAmelCase_: Any = [3, 3, 2_7, 3]
UpperCAmelCase_: Dict = [9_6, 1_9_2, 3_8_4, 7_6_8]
if "base" in model_name:
UpperCAmelCase_: int = [3, 3, 2_7, 3]
UpperCAmelCase_: Optional[int] = [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
UpperCAmelCase_: Any = 5_1_2
if "large" in model_name:
UpperCAmelCase_: List[Any] = [3, 3, 2_7, 3]
UpperCAmelCase_: Tuple = [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
UpperCAmelCase_: Optional[int] = 7_6_8
if "xlarge" in model_name:
UpperCAmelCase_: Dict = [3, 3, 2_7, 3]
UpperCAmelCase_: Tuple = [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
UpperCAmelCase_: Dict = 1_0_2_4
# set label information
UpperCAmelCase_: Dict = 1_5_0
UpperCAmelCase_: str = """huggingface/label-files"""
UpperCAmelCase_: int = """ade20k-id2label.json"""
UpperCAmelCase_: int = json.load(open(hf_hub_download(lowerCAmelCase__ , lowerCAmelCase__ , repo_type="""dataset""" ) , """r""" ) )
UpperCAmelCase_: Optional[Any] = {int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
UpperCAmelCase_: Union[str, Any] = {v: k for k, v in idalabel.items()}
UpperCAmelCase_: List[Any] = ConvNextConfig(
depths=lowerCAmelCase__ , hidden_sizes=lowerCAmelCase__ , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
UpperCAmelCase_: Optional[Any] = UperNetConfig(
backbone_config=lowerCAmelCase__ , auxiliary_in_channels=lowerCAmelCase__ , num_labels=lowerCAmelCase__ , idalabel=lowerCAmelCase__ , labelaid=lowerCAmelCase__ , )
return config
def lowerCAmelCase_ (lowerCAmelCase__: Optional[int] ):
"""simple docstring"""
UpperCAmelCase_: Tuple = []
# fmt: off
# stem
rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""") )
rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F'backbone.stages.{i}.{j}.gamma', F'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter') )
rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.weight', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight') )
rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.bias', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias') )
rename_keys.append((F'backbone.stages.{i}.{j}.norm.weight', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight') )
rename_keys.append((F'backbone.stages.{i}.{j}.norm.bias', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias') )
rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight') )
rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias') )
rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight') )
rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias') )
if i > 0:
rename_keys.append((F'backbone.downsample_layers.{i}.0.weight', F'backbone.encoder.stages.{i}.downsampling_layer.0.weight') )
rename_keys.append((F'backbone.downsample_layers.{i}.0.bias', F'backbone.encoder.stages.{i}.downsampling_layer.0.bias') )
rename_keys.append((F'backbone.downsample_layers.{i}.1.weight', F'backbone.encoder.stages.{i}.downsampling_layer.1.weight') )
rename_keys.append((F'backbone.downsample_layers.{i}.1.bias', F'backbone.encoder.stages.{i}.downsampling_layer.1.bias') )
rename_keys.append((F'backbone.norm{i}.weight', F'backbone.hidden_states_norms.stage{i+1}.weight') )
rename_keys.append((F'backbone.norm{i}.bias', F'backbone.hidden_states_norms.stage{i+1}.bias') )
# decode head
rename_keys.extend(
[
("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
] )
# fmt: on
return rename_keys
def lowerCAmelCase_ (lowerCAmelCase__: Dict , lowerCAmelCase__: int , lowerCAmelCase__: int ):
"""simple docstring"""
UpperCAmelCase_: Tuple = dct.pop(lowerCAmelCase__ )
UpperCAmelCase_: int = val
def lowerCAmelCase_ (lowerCAmelCase__: int , lowerCAmelCase__: Optional[Any] , lowerCAmelCase__: int ):
"""simple docstring"""
UpperCAmelCase_: str = {
"""upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
"""upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
"""upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
"""upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
"""upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
}
UpperCAmelCase_: List[str] = model_name_to_url[model_name]
UpperCAmelCase_: Any = torch.hub.load_state_dict_from_url(lowerCAmelCase__ , map_location="""cpu""" )["""state_dict"""]
UpperCAmelCase_: Any = get_upernet_config(lowerCAmelCase__ )
UpperCAmelCase_: int = UperNetForSemanticSegmentation(lowerCAmelCase__ )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
UpperCAmelCase_: Optional[int] = state_dict.pop(lowerCAmelCase__ )
if "bn" in key:
UpperCAmelCase_: str = key.replace("""bn""" , """batch_norm""" )
UpperCAmelCase_: Dict = val
# rename keys
UpperCAmelCase_: Optional[int] = create_rename_keys(lowerCAmelCase__ )
for src, dest in rename_keys:
rename_key(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
model.load_state_dict(lowerCAmelCase__ )
# verify on image
UpperCAmelCase_: List[str] = """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
UpperCAmelCase_: Any = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw ).convert("""RGB""" )
UpperCAmelCase_: Tuple = SegformerImageProcessor()
UpperCAmelCase_: List[Any] = processor(lowerCAmelCase__ , return_tensors="""pt""" ).pixel_values
with torch.no_grad():
UpperCAmelCase_: List[str] = model(lowerCAmelCase__ )
if model_name == "upernet-convnext-tiny":
UpperCAmelCase_: List[Any] = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] )
elif model_name == "upernet-convnext-small":
UpperCAmelCase_: Optional[Any] = torch.tensor(
[[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]] )
elif model_name == "upernet-convnext-base":
UpperCAmelCase_: int = torch.tensor(
[[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]] )
elif model_name == "upernet-convnext-large":
UpperCAmelCase_: Union[str, Any] = torch.tensor(
[[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]] )
elif model_name == "upernet-convnext-xlarge":
UpperCAmelCase_: List[str] = torch.tensor(
[[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]] )
print("""Logits:""" , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , lowerCAmelCase__ , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCAmelCase__ )
print(F'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(lowerCAmelCase__ )
if push_to_hub:
print(F'Pushing model and processor for {model_name} to hub' )
model.push_to_hub(F'openmmlab/{model_name}' )
processor.push_to_hub(F'openmmlab/{model_name}' )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='upernet-convnext-tiny',
type=str,
choices=[F'''upernet-convnext-{size}''' for size in ['tiny', 'small', 'base', 'large', 'xlarge']],
help='Name of the ConvNext UperNet model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
a : str = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 362 |
from __future__ import annotations
def lowerCAmelCase_ (lowerCAmelCase__: list[float] ):
"""simple docstring"""
UpperCAmelCase_: Union[str, Any] = 0.00
UpperCAmelCase_: List[str] = 0
for resistor in resistors:
if resistor <= 0:
UpperCAmelCase_: Dict = F'Resistor at index {index} has a negative or zero value!'
raise ValueError(lowerCAmelCase__ )
first_sum += 1 / float(lowerCAmelCase__ )
index += 1
return 1 / first_sum
def lowerCAmelCase_ (lowerCAmelCase__: list[float] ):
"""simple docstring"""
UpperCAmelCase_: Any = 0.00
UpperCAmelCase_: int = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
UpperCAmelCase_: int = F'Resistor at index {index} has a negative value!'
raise ValueError(lowerCAmelCase__ )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 82 | 0 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class lowerCamelCase__ :
def __A (self , UpperCAmelCase ) -> Dict:
raise NotImplementedError()
def __A (self ) -> Optional[int]:
raise NotImplementedError()
class lowerCamelCase__ ( lowerCAmelCase):
def __init__(self , UpperCAmelCase , UpperCAmelCase = False , **UpperCAmelCase ) -> Any:
_lowercase =tokenizer
_lowercase =skip_prompt
_lowercase =decode_kwargs
# variables used in the streaming process
_lowercase =[]
_lowercase =0
_lowercase =True
def __A (self , UpperCAmelCase ) -> int:
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('''TextStreamer only supports batch size 1''' )
elif len(value.shape ) > 1:
_lowercase =value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
_lowercase =False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
_lowercase =self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('''\n''' ):
_lowercase =text[self.print_len :]
_lowercase =[]
_lowercase =0
# If the last token is a CJK character, we print the characters.
elif len(UpperCAmelCase ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
_lowercase =text[self.print_len :]
self.print_len += len(UpperCAmelCase )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
_lowercase =text[self.print_len : text.rfind(''' ''' ) + 1]
self.print_len += len(UpperCAmelCase )
self.on_finalized_text(UpperCAmelCase )
def __A (self ) -> Any:
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
_lowercase =self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
_lowercase =text[self.print_len :]
_lowercase =[]
_lowercase =0
else:
_lowercase =''''''
_lowercase =True
self.on_finalized_text(UpperCAmelCase , stream_end=UpperCAmelCase )
def __A (self , UpperCAmelCase , UpperCAmelCase = False ) -> Union[str, Any]:
print(UpperCAmelCase , flush=UpperCAmelCase , end='''''' if not stream_end else None )
def __A (self , UpperCAmelCase ) -> Optional[int]:
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4_e_0_0 and cp <= 0X9_f_f_f)
or (cp >= 0X3_4_0_0 and cp <= 0X4_d_b_f) #
or (cp >= 0X2_0_0_0_0 and cp <= 0X2_a_6_d_f) #
or (cp >= 0X2_a_7_0_0 and cp <= 0X2_b_7_3_f) #
or (cp >= 0X2_b_7_4_0 and cp <= 0X2_b_8_1_f) #
or (cp >= 0X2_b_8_2_0 and cp <= 0X2_c_e_a_f) #
or (cp >= 0Xf_9_0_0 and cp <= 0Xf_a_f_f)
or (cp >= 0X2_f_8_0_0 and cp <= 0X2_f_a_1_f) #
): #
return True
return False
class lowerCamelCase__ ( lowerCAmelCase):
def __init__(self , UpperCAmelCase , UpperCAmelCase = False , UpperCAmelCase = None , **UpperCAmelCase ) -> List[str]:
super().__init__(UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
_lowercase =Queue()
_lowercase =None
_lowercase =timeout
def __A (self , UpperCAmelCase , UpperCAmelCase = False ) -> Any:
self.text_queue.put(UpperCAmelCase , timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal , timeout=self.timeout )
def __iter__(self ) -> List[str]:
return self
def __A (self ) -> Dict:
_lowercase =self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 5 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase__ ( nn.Module):
def __init__(self , UpperCAmelCase = 1_6 , UpperCAmelCase = 8_8 , UpperCAmelCase = None , UpperCAmelCase = 1 , UpperCAmelCase = 0.0 , UpperCAmelCase = 3_2 , UpperCAmelCase = None , UpperCAmelCase = False , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = "geglu" , UpperCAmelCase = None , ) -> Any:
super().__init__()
_lowercase =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
_lowercase =0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_lowercase =[7_7, 2_5_7]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_lowercase =[1, 0]
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase = True , ) -> str:
_lowercase =hidden_states
_lowercase =[]
_lowercase =0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_lowercase =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_lowercase =self.transformer_index_for_condition[i]
_lowercase =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]
_lowercase =encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_lowercase =output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=UpperCAmelCase )
| 5 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase ( snake_case__ , unittest.TestCase):
"""simple docstring"""
a__ : Union[str, Any] = BlenderbotSmallTokenizer
a__ : Union[str, Any] = False
def _SCREAMING_SNAKE_CASE ( self : int ) -> int:
super().setUp()
UpperCAmelCase_= ["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
UpperCAmelCase_= dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) )
UpperCAmelCase_= ["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
UpperCAmelCase_= {"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}
UpperCAmelCase_= os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase_= os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(__UpperCAmelCase ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(__UpperCAmelCase ) )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , **__UpperCAmelCase : Optional[Any] ) -> str:
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , __UpperCAmelCase : Any ) -> Union[str, Any]:
UpperCAmelCase_= """adapt act apte"""
UpperCAmelCase_= """adapt act apte"""
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Any:
UpperCAmelCase_= BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
UpperCAmelCase_= """adapt act apte"""
UpperCAmelCase_= ["""adapt""", """act""", """ap@@""", """te"""]
UpperCAmelCase_= tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase_= [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
UpperCAmelCase_= [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[Any]:
UpperCAmelCase_= BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
assert tok("""sam""" ).input_ids == [1_384]
UpperCAmelCase_= """I am a small frog."""
UpperCAmelCase_= tok([src_text] , padding=__UpperCAmelCase , truncation=__UpperCAmelCase )["""input_ids"""]
UpperCAmelCase_= tok.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Any:
UpperCAmelCase_= BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
UpperCAmelCase_= """I am a small frog ."""
UpperCAmelCase_= """."""
UpperCAmelCase_= tok(__UpperCAmelCase )["""input_ids"""]
UpperCAmelCase_= tok(__UpperCAmelCase )["""input_ids"""]
assert encoded[-1] == encoded_dot[0]
| 277 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment
def __a ( ) -> str:
'''simple docstring'''
UpperCAmelCase_= torch.nn.Linear(2 ,4 )
UpperCAmelCase_= torch.optim.AdamW(model.parameters() ,lr=1.0 )
UpperCAmelCase_= torch.optim.lr_scheduler.OneCycleLR(lowerCAmelCase_ ,max_lr=0.01 ,steps_per_epoch=2 ,epochs=1 )
UpperCAmelCase_= DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
UpperCAmelCase_= DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )
return model, optimizer, scheduler, train_dl, valid_dl
def __a ( lowerCAmelCase_ : Any ) -> Union[str, Any]:
'''simple docstring'''
return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def __a ( lowerCAmelCase_ : Tuple ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_= torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
model.load_state_dict(lowerCAmelCase_ )
class lowercase ( snake_case__):
"""simple docstring"""
@require_cuda
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[Any]:
UpperCAmelCase_= Accelerator()
assert PartialState._shared_state["_cpu"] is False
assert PartialState._shared_state["device"].type == "cuda"
with self.assertRaises(__UpperCAmelCase ):
UpperCAmelCase_= Accelerator(cpu=__UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> Union[str, Any]:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_= GradientState()
assert state.num_steps == 1
UpperCAmelCase_= 4
assert state.num_steps == 4
assert state.sync_gradients is True
UpperCAmelCase_= False
assert state.sync_gradients is False
GradientState._reset_state()
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> List[str]:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= create_components()
(
(
UpperCAmelCase_
), (
UpperCAmelCase_
), (
UpperCAmelCase_
), (
UpperCAmelCase_
), (
UpperCAmelCase_
),
)= accelerator.prepare(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
self.assertTrue(prepared_model in accelerator._models )
self.assertTrue(prepared_optimizer in accelerator._optimizers )
self.assertTrue(prepared_scheduler in accelerator._schedulers )
self.assertTrue(prepared_train_dl in accelerator._dataloaders )
self.assertTrue(prepared_valid_dl in accelerator._dataloaders )
def _SCREAMING_SNAKE_CASE ( self : int ) -> Dict:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= create_components()
accelerator.prepare(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
accelerator.free_memory()
self.assertTrue(len(accelerator._models ) == 0 )
self.assertTrue(len(accelerator._optimizers ) == 0 )
self.assertTrue(len(accelerator._schedulers ) == 0 )
self.assertTrue(len(accelerator._dataloaders ) == 0 )
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Optional[Any]:
PartialState._reset_state()
# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
def noop(*__UpperCAmelCase : Dict , **__UpperCAmelCase : Tuple ):
pass
with patch("""torch.cuda.set_device""" , __UpperCAmelCase ), patch_environment(ACCELERATE_TORCH_DEVICE="""cuda:64""" ):
UpperCAmelCase_= Accelerator()
self.assertEqual(str(accelerator.state.device ) , """cuda:64""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= create_components()
accelerator.prepare(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase_= get_signature(__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__UpperCAmelCase )
# make sure random weights don't match
load_random_weights(__UpperCAmelCase )
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase ) ) > 1E-3 )
# make sure loaded weights match
accelerator.load_state(__UpperCAmelCase )
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase ) ) < 1E-3 )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= create_components()
accelerator.prepare(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase_= get_signature(__UpperCAmelCase )
# saving hook
def save_config(__UpperCAmelCase : Tuple , __UpperCAmelCase : List[str] , __UpperCAmelCase : Tuple ):
UpperCAmelCase_= {"""class_name""": models[0].__class__.__name__}
with open(os.path.join(__UpperCAmelCase , """data.json""" ) , """w""" ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
# loading hook
def load_config(__UpperCAmelCase : Tuple , __UpperCAmelCase : Union[str, Any] ):
with open(os.path.join(__UpperCAmelCase , """data.json""" ) , """r""" ) as f:
UpperCAmelCase_= json.load(__UpperCAmelCase )
UpperCAmelCase_= config["""class_name"""]
UpperCAmelCase_= accelerator.register_save_state_pre_hook(__UpperCAmelCase )
UpperCAmelCase_= accelerator.register_load_state_pre_hook(__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__UpperCAmelCase )
# make sure random weights don't match with hooks
load_random_weights(__UpperCAmelCase )
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase ) ) > 1E-3 )
# random class name to verify correct one is loaded
UpperCAmelCase_= """random"""
# make sure loaded weights match with hooks
accelerator.load_state(__UpperCAmelCase )
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase ) ) < 1E-3 )
# mode.class_name is loaded from config
self.assertTrue(model.class_name == model.__class__.__name__ )
# remove hooks
save_hook.remove()
load_hook.remove()
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__UpperCAmelCase )
# make sure random weights don't match with hooks removed
load_random_weights(__UpperCAmelCase )
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase ) ) > 1E-3 )
# random class name to verify correct one is loaded
UpperCAmelCase_= """random"""
# make sure loaded weights match with hooks removed
accelerator.load_state(__UpperCAmelCase )
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase ) ) < 1E-3 )
# mode.class_name is NOT loaded from config
self.assertTrue(model.class_name != model.__class__.__name__ )
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Union[str, Any]:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= create_components()
UpperCAmelCase_= None
# This should work
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= accelerator.prepare(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
self.assertTrue(dummy_obj is None )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Any:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= create_components()
UpperCAmelCase_= [1, 2, 3]
# This should work
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= accelerator.prepare(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
self.assertEqual(
getattr(__UpperCAmelCase , """_is_accelerate_prepared""" , __UpperCAmelCase ) , __UpperCAmelCase , """Dummy object should have `_is_accelerate_prepared` set to `True`""" , )
self.assertEqual(
getattr(__UpperCAmelCase , """_is_accelerate_prepared""" , __UpperCAmelCase ) , __UpperCAmelCase , """Model is missing `_is_accelerator_prepared` or is set to `False`""" , )
self.assertEqual(
getattr(__UpperCAmelCase , """_is_accelerate_prepared""" , __UpperCAmelCase ) , __UpperCAmelCase , """Optimizer is missing `_is_accelerator_prepared` or is set to `False`""" , )
self.assertEqual(
getattr(__UpperCAmelCase , """_is_accelerate_prepared""" , __UpperCAmelCase ) , __UpperCAmelCase , """Scheduler is missing `_is_accelerator_prepared` or is set to `False`""" , )
self.assertEqual(
getattr(__UpperCAmelCase , """_is_accelerate_prepared""" , __UpperCAmelCase ) , __UpperCAmelCase , """Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`""" , )
self.assertEqual(
getattr(__UpperCAmelCase , """_is_accelerate_prepared""" , __UpperCAmelCase ) , __UpperCAmelCase , """Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`""" , )
@slow
@require_bnb
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[Any]:
from transformers import AutoModelForCausalLM
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , load_in_abit=__UpperCAmelCase , device_map={"""""": 0} , )
UpperCAmelCase_= Accelerator()
# This should work
UpperCAmelCase_= accelerator.prepare(__UpperCAmelCase )
@slow
@require_bnb
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Tuple:
from transformers import AutoModelForCausalLM
UpperCAmelCase_= Accelerator()
with init_empty_weights():
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , )
model.tie_weights()
UpperCAmelCase_= infer_auto_device_map(__UpperCAmelCase )
UpperCAmelCase_= """cpu"""
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , device_map=__UpperCAmelCase , load_in_abit=__UpperCAmelCase , llm_inta_enable_fpaa_cpu_offload=__UpperCAmelCase )
# This should not work and get value error
with self.assertRaises(__UpperCAmelCase ):
UpperCAmelCase_= accelerator.prepare(__UpperCAmelCase )
@slow
@require_bnb
@require_multi_gpu
def _SCREAMING_SNAKE_CASE ( self : int ) -> Tuple:
from transformers import AutoModelForCausalLM
UpperCAmelCase_= {"""distributed_type""": DistributedType.MULTI_GPU}
with init_empty_weights():
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , )
model.tie_weights()
UpperCAmelCase_= infer_auto_device_map(__UpperCAmelCase )
UpperCAmelCase_= 1
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , load_in_abit=__UpperCAmelCase , device_map=__UpperCAmelCase , )
UpperCAmelCase_= Accelerator()
# This should not work and get value error
with self.assertRaises(__UpperCAmelCase ):
UpperCAmelCase_= accelerator.prepare(__UpperCAmelCase )
PartialState._reset_state()
@slow
@require_bnb
@require_multi_gpu
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Dict:
from transformers import AutoModelForCausalLM
with init_empty_weights():
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , )
UpperCAmelCase_= infer_auto_device_map(__UpperCAmelCase )
UpperCAmelCase_= 1
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , load_in_abit=__UpperCAmelCase , device_map=__UpperCAmelCase , )
UpperCAmelCase_= Accelerator()
# This should work
UpperCAmelCase_= accelerator.prepare(__UpperCAmelCase )
@require_cuda
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Optional[int]:
UpperCAmelCase_= torch.nn.Linear(10 , 10 )
UpperCAmelCase_= torch.optim.SGD(model.parameters() , lr=0.01 )
UpperCAmelCase_= Accelerator(cpu=__UpperCAmelCase )
UpperCAmelCase_= accelerator.prepare(__UpperCAmelCase )
| 277 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
lowercase : Optional[int] = (7_20, 12_80) # Height, Width
lowercase : List[str] = (0.4, 0.6) # if height or width lower than this scale, drop it.
lowercase : List[Any] = 1 / 1_00
lowercase : Optional[Any] = ''
lowercase : Dict = ''
lowercase : str = ''
lowercase : str = 2_50
def lowerCAmelCase_ ( ):
'''simple docstring'''
A, A : Dict = get_dataset(snake_case__ , snake_case__ )
for index in range(snake_case__ ):
A : Tuple = random.sample(range(len(snake_case__ ) ) , 4 )
A, A, A : List[str] = update_image_and_anno(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , filter_scale=snake_case__ , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
A : Union[str, Any] = random_chars(32 )
A : Any = path.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
A : Union[str, Any] = F'{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'
cva.imwrite(F'{file_root}.jpg' , snake_case__ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F'Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}' )
A : Dict = []
for anno in new_annos:
A : Optional[int] = anno[3] - anno[1]
A : int = anno[4] - anno[2]
A : str = anno[1] + width / 2
A : List[str] = anno[2] + height / 2
A : Dict = F'{anno[0]} {x_center} {y_center} {width} {height}'
annos_list.append(snake_case__ )
with open(F'{file_root}.txt' , '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
A : Dict = []
A : Dict = []
for label_file in glob.glob(os.path.join(snake_case__ , '''*.txt''' ) ):
A : List[str] = label_file.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
with open(snake_case__ ) as in_file:
A : Optional[Any] = in_file.readlines()
A : Optional[Any] = os.path.join(snake_case__ , F'{label_name}.jpg' )
A : Tuple = []
for obj_list in obj_lists:
A : List[Any] = obj_list.rstrip('''\n''' ).split(''' ''' )
A : str = float(obj[1] ) - float(obj[3] ) / 2
A : List[Any] = float(obj[2] ) - float(obj[4] ) / 2
A : int = float(obj[1] ) + float(obj[3] ) / 2
A : Optional[Any] = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(snake_case__ )
labels.append(snake_case__ )
return img_paths, labels
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ = 0.0 , ):
'''simple docstring'''
A : List[str] = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
A : List[Any] = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
A : Optional[Any] = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
A : Optional[Any] = int(scale_x * output_size[1] )
A : Union[str, Any] = int(scale_y * output_size[0] )
A : Union[str, Any] = []
A : Union[str, Any] = []
for i, index in enumerate(snake_case__ ):
A : List[Any] = all_img_list[index]
path_list.append(snake_case__ )
A : Tuple = all_annos[index]
A : Optional[Any] = cva.imread(snake_case__ )
if i == 0: # top-left
A : Optional[Any] = cva.resize(snake_case__ , (divid_point_x, divid_point_y) )
A : Optional[Any] = img
for bbox in img_annos:
A : Union[str, Any] = bbox[1] * scale_x
A : Optional[Any] = bbox[2] * scale_y
A : int = bbox[3] * scale_x
A : Optional[int] = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
A : Any = cva.resize(snake_case__ , (output_size[1] - divid_point_x, divid_point_y) )
A : Optional[Any] = img
for bbox in img_annos:
A : Optional[Any] = scale_x + bbox[1] * (1 - scale_x)
A : Tuple = bbox[2] * scale_y
A : Any = scale_x + bbox[3] * (1 - scale_x)
A : List[str] = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
A : Optional[Any] = cva.resize(snake_case__ , (divid_point_x, output_size[0] - divid_point_y) )
A : Any = img
for bbox in img_annos:
A : Optional[Any] = bbox[1] * scale_x
A : List[str] = scale_y + bbox[2] * (1 - scale_y)
A : Optional[Any] = bbox[3] * scale_x
A : Any = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
A : List[str] = cva.resize(
snake_case__ , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
A : str = img
for bbox in img_annos:
A : Dict = scale_x + bbox[1] * (1 - scale_x)
A : int = scale_y + bbox[2] * (1 - scale_y)
A : List[Any] = scale_x + bbox[3] * (1 - scale_x)
A : Any = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
A : List[str] = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
A : int = ascii_lowercase + digits
return "".join(random.choice(snake_case__ ) for _ in range(snake_case__ ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 3 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowercase : Dict = {
'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
'processing_speech_to_text': ['Speech2TextProcessor'],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : List[Any] = ['Speech2TextTokenizer']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : List[str] = ['Speech2TextFeatureExtractor']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Dict = [
'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFSpeech2TextForConditionalGeneration',
'TFSpeech2TextModel',
'TFSpeech2TextPreTrainedModel',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Any = [
'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Speech2TextForConditionalGeneration',
'Speech2TextModel',
'Speech2TextPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowercase : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 3 | 1 |
'''simple docstring'''
from torch import nn
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : List[Any] ) -> int:
super().__init__()
UpperCAmelCase : Optional[int] = class_size
UpperCAmelCase : Optional[int] = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.mlp2 = (nn.Linear(embed_size, class_size))
UpperCAmelCase : Dict = nn.Linear(lowercase_ , lowercase_ )
def UpperCAmelCase_ ( self : List[Any] , lowercase_ : Any ) -> Union[str, Any]:
# hidden_state = nn.functional.relu(self.mlp1(hidden_state))
# hidden_state = self.mlp2(hidden_state)
UpperCAmelCase : Optional[int] = self.mlp(lowercase_ )
return logits
| 280 |
'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowercase__ = logging.get_logger(__name__)
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Optional[Any] = b.T
UpperCAmelCase : Optional[int] = np.sum(np.square(UpperCAmelCase_ ) , axis=1 )
UpperCAmelCase : List[Any] = np.sum(np.square(UpperCAmelCase_ ) , axis=0 )
UpperCAmelCase : List[str] = np.matmul(UpperCAmelCase_ , UpperCAmelCase_ )
UpperCAmelCase : Union[str, Any] = aa[:, None] - 2 * ab + ba[None, :]
return d
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : int = x.reshape(-1 , 3 )
UpperCAmelCase : Optional[int] = squared_euclidean_distance(UpperCAmelCase_ , UpperCAmelCase_ )
return np.argmin(UpperCAmelCase_ , axis=1 )
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = ["""pixel_values"""]
def __init__( self : List[Any] , lowercase_ : Optional[Union[List[List[int]], np.ndarray]] = None , lowercase_ : bool = True , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = PILImageResampling.BILINEAR , lowercase_ : bool = True , lowercase_ : bool = True , **lowercase_ : Optional[Any] , ) -> None:
super().__init__(**lowercase_ )
UpperCAmelCase : Any = size if size is not None else {'height': 256, 'width': 256}
UpperCAmelCase : List[Any] = get_size_dict(lowercase_ )
UpperCAmelCase : str = np.array(lowercase_ ) if clusters is not None else None
UpperCAmelCase : Any = do_resize
UpperCAmelCase : List[Any] = size
UpperCAmelCase : Any = resample
UpperCAmelCase : Dict = do_normalize
UpperCAmelCase : List[Any] = do_color_quantize
def UpperCAmelCase_ ( self : int , lowercase_ : np.ndarray , lowercase_ : Dict[str, int] , lowercase_ : PILImageResampling = PILImageResampling.BILINEAR , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : Any , ) -> np.ndarray:
UpperCAmelCase : Dict = get_size_dict(lowercase_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}""" )
return resize(
lowercase_ , size=(size['height'], size['width']) , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
def UpperCAmelCase_ ( self : Optional[int] , lowercase_ : np.ndarray , lowercase_ : Optional[Union[str, ChannelDimension]] = None , ) -> np.ndarray:
UpperCAmelCase : int = rescale(image=lowercase_ , scale=1 / 127.5 , data_format=lowercase_ )
UpperCAmelCase : Dict = image - 1
return image
def UpperCAmelCase_ ( self : str , lowercase_ : ImageInput , lowercase_ : bool = None , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = None , lowercase_ : bool = None , lowercase_ : Optional[bool] = None , lowercase_ : Optional[Union[List[List[int]], np.ndarray]] = None , lowercase_ : Optional[Union[str, TensorType]] = None , lowercase_ : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST , **lowercase_ : List[str] , ) -> PIL.Image.Image:
UpperCAmelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase : Optional[Any] = size if size is not None else self.size
UpperCAmelCase : Optional[int] = get_size_dict(lowercase_ )
UpperCAmelCase : Any = resample if resample is not None else self.resample
UpperCAmelCase : Optional[Any] = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase : str = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
UpperCAmelCase : Optional[int] = clusters if clusters is not None else self.clusters
UpperCAmelCase : List[str] = np.array(lowercase_ )
UpperCAmelCase : int = make_list_of_images(lowercase_ )
if not valid_images(lowercase_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_color_quantize and clusters is None:
raise ValueError('Clusters must be specified if do_color_quantize is True.' )
# All transformations expect numpy arrays.
UpperCAmelCase : Dict = [to_numpy_array(lowercase_ ) for image in images]
if do_resize:
UpperCAmelCase : List[Any] = [self.resize(image=lowercase_ , size=lowercase_ , resample=lowercase_ ) for image in images]
if do_normalize:
UpperCAmelCase : Tuple = [self.normalize(image=lowercase_ ) for image in images]
if do_color_quantize:
UpperCAmelCase : List[str] = [to_channel_dimension_format(lowercase_ , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
UpperCAmelCase : int = np.array(lowercase_ )
UpperCAmelCase : str = color_quantize(lowercase_ , lowercase_ ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
UpperCAmelCase : Optional[int] = images.shape[0]
UpperCAmelCase : Union[str, Any] = images.reshape(lowercase_ , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
UpperCAmelCase : int = list(lowercase_ )
else:
UpperCAmelCase : Dict = [to_channel_dimension_format(lowercase_ , lowercase_ ) for image in images]
UpperCAmelCase : Any = {'input_ids': images}
return BatchFeature(data=lowercase_ , tensor_type=lowercase_ )
| 280 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ) -> List[Any]:
lowerCamelCase : Any = "ZinengTang/tvlt-base"
lowerCamelCase : Dict = tempfile.mkdtemp()
def _lowercase ( self , **UpperCamelCase__ ) -> int:
return TvltImageProcessor.from_pretrained(self.checkpoint , **UpperCAmelCase_ )
def _lowercase ( self , **UpperCamelCase__ ) -> Any:
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **UpperCAmelCase_ )
def _lowercase ( self ) -> Any:
shutil.rmtree(self.tmpdirname )
def _lowercase ( self ) -> List[Any]:
lowerCamelCase : Union[str, Any] = self.get_image_processor()
lowerCamelCase : Any = self.get_feature_extractor()
lowerCamelCase : Tuple = TvltProcessor(image_processor=UpperCAmelCase_ , feature_extractor=UpperCAmelCase_ )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase : Any = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , UpperCAmelCase_ )
self.assertIsInstance(processor.image_processor , UpperCAmelCase_ )
def _lowercase ( self ) -> Dict:
lowerCamelCase : Union[str, Any] = self.get_image_processor()
lowerCamelCase : Tuple = self.get_feature_extractor()
lowerCamelCase : int = TvltProcessor(image_processor=UpperCAmelCase_ , feature_extractor=UpperCAmelCase_ )
lowerCamelCase : List[Any] = np.ones([1_2000] )
lowerCamelCase : Optional[Any] = feature_extractor(UpperCAmelCase_ , return_tensors="np" )
lowerCamelCase : Dict = processor(audio=UpperCAmelCase_ , return_tensors="np" )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _lowercase ( self ) -> List[Any]:
lowerCamelCase : Dict = self.get_image_processor()
lowerCamelCase : List[Any] = self.get_feature_extractor()
lowerCamelCase : Any = TvltProcessor(image_processor=UpperCAmelCase_ , feature_extractor=UpperCAmelCase_ )
lowerCamelCase : str = np.ones([3, 224, 224] )
lowerCamelCase : Optional[int] = image_processor(UpperCAmelCase_ , return_tensors="np" )
lowerCamelCase : Union[str, Any] = processor(images=UpperCAmelCase_ , return_tensors="np" )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _lowercase ( self ) -> Any:
lowerCamelCase : Dict = self.get_image_processor()
lowerCamelCase : Union[str, Any] = self.get_feature_extractor()
lowerCamelCase : Union[str, Any] = TvltProcessor(image_processor=UpperCAmelCase_ , feature_extractor=UpperCAmelCase_ )
lowerCamelCase : Any = np.ones([1_2000] )
lowerCamelCase : Any = np.ones([3, 224, 224] )
lowerCamelCase : Union[str, Any] = processor(audio=UpperCAmelCase_ , images=UpperCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) , ["audio_values", "audio_mask", "pixel_values", "pixel_mask"] )
# test if it raises when no input is passed
with pytest.raises(UpperCAmelCase_ ):
processor()
def _lowercase ( self ) -> Tuple:
lowerCamelCase : Dict = self.get_image_processor()
lowerCamelCase : Any = self.get_feature_extractor()
lowerCamelCase : Tuple = TvltProcessor(image_processor=UpperCAmelCase_ , feature_extractor=UpperCAmelCase_ )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg="`processor` and `image_processor`+`feature_extractor` model input names do not match" , )
| 48 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"microsoft/unispeech-large-1500h-cv": (
"https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "unispeech"
def __init__(self : Any , UpperCAmelCase_ : Any=32 , UpperCAmelCase_ : List[str]=768 , UpperCAmelCase_ : Any=12 , UpperCAmelCase_ : Union[str, Any]=12 , UpperCAmelCase_ : Optional[Any]=3_072 , UpperCAmelCase_ : List[Any]="gelu" , UpperCAmelCase_ : int=0.1 , UpperCAmelCase_ : Optional[int]=0.1 , UpperCAmelCase_ : int=0.1 , UpperCAmelCase_ : Any=0.0 , UpperCAmelCase_ : str=0.0 , UpperCAmelCase_ : Dict=0.1 , UpperCAmelCase_ : Optional[int]=0.1 , UpperCAmelCase_ : Optional[Any]=0.02 , UpperCAmelCase_ : Union[str, Any]=1E-5 , UpperCAmelCase_ : str="group" , UpperCAmelCase_ : List[Any]="gelu" , UpperCAmelCase_ : Tuple=(512, 512, 512, 512, 512, 512, 512) , UpperCAmelCase_ : str=(5, 2, 2, 2, 2, 2, 2) , UpperCAmelCase_ : Any=(10, 3, 3, 3, 3, 2, 2) , UpperCAmelCase_ : Optional[Any]=False , UpperCAmelCase_ : str=128 , UpperCAmelCase_ : int=16 , UpperCAmelCase_ : Dict=False , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : Dict=0.05 , UpperCAmelCase_ : Optional[int]=10 , UpperCAmelCase_ : Tuple=2 , UpperCAmelCase_ : Union[str, Any]=0.0 , UpperCAmelCase_ : int=10 , UpperCAmelCase_ : List[Any]=0 , UpperCAmelCase_ : Optional[Any]=320 , UpperCAmelCase_ : int=2 , UpperCAmelCase_ : Union[str, Any]=0.1 , UpperCAmelCase_ : str=100 , UpperCAmelCase_ : Any=256 , UpperCAmelCase_ : int=256 , UpperCAmelCase_ : Optional[Any]=0.1 , UpperCAmelCase_ : str="mean" , UpperCAmelCase_ : Union[str, Any]=False , UpperCAmelCase_ : List[str]=False , UpperCAmelCase_ : List[Any]=256 , UpperCAmelCase_ : Optional[int]=80 , UpperCAmelCase_ : Optional[int]=0 , UpperCAmelCase_ : Optional[Any]=1 , UpperCAmelCase_ : Union[str, Any]=2 , UpperCAmelCase_ : Dict=0.5 , **UpperCAmelCase_ : Optional[int] , ) ->str:
'''simple docstring'''
super().__init__(**UpperCAmelCase_ , pad_token_id=UpperCAmelCase_ , bos_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =hidden_size
lowerCamelCase__: List[str] =feat_extract_norm
lowerCamelCase__: Dict =feat_extract_activation
lowerCamelCase__: Optional[Any] =list(UpperCAmelCase_)
lowerCamelCase__: Any =list(UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =list(UpperCAmelCase_)
lowerCamelCase__: Dict =conv_bias
lowerCamelCase__: Optional[Any] =num_conv_pos_embeddings
lowerCamelCase__: Dict =num_conv_pos_embedding_groups
lowerCamelCase__: int =len(self.conv_dim)
lowerCamelCase__: Union[str, Any] =num_hidden_layers
lowerCamelCase__: Union[str, Any] =intermediate_size
lowerCamelCase__: Dict =hidden_act
lowerCamelCase__: List[Any] =num_attention_heads
lowerCamelCase__: Dict =hidden_dropout
lowerCamelCase__: Optional[Any] =attention_dropout
lowerCamelCase__: Optional[Any] =activation_dropout
lowerCamelCase__: Tuple =feat_proj_dropout
lowerCamelCase__: int =final_dropout
lowerCamelCase__: Optional[Any] =layerdrop
lowerCamelCase__: Dict =layer_norm_eps
lowerCamelCase__: Optional[Any] =initializer_range
lowerCamelCase__: int =num_ctc_classes
lowerCamelCase__: Tuple =vocab_size
lowerCamelCase__: Dict =do_stable_layer_norm
lowerCamelCase__: List[Any] =use_weighted_layer_sum
lowerCamelCase__: Dict =classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
F""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
F""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCamelCase__: int =apply_spec_augment
lowerCamelCase__: List[str] =mask_time_prob
lowerCamelCase__: Union[str, Any] =mask_time_length
lowerCamelCase__: List[Any] =mask_time_min_masks
lowerCamelCase__: Any =mask_feature_prob
lowerCamelCase__: Optional[Any] =mask_feature_length
lowerCamelCase__: List[str] =mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
lowerCamelCase__: Optional[Any] =num_codevectors_per_group
lowerCamelCase__: str =num_codevector_groups
lowerCamelCase__: Tuple =contrastive_logits_temperature
lowerCamelCase__: int =feat_quantizer_dropout
lowerCamelCase__: Any =num_negatives
lowerCamelCase__: List[str] =codevector_dim
lowerCamelCase__: Union[str, Any] =proj_codevector_dim
lowerCamelCase__: Any =diversity_loss_weight
# ctc loss
lowerCamelCase__: Any =ctc_loss_reduction
lowerCamelCase__: Dict =ctc_zero_infinity
# pretraining loss
lowerCamelCase__: Dict =replace_prob
@property
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->Optional[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1)
| 10 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
# See all BART models at https://huggingface.co/models?filter=bart
__UpperCAmelCase = {
'''vocab_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
},
'''merges_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
},
}
__UpperCAmelCase = {
'''facebook/bart-base''': 1_024,
'''facebook/bart-large''': 1_024,
'''facebook/bart-large-mnli''': 1_024,
'''facebook/bart-large-cnn''': 1_024,
'''facebook/bart-large-xsum''': 1_024,
'''yjernite/bart_eli5''': 1_024,
}
class a__ ( snake_case_ ):
'''simple docstring'''
lowercase__ : List[str] = VOCAB_FILES_NAMES
lowercase__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : Union[str, Any] = ["input_ids", "attention_mask"]
lowercase__ : Any = BartTokenizer
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_="replace" , lowerCamelCase_="<s>" , lowerCamelCase_="</s>" , lowerCamelCase_="</s>" , lowerCamelCase_="<s>" , lowerCamelCase_="<unk>" , lowerCamelCase_="<pad>" , lowerCamelCase_="<mask>" , lowerCamelCase_=False , lowerCamelCase_=True , **lowerCamelCase_ , ) -> Dict:
super().__init__(
_A , _A , tokenizer_file=_A , errors=_A , bos_token=_A , eos_token=_A , sep_token=_A , cls_token=_A , unk_token=_A , pad_token=_A , mask_token=_A , add_prefix_space=_A , trim_offsets=_A , **_A , )
lowerCAmelCase__ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , _A ) != add_prefix_space:
lowerCAmelCase__ = getattr(_A , pre_tok_state.pop('''type''' ) )
lowerCAmelCase__ = add_prefix_space
lowerCAmelCase__ = pre_tok_class(**_A )
lowerCAmelCase__ = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowerCAmelCase__ = 'post_processor'
lowerCAmelCase__ = getattr(self.backend_tokenizer , _A , _A )
if tokenizer_component_instance:
lowerCAmelCase__ = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
lowerCAmelCase__ = tuple(state['''sep'''] )
if "cls" in state:
lowerCAmelCase__ = tuple(state['''cls'''] )
lowerCAmelCase__ = False
if state.get('''add_prefix_space''' , _A ) != add_prefix_space:
lowerCAmelCase__ = add_prefix_space
lowerCAmelCase__ = True
if state.get('''trim_offsets''' , _A ) != trim_offsets:
lowerCAmelCase__ = trim_offsets
lowerCAmelCase__ = True
if changes_to_apply:
lowerCAmelCase__ = getattr(_A , state.pop('''type''' ) )
lowerCAmelCase__ = component_class(**_A )
setattr(self.backend_tokenizer , _A , _A )
@property
def __SCREAMING_SNAKE_CASE ( self ) -> str:
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> Tuple:
lowerCAmelCase__ = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else value
lowerCAmelCase__ = value
def __SCREAMING_SNAKE_CASE ( self , *lowerCamelCase_ , **lowerCamelCase_ ) -> BatchEncoding:
lowerCAmelCase__ = kwargs.get('''is_split_into_words''' , _A )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'''to use it with pretokenized inputs.''' )
return super()._batch_encode_plus(*_A , **_A )
def __SCREAMING_SNAKE_CASE ( self , *lowerCamelCase_ , **lowerCamelCase_ ) -> BatchEncoding:
lowerCAmelCase__ = kwargs.get('''is_split_into_words''' , _A )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'''to use it with pretokenized inputs.''' )
return super()._encode_plus(*_A , **_A )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None ) -> Tuple[str]:
lowerCAmelCase__ = self._tokenizer.model.save(_A , name=_A )
return tuple(_A )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_=None ) -> Optional[Any]:
lowerCAmelCase__ = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None ) -> List[int]:
lowerCAmelCase__ = [self.sep_token_id]
lowerCAmelCase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] | 350 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( A , A ) -> float:
lowerCAmelCase__ = sorted(numsa + numsa )
lowerCAmelCase__ , lowerCAmelCase__ = divmod(len(A ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase = [float(x) for x in input('''Enter the elements of first array: ''').split()]
__UpperCAmelCase = [float(x) for x in input('''Enter the elements of second array: ''').split()]
print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""") | 228 | 0 |
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
lowercase_ = (
'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '
'library. You can have a look at this example script for pointers: '
'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'
)
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> Tuple:
'''simple docstring'''
warnings.warn(__snake_case , __snake_case )
requires_backends(__snake_case , 'sklearn' )
return (preds == labels).mean()
def _snake_case( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
warnings.warn(__snake_case , __snake_case )
requires_backends(__snake_case , 'sklearn' )
A__ = simple_accuracy(__snake_case , __snake_case )
A__ = fa_score(y_true=__snake_case , y_pred=__snake_case )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def _snake_case( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> str:
'''simple docstring'''
warnings.warn(__snake_case , __snake_case )
requires_backends(__snake_case , 'sklearn' )
A__ = pearsonr(__snake_case , __snake_case )[0]
A__ = spearmanr(__snake_case , __snake_case )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def _snake_case( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Dict ) -> List[Any]:
'''simple docstring'''
warnings.warn(__snake_case , __snake_case )
requires_backends(__snake_case , 'sklearn' )
assert len(__snake_case ) == len(__snake_case ), f'Predictions and labels have mismatched lengths {len(__snake_case )} and {len(__snake_case )}'
if task_name == "cola":
return {"mcc": matthews_corrcoef(__snake_case , __snake_case )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(__snake_case , __snake_case )}
elif task_name == "mrpc":
return acc_and_fa(__snake_case , __snake_case )
elif task_name == "sts-b":
return pearson_and_spearman(__snake_case , __snake_case )
elif task_name == "qqp":
return acc_and_fa(__snake_case , __snake_case )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(__snake_case , __snake_case )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(__snake_case , __snake_case )}
elif task_name == "qnli":
return {"acc": simple_accuracy(__snake_case , __snake_case )}
elif task_name == "rte":
return {"acc": simple_accuracy(__snake_case , __snake_case )}
elif task_name == "wnli":
return {"acc": simple_accuracy(__snake_case , __snake_case )}
elif task_name == "hans":
return {"acc": simple_accuracy(__snake_case , __snake_case )}
else:
raise KeyError(__snake_case )
def _snake_case( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[str] ) -> List[Any]:
'''simple docstring'''
warnings.warn(__snake_case , __snake_case )
requires_backends(__snake_case , 'sklearn' )
if len(__snake_case ) != len(__snake_case ):
raise ValueError(f'Predictions and labels have mismatched lengths {len(__snake_case )} and {len(__snake_case )}' )
if task_name == "xnli":
return {"acc": simple_accuracy(__snake_case , __snake_case )}
else:
raise KeyError(__snake_case )
| 7 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case : Union[str, Any] = logging.get_logger(__name__)
__snake_case : List[str] = {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
'umberto-commoncrawl-cased-v1': (
'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
),
'umberto-wikipedia-uncased-v1': (
'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
),
}
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
__snake_case = 'camembert'
def __init__( self : int , lowerCAmelCase_ : Tuple=3_05_22 , lowerCAmelCase_ : Tuple=7_68 , lowerCAmelCase_ : List[str]=12 , lowerCAmelCase_ : Any=12 , lowerCAmelCase_ : Tuple=30_72 , lowerCAmelCase_ : Dict="gelu" , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : Tuple=5_12 , lowerCAmelCase_ : Optional[Any]=2 , lowerCAmelCase_ : Tuple=0.02 , lowerCAmelCase_ : Tuple=1e-12 , lowerCAmelCase_ : int=1 , lowerCAmelCase_ : Optional[Any]=0 , lowerCAmelCase_ : List[str]=2 , lowerCAmelCase_ : Dict="absolute" , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : Union[str, Any]=None , **lowerCAmelCase_ : Union[str, Any] , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase_ , bos_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , **lowerCAmelCase_ )
A__ : Optional[int] =vocab_size
A__ : Any =hidden_size
A__ : Optional[Any] =num_hidden_layers
A__ : Optional[int] =num_attention_heads
A__ : Optional[int] =hidden_act
A__ : Optional[Any] =intermediate_size
A__ : Tuple =hidden_dropout_prob
A__ : Union[str, Any] =attention_probs_dropout_prob
A__ : List[str] =max_position_embeddings
A__ : int =type_vocab_size
A__ : int =initializer_range
A__ : Tuple =layer_norm_eps
A__ : Dict =position_embedding_type
A__ : List[str] =use_cache
A__ : Dict =classifier_dropout
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
@property
def lowercase__ ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
A__ : Optional[int] ={0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A__ : List[str] ={0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 134 | 0 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Any , __UpperCamelCase : Tuple="shi-labs/oneformer_demo" ) -> Tuple:
"""simple docstring"""
with open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" ) , """r""" ) as f:
SCREAMING_SNAKE_CASE__ = json.load(__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = {}
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
for key, info in class_info.items():
SCREAMING_SNAKE_CASE__ = info["""name"""]
class_names.append(info["""name"""] )
if info["isthing"]:
thing_ids.append(int(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE__ = thing_ids
SCREAMING_SNAKE_CASE__ = class_names
return metadata
class __snake_case ( unittest.TestCase ):
def __init__( self : List[Any] , _lowercase : Optional[Any] , _lowercase : int=7 , _lowercase : Any=3 , _lowercase : int=30 , _lowercase : List[Any]=4_00 , _lowercase : Union[str, Any]=None , _lowercase : Dict=True , _lowercase : Tuple=True , _lowercase : int=[0.5, 0.5, 0.5] , _lowercase : List[str]=[0.5, 0.5, 0.5] , _lowercase : str=10 , _lowercase : Union[str, Any]=False , _lowercase : int=2_55 , _lowercase : List[str]="shi-labs/oneformer_demo" , _lowercase : Any="ade20k_panoptic.json" , _lowercase : Any=10 , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = batch_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = min_resolution
SCREAMING_SNAKE_CASE__ = max_resolution
SCREAMING_SNAKE_CASE__ = do_resize
SCREAMING_SNAKE_CASE__ = {"""shortest_edge""": 32, """longest_edge""": 13_33} if size is None else size
SCREAMING_SNAKE_CASE__ = do_normalize
SCREAMING_SNAKE_CASE__ = image_mean
SCREAMING_SNAKE_CASE__ = image_std
SCREAMING_SNAKE_CASE__ = class_info_file
SCREAMING_SNAKE_CASE__ = prepare_metadata(_lowercase , _lowercase )
SCREAMING_SNAKE_CASE__ = num_text
SCREAMING_SNAKE_CASE__ = repo_path
# for the post_process_functions
SCREAMING_SNAKE_CASE__ = 2
SCREAMING_SNAKE_CASE__ = 10
SCREAMING_SNAKE_CASE__ = 10
SCREAMING_SNAKE_CASE__ = 3
SCREAMING_SNAKE_CASE__ = 4
SCREAMING_SNAKE_CASE__ = num_labels
SCREAMING_SNAKE_CASE__ = do_reduce_labels
SCREAMING_SNAKE_CASE__ = ignore_index
def __a ( self : Optional[int] ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def __a ( self : List[Any] , _lowercase : Any , _lowercase : Optional[Any]=False ):
"""simple docstring"""
if not batched:
SCREAMING_SNAKE_CASE__ = image_inputs[0]
if isinstance(_lowercase , Image.Image ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = image.size
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = image.shape[1], image.shape[2]
if w < h:
SCREAMING_SNAKE_CASE__ = int(self.size["""shortest_edge"""] * h / w )
SCREAMING_SNAKE_CASE__ = self.size["""shortest_edge"""]
elif w > h:
SCREAMING_SNAKE_CASE__ = self.size["""shortest_edge"""]
SCREAMING_SNAKE_CASE__ = int(self.size["""shortest_edge"""] * w / h )
else:
SCREAMING_SNAKE_CASE__ = self.size["""shortest_edge"""]
SCREAMING_SNAKE_CASE__ = self.size["""shortest_edge"""]
else:
SCREAMING_SNAKE_CASE__ = []
for image in image_inputs:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
SCREAMING_SNAKE_CASE__ = max(_lowercase , key=lambda _lowercase : item[0] )[0]
SCREAMING_SNAKE_CASE__ = max(_lowercase , key=lambda _lowercase : item[1] )[1]
return expected_height, expected_width
def __a ( self : Dict ):
"""simple docstring"""
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class __snake_case ( lowerCamelCase_ , unittest.TestCase ):
lowerCAmelCase_ = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
lowerCAmelCase_ = image_processing_class
def __a ( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = OneFormerImageProcessorTester(self )
@property
def __a ( self : str ):
"""simple docstring"""
return self.image_processing_tester.prepare_image_processor_dict()
def __a ( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowercase , """image_mean""" ) )
self.assertTrue(hasattr(_lowercase , """image_std""" ) )
self.assertTrue(hasattr(_lowercase , """do_normalize""" ) )
self.assertTrue(hasattr(_lowercase , """do_resize""" ) )
self.assertTrue(hasattr(_lowercase , """size""" ) )
self.assertTrue(hasattr(_lowercase , """ignore_index""" ) )
self.assertTrue(hasattr(_lowercase , """class_info_file""" ) )
self.assertTrue(hasattr(_lowercase , """num_text""" ) )
self.assertTrue(hasattr(_lowercase , """repo_path""" ) )
self.assertTrue(hasattr(_lowercase , """metadata""" ) )
self.assertTrue(hasattr(_lowercase , """do_reduce_labels""" ) )
def __a ( self : Any ):
"""simple docstring"""
pass
def __a ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processing_tester , equal_resolution=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_expected_values(_lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_expected_values(_lowercase , batched=_lowercase )
SCREAMING_SNAKE_CASE__ = image_processor(
_lowercase , ["""semantic"""] * len(_lowercase ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def __a ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processing_tester , equal_resolution=_lowercase , numpify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_expected_values(_lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_expected_values(_lowercase , batched=_lowercase )
SCREAMING_SNAKE_CASE__ = image_processor(
_lowercase , ["""semantic"""] * len(_lowercase ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def __a ( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processing_tester , equal_resolution=_lowercase , torchify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_expected_values(_lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_expected_values(_lowercase , batched=_lowercase )
SCREAMING_SNAKE_CASE__ = image_processor(
_lowercase , ["""semantic"""] * len(_lowercase ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def __a ( self : Tuple , _lowercase : Optional[int]=False , _lowercase : Any=False , _lowercase : List[str]="np" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
SCREAMING_SNAKE_CASE__ = self.image_processing_tester.num_labels
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processing_tester , equal_resolution=_lowercase )
if with_segmentation_maps:
SCREAMING_SNAKE_CASE__ = num_labels
if is_instance_map:
SCREAMING_SNAKE_CASE__ = list(range(_lowercase ) ) * 2
SCREAMING_SNAKE_CASE__ = dict(enumerate(_lowercase ) )
SCREAMING_SNAKE_CASE__ = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
SCREAMING_SNAKE_CASE__ = [Image.fromarray(_lowercase ) for annotation in annotations]
SCREAMING_SNAKE_CASE__ = image_processor(
_lowercase , ["""semantic"""] * len(_lowercase ) , _lowercase , return_tensors="""pt""" , instance_id_to_semantic_id=_lowercase , pad_and_return_pixel_mask=_lowercase , )
return inputs
def __a ( self : str ):
"""simple docstring"""
pass
def __a ( self : Tuple ):
"""simple docstring"""
def common(_lowercase : Optional[int]=False , _lowercase : Union[str, Any]=None ):
SCREAMING_SNAKE_CASE__ = self.comm_get_image_processor_inputs(
with_segmentation_maps=_lowercase , is_instance_map=_lowercase , segmentation_type=_lowercase )
SCREAMING_SNAKE_CASE__ = inputs["""mask_labels"""]
SCREAMING_SNAKE_CASE__ = inputs["""class_labels"""]
SCREAMING_SNAKE_CASE__ = inputs["""pixel_values"""]
SCREAMING_SNAKE_CASE__ = inputs["""text_inputs"""]
# check the batch_size
for mask_label, class_label, text_input in zip(_lowercase , _lowercase , _lowercase ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(_lowercase ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=_lowercase )
common(is_instance_map=_lowercase , segmentation_type="""pil""" )
common(is_instance_map=_lowercase , segmentation_type="""pil""" )
def __a ( self : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = np.zeros((20, 50) )
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = binary_mask_to_rle(_lowercase )
self.assertEqual(len(_lowercase ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def __a ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_fake_oneformer_outputs()
SCREAMING_SNAKE_CASE__ = fature_extractor.post_process_semantic_segmentation(_lowercase )
self.assertEqual(len(_lowercase ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
SCREAMING_SNAKE_CASE__ = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
SCREAMING_SNAKE_CASE__ = fature_extractor.post_process_semantic_segmentation(_lowercase , target_sizes=_lowercase )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def __a ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_fake_oneformer_outputs()
SCREAMING_SNAKE_CASE__ = image_processor.post_process_instance_segmentation(_lowercase , threshold=0 )
self.assertTrue(len(_lowercase ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , _lowercase )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def __a ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_fake_oneformer_outputs()
SCREAMING_SNAKE_CASE__ = image_processor.post_process_panoptic_segmentation(_lowercase , threshold=0 )
self.assertTrue(len(_lowercase ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , _lowercase )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 355 | import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
__lowerCamelCase : Tuple = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
__lowerCamelCase : Optional[Any] = '''main'''
# Default branch name
__lowerCamelCase : Optional[int] = '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
__lowerCamelCase : Any = '''aaaaaaa'''
# This commit does not exist, so we should 404.
__lowerCamelCase : List[str] = '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
__lowerCamelCase : List[Any] = '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''
@contextlib.contextmanager
def __SCREAMING_SNAKE_CASE ( ) -> str:
"""simple docstring"""
print("""Welcome!""" )
yield
print("""Bye!""" )
@contextlib.contextmanager
def __SCREAMING_SNAKE_CASE ( ) -> Any:
"""simple docstring"""
print("""Bonjour!""" )
yield
print("""Au revoir!""" )
class __snake_case ( unittest.TestCase ):
def __a ( self : List[Any] ):
"""simple docstring"""
assert transformers.__spec__ is not None
assert importlib.util.find_spec("""transformers""" ) is not None
class __snake_case ( unittest.TestCase ):
@unittest.mock.patch("""sys.stdout""" , new_callable=io.StringIO )
def __a ( self : List[str] , _lowercase : str ):
"""simple docstring"""
with ContextManagers([] ):
print("""Transformers are awesome!""" )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , """Transformers are awesome!\n""" )
@unittest.mock.patch("""sys.stdout""" , new_callable=io.StringIO )
def __a ( self : Optional[Any] , _lowercase : str ):
"""simple docstring"""
with ContextManagers([context_en()] ):
print("""Transformers are awesome!""" )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , """Welcome!\nTransformers are awesome!\nBye!\n""" )
@unittest.mock.patch("""sys.stdout""" , new_callable=io.StringIO )
def __a ( self : Tuple , _lowercase : Dict ):
"""simple docstring"""
with ContextManagers([context_fr(), context_en()] ):
print("""Transformers are awesome!""" )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , """Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n""" )
@require_torch
def __a ( self : Union[str, Any] ):
"""simple docstring"""
self.assertEqual(find_labels(_lowercase ) , ["""labels"""] )
self.assertEqual(find_labels(_lowercase ) , ["""labels""", """next_sentence_label"""] )
self.assertEqual(find_labels(_lowercase ) , ["""start_positions""", """end_positions"""] )
class __snake_case ( lowerCamelCase_ ):
pass
self.assertEqual(find_labels(_lowercase ) , ["""labels"""] )
@require_tf
def __a ( self : Any ):
"""simple docstring"""
self.assertEqual(find_labels(_lowercase ) , ["""labels"""] )
self.assertEqual(find_labels(_lowercase ) , ["""labels""", """next_sentence_label"""] )
self.assertEqual(find_labels(_lowercase ) , ["""start_positions""", """end_positions"""] )
class __snake_case ( lowerCamelCase_ ):
pass
self.assertEqual(find_labels(_lowercase ) , ["""labels"""] )
@require_flax
def __a ( self : Union[str, Any] ):
"""simple docstring"""
self.assertEqual(find_labels(_lowercase ) , [] )
self.assertEqual(find_labels(_lowercase ) , [] )
self.assertEqual(find_labels(_lowercase ) , [] )
class __snake_case ( lowerCamelCase_ ):
pass
self.assertEqual(find_labels(_lowercase ) , [] )
| 204 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : torch.FloatTensor
class A ( nn.Module ):
def __init__(self : Union[str, Any] , __UpperCAmelCase : int=3 , __UpperCAmelCase : Dict=3 , __UpperCAmelCase : Optional[Any]=("DownEncoderBlock2D",) , __UpperCAmelCase : int=(6_4,) , __UpperCAmelCase : Union[str, Any]=2 , __UpperCAmelCase : Any=3_2 , __UpperCAmelCase : str="silu" , __UpperCAmelCase : Any=True , ) -> Dict:
"""simple docstring"""
super().__init__()
UpperCAmelCase__ = layers_per_block
UpperCAmelCase__ = torch.nn.Convad(
__UpperCAmelCase , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
UpperCAmelCase__ = None
UpperCAmelCase__ = nn.ModuleList([] )
# down
UpperCAmelCase__ = block_out_channels[0]
for i, down_block_type in enumerate(__UpperCAmelCase ):
UpperCAmelCase__ = output_channel
UpperCAmelCase__ = block_out_channels[i]
UpperCAmelCase__ = i == len(__UpperCAmelCase ) - 1
UpperCAmelCase__ = get_down_block(
__UpperCAmelCase , num_layers=self.layers_per_block , in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , add_downsample=not is_final_block , resnet_eps=1E-6 , downsample_padding=0 , resnet_act_fn=__UpperCAmelCase , resnet_groups=__UpperCAmelCase , attention_head_dim=__UpperCAmelCase , temb_channels=__UpperCAmelCase , )
self.down_blocks.append(__UpperCAmelCase )
# mid
UpperCAmelCase__ = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1E-6 , resnet_act_fn=__UpperCAmelCase , output_scale_factor=1 , resnet_time_scale_shift="default" , attention_head_dim=block_out_channels[-1] , resnet_groups=__UpperCAmelCase , temb_channels=__UpperCAmelCase , )
# out
UpperCAmelCase__ = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=__UpperCAmelCase , eps=1E-6 )
UpperCAmelCase__ = nn.SiLU()
UpperCAmelCase__ = 2 * out_channels if double_z else out_channels
UpperCAmelCase__ = nn.Convad(block_out_channels[-1] , __UpperCAmelCase , 3 , padding=1 )
UpperCAmelCase__ = False
def lowercase_ (self : List[Any] , __UpperCAmelCase : int ) -> str:
"""simple docstring"""
UpperCAmelCase__ = x
UpperCAmelCase__ = self.conv_in(__UpperCAmelCase )
if self.training and self.gradient_checkpointing:
def create_custom_forward(__UpperCAmelCase : int ):
def custom_forward(*__UpperCAmelCase : Optional[Any] ):
return module(*__UpperCAmelCase )
return custom_forward
# down
if is_torch_version(">=" , "1.11.0" ):
for down_block in self.down_blocks:
UpperCAmelCase__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(__UpperCAmelCase ) , __UpperCAmelCase , use_reentrant=__UpperCAmelCase )
# middle
UpperCAmelCase__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , __UpperCAmelCase , use_reentrant=__UpperCAmelCase )
else:
for down_block in self.down_blocks:
UpperCAmelCase__ = torch.utils.checkpoint.checkpoint(create_custom_forward(__UpperCAmelCase ) , __UpperCAmelCase )
# middle
UpperCAmelCase__ = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , __UpperCAmelCase )
else:
# down
for down_block in self.down_blocks:
UpperCAmelCase__ = down_block(__UpperCAmelCase )
# middle
UpperCAmelCase__ = self.mid_block(__UpperCAmelCase )
# post-process
UpperCAmelCase__ = self.conv_norm_out(__UpperCAmelCase )
UpperCAmelCase__ = self.conv_act(__UpperCAmelCase )
UpperCAmelCase__ = self.conv_out(__UpperCAmelCase )
return sample
class A ( nn.Module ):
def __init__(self : List[Any] , __UpperCAmelCase : str=3 , __UpperCAmelCase : Union[str, Any]=3 , __UpperCAmelCase : Optional[int]=("UpDecoderBlock2D",) , __UpperCAmelCase : str=(6_4,) , __UpperCAmelCase : Optional[Any]=2 , __UpperCAmelCase : Tuple=3_2 , __UpperCAmelCase : Any="silu" , __UpperCAmelCase : Any="group" , ) -> Dict:
"""simple docstring"""
super().__init__()
UpperCAmelCase__ = layers_per_block
UpperCAmelCase__ = nn.Convad(
__UpperCAmelCase , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
UpperCAmelCase__ = None
UpperCAmelCase__ = nn.ModuleList([] )
UpperCAmelCase__ = in_channels if norm_type == "spatial" else None
# mid
UpperCAmelCase__ = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1E-6 , resnet_act_fn=__UpperCAmelCase , output_scale_factor=1 , resnet_time_scale_shift="default" if norm_type == "group" else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=__UpperCAmelCase , temb_channels=__UpperCAmelCase , )
# up
UpperCAmelCase__ = list(reversed(__UpperCAmelCase ) )
UpperCAmelCase__ = reversed_block_out_channels[0]
for i, up_block_type in enumerate(__UpperCAmelCase ):
UpperCAmelCase__ = output_channel
UpperCAmelCase__ = reversed_block_out_channels[i]
UpperCAmelCase__ = i == len(__UpperCAmelCase ) - 1
UpperCAmelCase__ = get_up_block(
__UpperCAmelCase , num_layers=self.layers_per_block + 1 , in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , prev_output_channel=__UpperCAmelCase , add_upsample=not is_final_block , resnet_eps=1E-6 , resnet_act_fn=__UpperCAmelCase , resnet_groups=__UpperCAmelCase , attention_head_dim=__UpperCAmelCase , temb_channels=__UpperCAmelCase , resnet_time_scale_shift=__UpperCAmelCase , )
self.up_blocks.append(__UpperCAmelCase )
UpperCAmelCase__ = output_channel
# out
if norm_type == "spatial":
UpperCAmelCase__ = SpatialNorm(block_out_channels[0] , __UpperCAmelCase )
else:
UpperCAmelCase__ = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=__UpperCAmelCase , eps=1E-6 )
UpperCAmelCase__ = nn.SiLU()
UpperCAmelCase__ = nn.Convad(block_out_channels[0] , __UpperCAmelCase , 3 , padding=1 )
UpperCAmelCase__ = False
def lowercase_ (self : Optional[int] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Dict=None ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = z
UpperCAmelCase__ = self.conv_in(__UpperCAmelCase )
UpperCAmelCase__ = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(__UpperCAmelCase : str ):
def custom_forward(*__UpperCAmelCase : List[str] ):
return module(*__UpperCAmelCase )
return custom_forward
if is_torch_version(">=" , "1.11.0" ):
# middle
UpperCAmelCase__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , __UpperCAmelCase , __UpperCAmelCase , use_reentrant=__UpperCAmelCase )
UpperCAmelCase__ = sample.to(__UpperCAmelCase )
# up
for up_block in self.up_blocks:
UpperCAmelCase__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(__UpperCAmelCase ) , __UpperCAmelCase , __UpperCAmelCase , use_reentrant=__UpperCAmelCase )
else:
# middle
UpperCAmelCase__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , __UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase__ = sample.to(__UpperCAmelCase )
# up
for up_block in self.up_blocks:
UpperCAmelCase__ = torch.utils.checkpoint.checkpoint(create_custom_forward(__UpperCAmelCase ) , __UpperCAmelCase , __UpperCAmelCase )
else:
# middle
UpperCAmelCase__ = self.mid_block(__UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase__ = sample.to(__UpperCAmelCase )
# up
for up_block in self.up_blocks:
UpperCAmelCase__ = up_block(__UpperCAmelCase , __UpperCAmelCase )
# post-process
if latent_embeds is None:
UpperCAmelCase__ = self.conv_norm_out(__UpperCAmelCase )
else:
UpperCAmelCase__ = self.conv_norm_out(__UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase__ = self.conv_act(__UpperCAmelCase )
UpperCAmelCase__ = self.conv_out(__UpperCAmelCase )
return sample
class A ( nn.Module ):
def __init__(self : Optional[Any] , __UpperCAmelCase : str , __UpperCAmelCase : List[str] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Union[str, Any]="random" , __UpperCAmelCase : Dict=False , __UpperCAmelCase : Union[str, Any]=True ) -> Dict:
"""simple docstring"""
super().__init__()
UpperCAmelCase__ = n_e
UpperCAmelCase__ = vq_embed_dim
UpperCAmelCase__ = beta
UpperCAmelCase__ = legacy
UpperCAmelCase__ = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
UpperCAmelCase__ = remap
if self.remap is not None:
self.register_buffer("used" , torch.tensor(np.load(self.remap ) ) )
UpperCAmelCase__ = self.used.shape[0]
UpperCAmelCase__ = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
UpperCAmelCase__ = self.re_embed
UpperCAmelCase__ = self.re_embed + 1
print(
f"""Remapping {self.n_e} indices to {self.re_embed} indices. """
f"""Using {self.unknown_index} for unknown indices.""" )
else:
UpperCAmelCase__ = n_e
UpperCAmelCase__ = sane_index_shape
def lowercase_ (self : str , __UpperCAmelCase : str ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = inds.shape
assert len(__UpperCAmelCase ) > 1
UpperCAmelCase__ = inds.reshape(ishape[0] , -1 )
UpperCAmelCase__ = self.used.to(__UpperCAmelCase )
UpperCAmelCase__ = (inds[:, :, None] == used[None, None, ...]).long()
UpperCAmelCase__ = match.argmax(-1 )
UpperCAmelCase__ = match.sum(2 ) < 1
if self.unknown_index == "random":
UpperCAmelCase__ = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
UpperCAmelCase__ = self.unknown_index
return new.reshape(__UpperCAmelCase )
def lowercase_ (self : Tuple , __UpperCAmelCase : Optional[int] ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = inds.shape
assert len(__UpperCAmelCase ) > 1
UpperCAmelCase__ = inds.reshape(ishape[0] , -1 )
UpperCAmelCase__ = self.used.to(__UpperCAmelCase )
if self.re_embed > self.used.shape[0]: # extra token
UpperCAmelCase__ = 0 # simply set to zero
UpperCAmelCase__ = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , __UpperCAmelCase )
return back.reshape(__UpperCAmelCase )
def lowercase_ (self : Optional[Any] , __UpperCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = z.permute(0 , 2 , 3 , 1 ).contiguous()
UpperCAmelCase__ = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
UpperCAmelCase__ = torch.argmin(torch.cdist(__UpperCAmelCase , self.embedding.weight ) , dim=1 )
UpperCAmelCase__ = self.embedding(__UpperCAmelCase ).view(z.shape )
UpperCAmelCase__ = None
UpperCAmelCase__ = None
# compute loss for embedding
if not self.legacy:
UpperCAmelCase__ = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
UpperCAmelCase__ = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
UpperCAmelCase__ = z + (z_q - z).detach()
# reshape back to match original input shape
UpperCAmelCase__ = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
UpperCAmelCase__ = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
UpperCAmelCase__ = self.remap_to_used(__UpperCAmelCase )
UpperCAmelCase__ = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
UpperCAmelCase__ = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def lowercase_ (self : Optional[int] , __UpperCAmelCase : int , __UpperCAmelCase : Optional[Any] ) -> Any:
"""simple docstring"""
if self.remap is not None:
UpperCAmelCase__ = indices.reshape(shape[0] , -1 ) # add batch axis
UpperCAmelCase__ = self.unmap_to_all(__UpperCAmelCase )
UpperCAmelCase__ = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
UpperCAmelCase__ = self.embedding(__UpperCAmelCase )
if shape is not None:
UpperCAmelCase__ = z_q.view(__UpperCAmelCase )
# reshape back to match original input shape
UpperCAmelCase__ = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class A ( UpperCAmelCase_ ):
def __init__(self : Any , __UpperCAmelCase : Dict , __UpperCAmelCase : str=False ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = parameters
UpperCAmelCase__ , UpperCAmelCase__ = torch.chunk(__UpperCAmelCase , 2 , dim=1 )
UpperCAmelCase__ = torch.clamp(self.logvar , -30.0 , 20.0 )
UpperCAmelCase__ = deterministic
UpperCAmelCase__ = torch.exp(0.5 * self.logvar )
UpperCAmelCase__ = torch.exp(self.logvar )
if self.deterministic:
UpperCAmelCase__ = UpperCAmelCase__ = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def lowercase_ (self : Union[str, Any] , __UpperCAmelCase : Optional[torch.Generator] = None ) -> torch.FloatTensor:
"""simple docstring"""
UpperCAmelCase__ = randn_tensor(
self.mean.shape , generator=__UpperCAmelCase , device=self.parameters.device , dtype=self.parameters.dtype )
UpperCAmelCase__ = self.mean + self.std * sample
return x
def lowercase_ (self : str , __UpperCAmelCase : int=None ) -> Any:
"""simple docstring"""
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def lowercase_ (self : Dict , __UpperCAmelCase : Tuple , __UpperCAmelCase : Any=[1, 2, 3] ) -> Dict:
"""simple docstring"""
if self.deterministic:
return torch.Tensor([0.0] )
UpperCAmelCase__ = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=__UpperCAmelCase )
def lowercase_ (self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return self.mean
| 65 | def lowerCAmelCase_ ( __A, __A ) -> float:
'''simple docstring'''
def get_matched_characters(__A, __A ) -> str:
UpperCAmelCase__ = []
UpperCAmelCase__ = min(len(_stra ), len(_stra ) ) // 2
for i, l in enumerate(_stra ):
UpperCAmelCase__ = int(max(0, i - limit ) )
UpperCAmelCase__ = int(min(i + limit + 1, len(_stra ) ) )
if l in _stra[left:right]:
matched.append(__A )
UpperCAmelCase__ = f"""{_stra[0:_stra.index(__A )]} {_stra[_stra.index(__A ) + 1:]}"""
return "".join(__A )
# matching characters
UpperCAmelCase__ = get_matched_characters(__A, __A )
UpperCAmelCase__ = get_matched_characters(__A, __A )
UpperCAmelCase__ = len(__A )
# transposition
UpperCAmelCase__ = (
len([(ca, ca) for ca, ca in zip(__A, __A ) if ca != ca] ) // 2
)
if not match_count:
UpperCAmelCase__ = 0.0
else:
UpperCAmelCase__ = (
1
/ 3
* (
match_count / len(__A )
+ match_count / len(__A )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
UpperCAmelCase__ = 0
for ca, ca in zip(stra[:4], stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world'))
| 65 | 1 |
"""simple docstring"""
import math
import os
import sys
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = """"""
try:
with open(snake_case_ ,"""rb""" ) as binary_file:
UpperCamelCase : Any = binary_file.read()
for dat in data:
UpperCamelCase : int = f'{dat:08b}'
result += curr_byte
return result
except OSError:
print("""File not accessible""" )
sys.exit()
def A_ ( snake_case_ : dict[str, str] ,snake_case_ : str ,snake_case_ : int ,snake_case_ : str ):
'''simple docstring'''
lexicon.pop(snake_case_ )
UpperCamelCase : Union[str, Any] = last_match_id
if math.loga(snake_case_ ).is_integer():
for curr_key in lexicon:
UpperCamelCase : Any = """0""" + lexicon[curr_key]
UpperCamelCase : int = bin(snake_case_ )[2:]
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : str = {"""0""": """0""", """1""": """1"""}
UpperCamelCase : Dict = """""", """"""
UpperCamelCase : Optional[int] = len(snake_case_ )
for i in range(len(snake_case_ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
UpperCamelCase : Tuple = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
index += 1
UpperCamelCase : Any = """"""
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
UpperCamelCase : Dict = lexicon[curr_string]
result += last_match_id
return result
def A_ ( snake_case_ : str ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : str = os.path.getsize(snake_case_ )
UpperCamelCase : Union[str, Any] = bin(snake_case_ )[2:]
UpperCamelCase : List[str] = len(snake_case_ )
return "0" * (length_length - 1) + file_length_binary + compressed
def A_ ( snake_case_ : str ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : int = 8
try:
with open(snake_case_ ,"""wb""" ) as opened_file:
UpperCamelCase : Dict = [
to_write[i : i + byte_length]
for i in range(0 ,len(snake_case_ ) ,snake_case_ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append("""10000000""" )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array:
opened_file.write(int(snake_case_ ,2 ).to_bytes(1 ,byteorder="""big""" ) )
except OSError:
print("""File not accessible""" )
sys.exit()
def A_ ( snake_case_ : str ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : List[str] = read_file_binary(snake_case_ )
UpperCamelCase : Optional[int] = compress_data(snake_case_ )
UpperCamelCase : Any = add_file_length(snake_case_ ,snake_case_ )
write_file_binary(snake_case_ ,snake_case_ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 360 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "geglu" , SCREAMING_SNAKE_CASE_ = None , ):
super().__init__()
UpperCamelCase : int = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=SCREAMING_SNAKE_CASE_ , attention_head_dim=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , dropout=SCREAMING_SNAKE_CASE_ , norm_num_groups=SCREAMING_SNAKE_CASE_ , cross_attention_dim=SCREAMING_SNAKE_CASE_ , attention_bias=SCREAMING_SNAKE_CASE_ , sample_size=SCREAMING_SNAKE_CASE_ , num_vector_embeds=SCREAMING_SNAKE_CASE_ , activation_fn=SCREAMING_SNAKE_CASE_ , num_embeds_ada_norm=SCREAMING_SNAKE_CASE_ , )
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 : Optional[Any] = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
UpperCamelCase : List[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 : int = [1, 0]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase : Dict = hidden_states
UpperCamelCase : Optional[Any] = []
UpperCamelCase : List[Any] = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
UpperCamelCase : Optional[int] = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
UpperCamelCase : str = self.transformer_index_for_condition[i]
UpperCamelCase : Any = self.transformers[transformer_index](
SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , cross_attention_kwargs=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
UpperCamelCase : Any = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
UpperCamelCase : List[str] = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase ( UpperCamelCase__ ):
def __init__( self :Dict , lowercase_ :VQModel , lowercase_ :UNetaDModel , lowercase_ :DDIMScheduler )-> List[Any]:
super().__init__()
self.register_modules(vqvae=lowercase_ , unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self :str , lowercase_ :int = 1 , lowercase_ :Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ :float = 0.0 , lowercase_ :int = 50 , lowercase_ :Optional[str] = "pil" , lowercase_ :bool = True , **lowercase_ :Dict , )-> Union[Tuple, ImagePipelineOutput]:
A__ = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=lowercase_ , )
A__ = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
A__ = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(lowercase_ )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
A__ = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
A__ = {}
if accepts_eta:
A__ = eta
for t in self.progress_bar(self.scheduler.timesteps ):
A__ = self.scheduler.scale_model_input(lowercase_ , lowercase_ )
# predict the noise residual
A__ = self.unet(lowercase_ , lowercase_ ).sample
# compute the previous noisy sample x_t -> x_t-1
A__ = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
# decode the image latents with the VAE
A__ = self.vqvae.decode(lowercase_ ).sample
A__ = (image / 2 + 0.5).clamp(0 , 1 )
A__ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
A__ = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowercase_ )
| 237 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class UpperCAmelCase ( UpperCamelCase__ ):
def __init__( self :Optional[Any] , *lowercase_ :int , lowercase_ :Any=None , lowercase_ :List[str]=None , **lowercase_ :Any )-> Any:
super().__init__(*lowercase_ , **lowercase_ )
A__ = eval_examples
A__ = post_process_function
def UpperCAmelCase_ ( self :str , lowercase_ :str=None , lowercase_ :Optional[int]=None , lowercase_ :Optional[int]=None , lowercase_ :str = "eval" )-> Union[str, Any]:
A__ = self.eval_dataset if eval_dataset is None else eval_dataset
A__ = self.get_eval_dataloader(lowercase_ )
A__ = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
A__ = self.compute_metrics
A__ = None
A__ = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
A__ = time.time()
try:
A__ = eval_loop(
lowercase_ , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowercase_ , metric_key_prefix=lowercase_ , )
finally:
A__ = compute_metrics
A__ = self.args.eval_batch_size * self.args.world_size
if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
output.metrics.update(
speed_metrics(
lowercase_ , lowercase_ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
A__ = self.post_process_function(lowercase_ , lowercase_ , output.predictions )
A__ = self.compute_metrics(lowercase_ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
A__ = metrics.pop(lowercase_ )
metrics.update(output.metrics )
else:
A__ = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(lowercase_ )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
A__ = self.callback_handler.on_evaluate(self.args , self.state , self.control , lowercase_ )
return metrics
def UpperCAmelCase_ ( self :List[str] , lowercase_ :List[Any] , lowercase_ :str , lowercase_ :Any=None , lowercase_ :str = "test" )-> List[Any]:
A__ = self.get_test_dataloader(lowercase_ )
# Temporarily disable metric computation, we will do it in the loop here.
A__ = self.compute_metrics
A__ = None
A__ = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
A__ = time.time()
try:
A__ = eval_loop(
lowercase_ , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowercase_ , metric_key_prefix=lowercase_ , )
finally:
A__ = compute_metrics
A__ = self.args.eval_batch_size * self.args.world_size
if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
output.metrics.update(
speed_metrics(
lowercase_ , lowercase_ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
A__ = self.post_process_function(lowercase_ , lowercase_ , output.predictions , "predict" )
A__ = self.compute_metrics(lowercase_ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
A__ = metrics.pop(lowercase_ )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=lowercase_ )
| 237 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
"""junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
"""junnyu/roformer_chinese_char_small""": (
"""https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
),
"""junnyu/roformer_chinese_char_base""": (
"""https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
),
"""junnyu/roformer_small_discriminator""": (
"""https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
),
"""junnyu/roformer_small_generator""": (
"""https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = '''roformer'''
def __init__( self : str , _UpperCAmelCase : str=50000 , _UpperCAmelCase : Any=None , _UpperCAmelCase : Dict=768 , _UpperCAmelCase : Optional[int]=12 , _UpperCAmelCase : str=12 , _UpperCAmelCase : Optional[Any]=3072 , _UpperCAmelCase : Union[str, Any]="gelu" , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Optional[Any]=1536 , _UpperCAmelCase : str=2 , _UpperCAmelCase : Any=0.02 , _UpperCAmelCase : int=1e-12 , _UpperCAmelCase : List[str]=0 , _UpperCAmelCase : Any=False , _UpperCAmelCase : Tuple=True , **_UpperCAmelCase : str , ) -> Dict:
'''simple docstring'''
super().__init__(pad_token_id=_UpperCAmelCase , **_UpperCAmelCase )
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = hidden_size if embedding_size is None else embedding_size
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = type_vocab_size
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = rotary_value
UpperCAmelCase_ = use_cache
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def lowercase__ ( self : List[str] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
UpperCAmelCase_ = {0: "batch", 1: "choice", 2: "sequence"}
else:
UpperCAmelCase_ = {0: "batch", 1: "sequence"}
UpperCAmelCase_ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 354 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCamelCase = """src/diffusers"""
lowerCamelCase = """."""
# This is to make sure the diffusers module imported is the one in the repo.
lowerCamelCase = importlib.util.spec_from_file_location(
"""diffusers""",
os.path.join(DIFFUSERS_PATH, """__init__.py"""),
submodule_search_locations=[DIFFUSERS_PATH],
)
lowerCamelCase = spec.loader.load_module()
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
return line.startswith(lowerCAmelCase__ ) or len(lowerCAmelCase__ ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$" , lowerCAmelCase__ ) is not None
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = object_name.split("." )
UpperCAmelCase_ = 0
# First let's find the module where our object lives.
UpperCAmelCase_ = parts[i]
while i < len(lowerCAmelCase__ ) and not os.path.isfile(os.path.join(lowerCAmelCase__ , f"""{module}.py""" ) ):
i += 1
if i < len(lowerCAmelCase__ ):
UpperCAmelCase_ = os.path.join(lowerCAmelCase__ , parts[i] )
if i >= len(lowerCAmelCase__ ):
raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}.""" )
with open(os.path.join(lowerCAmelCase__ , f"""{module}.py""" ) , "r" , encoding="utf-8" , newline="\n" ) as f:
UpperCAmelCase_ = f.readlines()
# Now let's find the class / func in the code!
UpperCAmelCase_ = ""
UpperCAmelCase_ = 0
for name in parts[i + 1 :]:
while (
line_index < len(lowerCAmelCase__ ) and re.search(rf"""^{indent}(class|def)\s+{name}(\(|\:)""" , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(lowerCAmelCase__ ):
raise ValueError(f""" {object_name} does not match any function or class in {module}.""" )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
UpperCAmelCase_ = line_index
while line_index < len(lowerCAmelCase__ ) and _should_continue(lines[line_index] , lowerCAmelCase__ ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
UpperCAmelCase_ = lines[start_index:line_index]
return "".join(lowerCAmelCase__ )
lowerCamelCase = re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
lowerCamelCase = re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
lowerCamelCase = re.compile(r"""<FILL\s+[^>]*>""")
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = code.split("\n" )
UpperCAmelCase_ = 0
while idx < len(lowerCAmelCase__ ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(lowerCAmelCase__ ):
return re.search(r"^(\s*)\S" , lines[idx] ).groups()[0]
return ""
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = len(get_indent(lowerCAmelCase__ ) ) > 0
if has_indent:
UpperCAmelCase_ = f"""class Bla:\n{code}"""
UpperCAmelCase_ = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 , preview=lowerCAmelCase__ )
UpperCAmelCase_ = black.format_str(lowerCAmelCase__ , mode=lowerCAmelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = style_docstrings_in_code(lowerCAmelCase__ )
return result[len("class Bla:\n" ) :] if has_indent else result
def a__ ( lowerCAmelCase__ , lowerCAmelCase__=False ):
with open(lowerCAmelCase__ , "r" , encoding="utf-8" , newline="\n" ) as f:
UpperCAmelCase_ = f.readlines()
UpperCAmelCase_ = []
UpperCAmelCase_ = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(lowerCAmelCase__ ):
UpperCAmelCase_ = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = search.groups()
UpperCAmelCase_ = find_code_in_diffusers(lowerCAmelCase__ )
UpperCAmelCase_ = get_indent(lowerCAmelCase__ )
UpperCAmelCase_ = line_index + 1 if indent == theoretical_indent else line_index + 2
UpperCAmelCase_ = theoretical_indent
UpperCAmelCase_ = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
UpperCAmelCase_ = True
while line_index < len(lowerCAmelCase__ ) and should_continue:
line_index += 1
if line_index >= len(lowerCAmelCase__ ):
break
UpperCAmelCase_ = lines[line_index]
UpperCAmelCase_ = _should_continue(lowerCAmelCase__ , lowerCAmelCase__ ) and re.search(f"""^{indent}# End copy""" , lowerCAmelCase__ ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
UpperCAmelCase_ = lines[start_index:line_index]
UpperCAmelCase_ = "".join(lowerCAmelCase__ )
# Remove any nested `Copied from` comments to avoid circular copies
UpperCAmelCase_ = [line for line in theoretical_code.split("\n" ) if _re_copy_warning.search(lowerCAmelCase__ ) is None]
UpperCAmelCase_ = "\n".join(lowerCAmelCase__ )
# Before comparing, use the `replace_pattern` on the original code.
if len(lowerCAmelCase__ ) > 0:
UpperCAmelCase_ = replace_pattern.replace("with" , "" ).split("," )
UpperCAmelCase_ = [_re_replace_pattern.search(lowerCAmelCase__ ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = pattern.groups()
UpperCAmelCase_ = re.sub(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
if option.strip() == "all-casing":
UpperCAmelCase_ = re.sub(obja.lower() , obja.lower() , lowerCAmelCase__ )
UpperCAmelCase_ = re.sub(obja.upper() , obja.upper() , lowerCAmelCase__ )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
UpperCAmelCase_ = blackify(lines[start_index - 1] + theoretical_code )
UpperCAmelCase_ = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
UpperCAmelCase_ = lines[:start_index] + [theoretical_code] + lines[line_index:]
UpperCAmelCase_ = start_index + 1
if overwrite and len(lowerCAmelCase__ ) > 0:
# Warn the user a file has been modified.
print(f"""Detected changes, rewriting {filename}.""" )
with open(lowerCAmelCase__ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(lowerCAmelCase__ )
return diffs
def a__ ( lowerCAmelCase__ = False ):
UpperCAmelCase_ = glob.glob(os.path.join(lowerCAmelCase__ , "**/*.py" ) , recursive=lowerCAmelCase__ )
UpperCAmelCase_ = []
for filename in all_files:
UpperCAmelCase_ = is_copy_consistent(lowerCAmelCase__ , lowerCAmelCase__ )
diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
if not overwrite and len(lowerCAmelCase__ ) > 0:
UpperCAmelCase_ = "\n".join(lowerCAmelCase__ )
raise Exception(
"Found the following copy inconsistencies:\n"
+ diff
+ "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them." )
if __name__ == "__main__":
lowerCamelCase = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
lowerCamelCase = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 241 | 0 |
lowercase__ : Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase__ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase__ : str = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "Friday",
6: "Saturday",
}
def A_ ( snake_case : int , snake_case : int , snake_case : int ) -> str:
'''simple docstring'''
assert len(str(snake_case ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
__UpperCamelCase = year // 100
__UpperCamelCase = (5 * (century % 4) + 2) % 7
__UpperCamelCase = year % 100
__UpperCamelCase = centurian % 12
__UpperCamelCase = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
__UpperCamelCase = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
else DOOMSDAY_LEAP[month - 1]
)
__UpperCamelCase = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 328 |
from math import factorial
def A_ ( snake_case : int = 100 ) -> int:
'''simple docstring'''
return sum(int(snake_case ) for x in str(factorial(snake_case ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 328 | 1 |
UpperCAmelCase_ : Optional[Any] = {'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
UpperCAmelCase_ : Tuple = ['''a''', '''b''', '''c''', '''d''', '''e''']
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Any , __magic_name__ : Optional[int] , __magic_name__ : List[str] ) -> List[Any]:
"""simple docstring"""
UpperCamelCase :Any = start
# add current to visited
visited.append(__magic_name__ )
UpperCamelCase :Optional[Any] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
UpperCamelCase :Optional[Any] = topological_sort(__magic_name__ , __magic_name__ , __magic_name__ )
# if all neighbors visited add current to sort
sort.append(__magic_name__ )
# if all vertices haven't been visited select a new one to visit
if len(__magic_name__ ) != len(__magic_name__ ):
for vertice in vertices:
if vertice not in visited:
UpperCamelCase :List[str] = topological_sort(__magic_name__ , __magic_name__ , __magic_name__ )
# return sort
return sort
if __name__ == "__main__":
UpperCAmelCase_ : Optional[int] = topological_sort('''a''', [], [])
print(sort)
| 62 |
from math import pi
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int , __magic_name__ : int ) -> float:
"""simple docstring"""
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 62 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def _A ( ) -> int:
"""simple docstring"""
__UpperCamelCase = 9
__UpperCamelCase = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
__UpperCamelCase = kruskal(__snake_case , __snake_case )
__UpperCamelCase = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(__snake_case ) == sorted(__snake_case )
| 310 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __lowerCamelCase ( __snake_case : str, __snake_case : dict ) -> str:
"""simple docstring"""
A__ : Optional[Any] =BeautifulSoup(requests.get(__snake_case, params=__snake_case ).content, """html.parser""" )
A__ : List[str] =soup.find("""div""", attrs={"""class""": """gs_ri"""} )
A__ : Tuple =div.find("""div""", attrs={"""class""": """gs_fl"""} ).find_all("""a""" )
return anchors[2].get_text()
if __name__ == "__main__":
__snake_case : Optional[Any] = {
'title': (
'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
'capacitance, volumetric capacitance, and energy density'
),
'journal': 'Chem. Mater.',
'volume': 30,
'pages': '3979-3990',
'year': 2018,
'hl': 'en',
}
print(get_citation('https://scholar.google.com/scholar_lookup', params=params))
| 134 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=3 ,UpperCAmelCase_=32 ,UpperCAmelCase_=3 ,UpperCAmelCase_=10 ,UpperCAmelCase_=[10, 20, 30, 40] ,UpperCAmelCase_=[1, 1, 2, 1] ,UpperCAmelCase_=True ,UpperCAmelCase_=True ,UpperCAmelCase_="relu" ,UpperCAmelCase_=3 ,UpperCAmelCase_=None ,):
_lowercase : List[str] = parent
_lowercase : str = batch_size
_lowercase : Tuple = image_size
_lowercase : Dict = num_channels
_lowercase : List[str] = embeddings_size
_lowercase : str = hidden_sizes
_lowercase : List[Any] = depths
_lowercase : Optional[Any] = is_training
_lowercase : int = use_labels
_lowercase : List[Any] = hidden_act
_lowercase : Optional[Any] = num_labels
_lowercase : str = scope
_lowercase : Tuple = len(UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowercase : List[Any] = None
if self.use_labels:
_lowercase : Dict = ids_tensor([self.batch_size] ,self.num_labels )
_lowercase : List[Any] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase__ ( self ):
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 ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Dict = TFRegNetModel(config=UpperCAmelCase_ )
_lowercase : Union[str, Any] = model(UpperCAmelCase_ ,training=UpperCAmelCase_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = self.num_labels
_lowercase : Optional[int] = TFRegNetForImageClassification(UpperCAmelCase_ )
_lowercase : Dict = model(UpperCAmelCase_ ,labels=UpperCAmelCase_ ,training=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self ):
_lowercase : Optional[int] = self.prepare_config_and_inputs()
_lowercase , _lowercase , _lowercase : Optional[int] = config_and_inputs
_lowercase : Union[str, Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class UpperCamelCase ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ : List[Any] = (
{"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE_ : Optional[Any] = False
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : Any = False
def lowerCamelCase__ ( self ):
_lowercase : Dict = TFRegNetModelTester(self )
_lowercase : List[Any] = ConfigTester(self ,config_class=UpperCAmelCase_ ,has_text_modality=UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
return
@unittest.skip(reason="""RegNet does not use inputs_embeds""" )
def lowerCamelCase__ ( self ):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 ,reason="""TF does not support backprop for grouped convolutions on CPU.""" ,)
@slow
def lowerCamelCase__ ( self ):
super().test_keras_fit()
@unittest.skip(reason="""RegNet does not support input and output embeddings""" )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
_lowercase , _lowercase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase : Optional[int] = model_class(UpperCAmelCase_ )
_lowercase : Optional[int] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowercase : Optional[Any] = [*signature.parameters.keys()]
_lowercase : List[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
def check_hidden_states_output(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : List[Any] = model_class(UpperCAmelCase_ )
_lowercase : Optional[Any] = model(**self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ) ,training=UpperCAmelCase_ )
_lowercase : Dict = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_lowercase : Union[str, Any] = self.model_tester.num_stages
self.assertEqual(len(UpperCAmelCase_ ) ,expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[self.model_tester.image_size // 2, self.model_tester.image_size // 2] ,)
_lowercase , _lowercase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : Optional[int] = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
_lowercase : List[Any] = layer_type
_lowercase : 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"]
_lowercase : List[str] = True
check_hidden_states_output(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase , _lowercase : int = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_={} ):
_lowercase : Dict = model(UpperCAmelCase_ ,return_dict=UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Tuple = model(UpperCAmelCase_ ,return_dict=UpperCAmelCase_ ,**UpperCAmelCase_ ).to_tuple()
def recursive_check(UpperCAmelCase_ ,UpperCAmelCase_ ):
if isinstance(UpperCAmelCase_ ,(List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase_ ,UpperCAmelCase_ ):
recursive_check(UpperCAmelCase_ ,UpperCAmelCase_ )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(UpperCAmelCase_ ,UpperCAmelCase_ ) ) ,msg=(
"""Tuple and dict output are not equal. Difference:"""
f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
) ,)
recursive_check(UpperCAmelCase_ ,UpperCAmelCase_ )
for model_class in self.all_model_classes:
_lowercase : Any = model_class(UpperCAmelCase_ )
_lowercase : int = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : Any = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ )
check_equivalence(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : Union[str, Any] = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ,return_labels=UpperCAmelCase_ )
_lowercase : Dict = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ,return_labels=UpperCAmelCase_ )
check_equivalence(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : Union[str, Any] = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : Any = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ )
check_equivalence(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,{"""output_hidden_states""": True} )
_lowercase : Tuple = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ,return_labels=UpperCAmelCase_ )
_lowercase : Dict = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ,return_labels=UpperCAmelCase_ )
check_equivalence(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,{"""output_hidden_states""": True} )
def lowerCamelCase__ ( self ):
_lowercase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_ )
@slow
def lowerCamelCase__ ( self ):
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase : List[str] = TFRegNetModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Dict = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowerCamelCase__ ( self ):
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase__ ( self ):
_lowercase : str = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
_lowercase : int = self.default_image_processor
_lowercase : List[str] = prepare_img()
_lowercase : Union[str, Any] = image_processor(images=UpperCAmelCase_ ,return_tensors="""tf""" )
# forward pass
_lowercase : List[str] = model(**UpperCAmelCase_ ,training=UpperCAmelCase_ )
# verify the logits
_lowercase : Tuple = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape ,UpperCAmelCase_ )
_lowercase : Union[str, Any] = tf.constant([-0.4180, -1.5051, -3.4836] )
tf.debugging.assert_near(outputs.logits[0, :3] ,UpperCAmelCase_ ,atol=1E-4 )
| 336 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCAmelCase: Optional[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
UpperCAmelCase: Tuple = [0, 25, 50]
UpperCAmelCase: List[Any] = [25, 50, 75]
UpperCAmelCase: Optional[int] = fuzz.membership.trimf(X, abca)
UpperCAmelCase: Any = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
UpperCAmelCase: List[Any] = np.ones(75)
UpperCAmelCase: Any = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
UpperCAmelCase: str = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
UpperCAmelCase: Optional[int] = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
UpperCAmelCase: List[Any] = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
UpperCAmelCase: Optional[int] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
UpperCAmelCase: int = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
UpperCAmelCase: int = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
UpperCAmelCase: List[Any] = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
UpperCAmelCase: int = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("""Young""")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("""Middle aged""")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("""union""")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("""intersection""")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("""complement_a""")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("""difference a/b""")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("""alg_sum""")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("""alg_product""")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("""bdd_sum""")
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title("""bdd_difference""")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 336 | 1 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase = [
'''word_embeddings_layernorm.weight''',
'''word_embeddings_layernorm.bias''',
'''input_layernorm.weight''',
'''input_layernorm.bias''',
'''post_attention_layernorm.weight''',
'''post_attention_layernorm.bias''',
'''self_attention.dense.bias''',
'''mlp.dense_4h_to_h.bias''',
'''ln_f.weight''',
'''ln_f.bias''',
]
lowerCAmelCase = [
'''mlp.dense_4h_to_h.weight''',
'''self_attention.dense.weight''',
]
def _lowerCamelCase( lowercase__ , lowercase__ ) -> str:
'''simple docstring'''
__lowercase= {
'word_embeddings.weight': 'word_embeddings.weight',
'word_embeddings.norm.weight': 'word_embeddings_layernorm.weight',
'word_embeddings.norm.bias': 'word_embeddings_layernorm.bias',
'weight': 'ln_f.weight',
'bias': 'ln_f.bias',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
__lowercase= int(re.match(R'.*layer_(\d*).*' , lowercase__ )[1] )
layer_number -= 3
return F'h.{layer_number}.' + key
def _lowerCamelCase( lowercase__ ) -> List[str]:
'''simple docstring'''
if dtype == torch.bool:
return 1 / 8
__lowercase= re.search(R'[^\d](\d+)$' , str(lowercase__ ) )
if bit_search is None:
raise ValueError(F'`dtype` is not a valid dtype: {dtype}.' )
__lowercase= int(bit_search.groups()[0] )
return bit_size // 8
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ) -> Optional[Any]:
'''simple docstring'''
if bloom_config_file == "":
__lowercase= BloomConfig()
else:
__lowercase= BloomConfig.from_json_file(lowercase__ )
if shard_model:
__lowercase= os.listdir(lowercase__ )
__lowercase= sorted(filter(lambda lowercase__ : s.startswith('layer' ) and "model_00" in s , lowercase__ ) )
__lowercase= {'weight_map': {}, 'metadata': {}}
__lowercase= 0
__lowercase= None
__lowercase= BloomConfig()
for j, file in enumerate(lowercase__ ):
print('Processing file: {}'.format(lowercase__ ) )
__lowercase= None
for i in range(lowercase__ ):
# load all TP files
__lowercase= file.replace('model_00' , F'model_0{i}' )
__lowercase= torch.load(os.path.join(lowercase__ , lowercase__ ) , map_location='cpu' )
# Rename keys in the transformers names
__lowercase= list(temp.keys() )
for key in keys:
__lowercase= temp.pop(lowercase__ )
if tensors is None:
__lowercase= temp
else:
for key in tensors.keys():
if any(key.endswith(lowercase__ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
__lowercase= 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
__lowercase= torch.cat([tensors[key], temp[key]] , dim=lowercase__ )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(lowercase__ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
__lowercase= tensors[key] / pretraining_tp
torch.save(
lowercase__ , os.path.join(
lowercase__ , 'pytorch_model_{}-of-{}.bin'.format(str(j + 1 ).zfill(5 ) , str(len(lowercase__ ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
__lowercase= tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
__lowercase= 'pytorch_model_{}-of-{}.bin'.format(
str(j + 1 ).zfill(5 ) , str(len(lowercase__ ) ).zfill(5 ) )
__lowercase= BloomConfig()
__lowercase= pytorch_dump_folder_path + '/' + CONFIG_NAME
__lowercase= total_size
with open(lowercase__ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
with open(os.path.join(lowercase__ , WEIGHTS_NAME + '.index.json' ) , 'w' , encoding='utf-8' ) as f:
__lowercase= json.dumps(lowercase__ , indent=2 , sort_keys=lowercase__ ) + '\n'
f.write(lowercase__ )
else:
__lowercase= BloomModel(lowercase__ )
__lowercase= os.listdir(lowercase__ )
__lowercase= sorted(filter(lambda lowercase__ : s.startswith('layer' ) and "model_00" in s , lowercase__ ) )
__lowercase= None
for i, file in enumerate(lowercase__ ):
__lowercase= None
for i in range(lowercase__ ):
# load all TP files
__lowercase= file.replace('model_00' , F'model_0{i}' )
__lowercase= torch.load(os.path.join(lowercase__ , lowercase__ ) , map_location='cpu' )
# Rename keys in the transformers names
__lowercase= list(temp.keys() )
for key in keys:
__lowercase= temp.pop(lowercase__ )
if tensors is None:
__lowercase= temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(lowercase__ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
__lowercase= 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
__lowercase= torch.cat([tensors[key], temp[key]] , dim=lowercase__ )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(lowercase__ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
__lowercase= tensors[key] / pretraining_tp
__lowercase= model.load_state_dict(lowercase__ , strict=lowercase__ )
assert not other_keys.unexpected_keys, F'The keys {other_keys.unexpected_keys} are unexpected'
if missing_keys is None:
__lowercase= set(other_keys.missing_keys )
else:
__lowercase= missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, F'The keys {missing_keys} are missing'
# Save pytorch-model
os.makedirs(lowercase__ , exist_ok=lowercase__ )
__lowercase= pytorch_dump_folder_path + '/' + WEIGHTS_NAME
__lowercase= pytorch_dump_folder_path + '/' + CONFIG_NAME
print(F'Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}' )
if config.torch_dtype is not None:
__lowercase= model.to(config.torch_dtype )
torch.save(model.state_dict() , lowercase__ )
print(F'Save configuration file to {pytorch_config_dump_path}' )
with open(lowercase__ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bloom_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path to the Megatron-LM checkpoint path.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--bloom_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--shard_model''',
action='''store_true''',
help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
)
parser.add_argument(
'''--pretraining_tp''',
default=4,
type=int,
help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
)
lowerCAmelCase = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 295 |
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
lowerCAmelCase = '''▁'''
lowerCAmelCase = {'''vocab_file''': '''spiece.model'''}
lowerCAmelCase = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}
lowerCAmelCase = {
'''google/pegasus-xsum''': 5_1_2,
}
lowerCAmelCase = logging.get_logger(__name__)
class A ( A_ ):
UpperCamelCase_ : Union[str, Any] =VOCAB_FILES_NAMES
UpperCamelCase_ : List[Any] =VOCAB_FILES_NAMES
UpperCamelCase_ : int =PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Tuple =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : int =['''input_ids''', '''attention_mask''']
def __init__(self , lowerCAmelCase , lowerCAmelCase="<pad>" , lowerCAmelCase="</s>" , lowerCAmelCase="<unk>" , lowerCAmelCase="<mask_2>" , lowerCAmelCase="<mask_1>" , lowerCAmelCase=None , lowerCAmelCase=1_0_3 , lowerCAmelCase = None , **lowerCAmelCase , ):
__lowercase= offset
if additional_special_tokens is not None:
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
raise TypeError(
f'additional_special_tokens should be of type {type(lowerCAmelCase )}, but is'
f' {type(lowerCAmelCase )}' )
__lowercase= (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'<unk_{i}>' for i in range(len(lowerCAmelCase ) , self.offset - 1 )
]
if len(set(lowerCAmelCase ) ) != len(lowerCAmelCase ):
raise ValueError(
'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
__lowercase= additional_special_tokens_extended
else:
__lowercase= [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'<unk_{i}>' for i in range(2 , self.offset )]
__lowercase= {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=lowerCAmelCase , unk_token=lowerCAmelCase , mask_token=lowerCAmelCase , pad_token=lowerCAmelCase , mask_token_sent=lowerCAmelCase , offset=lowerCAmelCase , additional_special_tokens=lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase , )
__lowercase= mask_token_sent
__lowercase= vocab_file
__lowercase= spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCAmelCase )
# add special tokens to encoder dict
__lowercase= {
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
__lowercase= {v: k for k, v in self.encoder.items()}
@property
def _A (self ):
return len(self.sp_model ) + self.offset
def _A (self ):
__lowercase= {self.convert_ids_to_tokens(lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__(self ):
__lowercase= self.__dict__.copy()
__lowercase= None
return state
def __setstate__(self , lowerCAmelCase ):
__lowercase= d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
__lowercase= {}
__lowercase= spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _A (self , lowerCAmelCase ):
return self.sp_model.encode(lowerCAmelCase , out_type=lowerCAmelCase )
def _A (self , lowerCAmelCase ):
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
__lowercase= self.sp_model.piece_to_id(lowerCAmelCase )
return sp_id + self.offset
def _A (self , lowerCAmelCase ):
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
__lowercase= self.sp_model.IdToPiece(index - self.offset )
return token
def _A (self , lowerCAmelCase ):
__lowercase= []
__lowercase= ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(lowerCAmelCase ) + token
__lowercase= []
else:
current_sub_tokens.append(lowerCAmelCase )
out_string += self.sp_model.decode(lowerCAmelCase )
return out_string.strip()
def _A (self , lowerCAmelCase=False ):
return 1
def _A (self , lowerCAmelCase ):
__lowercase= set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def _A (self , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = False ):
if already_has_special_tokens:
return self._special_token_mask(lowerCAmelCase )
elif token_ids_a is None:
return self._special_token_mask(lowerCAmelCase ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A (self , lowerCAmelCase , lowerCAmelCase=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A (self , lowerCAmelCase , lowerCAmelCase = None ):
if not os.path.isdir(lowerCAmelCase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
__lowercase= os.path.join(
lowerCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCAmelCase , 'wb' ) as fi:
__lowercase= self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase )
return (out_vocab_file,)
| 295 | 1 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class lowercase ( UpperCamelCase__ ):
_a = "char"
_a = "bpe"
_a = "wp"
_snake_case = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class lowercase ( UpperCamelCase__ ):
_a = ["image_processor", "char_tokenizer"]
_a = "ViTImageProcessor"
_a = "MgpstrTokenizer"
def __init__( self , _a=None , _a=None , **_a ) -> Union[str, Any]:
_A : Dict = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , lowerCamelCase_ , )
_A : int = kwargs.pop("""feature_extractor""" )
_A : Optional[Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
_A : List[str] = tokenizer
_A : List[Any] = AutoTokenizer.from_pretrained("""gpt2""" )
_A : List[Any] = AutoTokenizer.from_pretrained("""bert-base-uncased""" )
super().__init__(lowerCamelCase_ , lowerCamelCase_ )
def __call__( self , _a=None , _a=None , _a=None , **_a ) -> int:
if images is None and text is None:
raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
if images is not None:
_A : Union[str, Any] = self.image_processor(lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
if text is not None:
_A : List[Any] = self.char_tokenizer(lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
if text is None:
return inputs
elif images is None:
return encodings
else:
_A : int = encodings["""input_ids"""]
return inputs
def a__ ( self , _a ) -> List[Any]:
_A , _A , _A : Optional[int] = sequences
_A : Dict = char_preds.size(0 )
_A , _A : Tuple = self._decode_helper(lowerCamelCase_ , """char""" )
_A , _A : Dict = self._decode_helper(lowerCamelCase_ , """bpe""" )
_A , _A : List[str] = self._decode_helper(lowerCamelCase_ , """wp""" )
_A : List[str] = []
_A : str = []
for i in range(lowerCamelCase_ ):
_A : Optional[Any] = [char_scores[i], bpe_scores[i], wp_scores[i]]
_A : int = [char_strs[i], bpe_strs[i], wp_strs[i]]
_A : Dict = scores.index(max(lowerCamelCase_ ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
_A : Union[str, Any] = {}
_A : int = final_strs
_A : Union[str, Any] = final_scores
_A : Optional[Any] = char_strs
_A : Any = bpe_strs
_A : List[Any] = wp_strs
return out
def a__ ( self , _a , _a ) -> List[str]:
if format == DecodeType.CHARACTER:
_A : Optional[int] = self.char_decode
_A : Any = 1
_A : Optional[int] = """[s]"""
elif format == DecodeType.BPE:
_A : Optional[int] = self.bpe_decode
_A : Any = 2
_A : str = """#"""
elif format == DecodeType.WORDPIECE:
_A : str = self.wp_decode
_A : str = 102
_A : Dict = """[SEP]"""
else:
raise ValueError(F'''Format {format} is not supported.''' )
_A , _A : List[Any] = [], []
_A : Optional[Any] = pred_logits.size(0 )
_A : int = pred_logits.size(1 )
_A , _A : int = pred_logits.topk(1 , dim=-1 , largest=lowerCamelCase_ , sorted=lowerCamelCase_ )
_A : Union[str, Any] = preds_index.view(-1 , lowerCamelCase_ )[:, 1:]
_A : Optional[Any] = decoder(lowerCamelCase_ )
_A , _A : List[Any] = torch.nn.functional.softmax(lowerCamelCase_ , dim=2 ).max(dim=2 )
_A : str = preds_max_prob[:, 1:]
for index in range(lowerCamelCase_ ):
_A : List[Any] = preds_str[index].find(lowerCamelCase_ )
_A : List[str] = preds_str[index][:pred_eos]
_A : int = preds_index[index].cpu().tolist()
_A : Optional[Any] = pred_index.index(lowerCamelCase_ ) if eos_token in pred_index else -1
_A : Union[str, Any] = preds_max_prob[index][: pred_eos_index + 1]
_A : str = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(lowerCamelCase_ )
conf_scores.append(lowerCamelCase_ )
return dec_strs, conf_scores
def a__ ( self , _a ) -> Any:
_A : str = [seq.replace(""" """ , """""" ) for seq in self.char_tokenizer.batch_decode(lowerCamelCase_ )]
return decode_strs
def a__ ( self , _a ) -> List[Any]:
return self.bpe_tokenizer.batch_decode(lowerCamelCase_ )
def a__ ( self , _a ) -> List[Any]:
_A : Optional[Any] = [seq.replace(""" """ , """""" ) for seq in self.wp_tokenizer.batch_decode(lowerCamelCase_ )]
return decode_strs
| 355 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
_snake_case = logging.getLogger()
def lowerCAmelCase_ ( ):
_A : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("""-f""" )
_A : Optional[Any] = parser.parse_args()
return args.f
class lowercase ( UpperCamelCase__ ):
def a__ ( self ) -> None:
_A : List[Any] = logging.StreamHandler(sys.stdout )
logger.addHandler(_a )
def a__ ( self , _a ) -> Dict:
_A : Tuple = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 , """run_glue_deebert.py""" )
with patch.object(_a , """argv""" , _a ):
_A : Optional[Any] = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(_a , 0.666 )
@slow
@require_torch_non_multi_gpu
def a__ ( self ) -> Optional[int]:
_A : Tuple = """
--model_type roberta
--model_name_or_path roberta-base
--task_name MRPC
--do_train
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--max_seq_length 128
--per_gpu_eval_batch_size=1
--per_gpu_train_batch_size=8
--learning_rate 2e-4
--num_train_epochs 3
--overwrite_output_dir
--seed 42
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--save_steps 0
--overwrite_cache
--eval_after_first_stage
""".split()
self.run_and_check(_a )
_A : Optional[Any] = """
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--eval_each_highway
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
""".split()
self.run_and_check(_a )
_A : List[str] = """
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--early_exit_entropy 0.1
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
""".split()
self.run_and_check(_a )
| 343 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and number_of_steps > 0
), f"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if number_of_steps == 1:
return 1
lowerCAmelCase_ , lowerCAmelCase_ : str = 1, 1
for _ in range(number_of_steps - 1 ):
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = current + previous, current
return current
if __name__ == "__main__":
import doctest
doctest.testmod() | 262 |
import inspect
import unittest
class snake_case__( unittest.TestCase ):
'''simple docstring'''
def lowercase_ ( self ) -> int:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def lowercase_ ( self ) -> List[str]:
import diffusers
from diffusers.dependency_versions_table import deps
lowerCAmelCase_ : Any = inspect.getmembers(__lowercase , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
lowerCAmelCase_ : Optional[int] = '''k-diffusion'''
elif backend == "invisible_watermark":
lowerCAmelCase_ : Dict = '''invisible-watermark'''
assert backend in deps, f"""{backend} is not in the deps table!""" | 262 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__A =logging.get_logger(__name__)
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__=False ):
lowerCamelCase_ = []
# fmt: off
# stem:
rename_keys.append(("cls_token", "vit.embeddings.cls_token") )
rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings") )
rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias") )
# backbone
rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias") )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight') )
rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias') )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias') )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
("pre_logits.fc.weight", "pooler.dense.weight"),
("pre_logits.fc.bias", "pooler.dense.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowerCamelCase_ = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
# fmt: on
return rename_keys
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ):
for i in range(config.num_hidden_layers ):
if base_model:
lowerCamelCase_ = ""
else:
lowerCamelCase_ = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCamelCase_ = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
lowerCamelCase_ = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase_ = in_proj_weight[
: config.hidden_size, :
]
lowerCamelCase_ = in_proj_bias[: config.hidden_size]
lowerCamelCase_ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCamelCase_ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCamelCase_ = in_proj_weight[
-config.hidden_size :, :
]
lowerCamelCase_ = in_proj_bias[-config.hidden_size :]
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(lowerCamelCase__ , lowerCamelCase__ )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = dct.pop(lowerCamelCase__ )
lowerCamelCase_ = val
def lowerCamelCase_ ( ):
lowerCamelCase_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCamelCase_ = Image.open(requests.get(lowerCamelCase__ , stream=lowerCamelCase__ ).raw )
return im
@torch.no_grad()
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ):
lowerCamelCase_ = BitConfig(
global_padding="same" , layer_type="bottleneck" , depths=(3, 4, 9) , out_features=["stage3"] , embedding_dynamic_padding=lowerCamelCase__ , )
lowerCamelCase_ = ViTHybridConfig(backbone_config=lowerCamelCase__ , image_size=3_8_4 , num_labels=1_0_0_0 )
lowerCamelCase_ = False
# load original model from timm
lowerCamelCase_ = timm.create_model(lowerCamelCase__ , pretrained=lowerCamelCase__ )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
lowerCamelCase_ = timm_model.state_dict()
if base_model:
remove_classification_head_(lowerCamelCase__ )
lowerCamelCase_ = create_rename_keys(lowerCamelCase__ , lowerCamelCase__ )
for src, dest in rename_keys:
rename_key(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
read_in_q_k_v(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ = "huggingface/label-files"
lowerCamelCase_ = "imagenet-1k-id2label.json"
lowerCamelCase_ = json.load(open(hf_hub_download(lowerCamelCase__ , lowerCamelCase__ , repo_type="dataset" ) , "r" ) )
lowerCamelCase_ = {int(lowerCamelCase__ ): v for k, v in idalabel.items()}
lowerCamelCase_ = idalabel
lowerCamelCase_ = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
lowerCamelCase_ = ViTHybridModel(lowerCamelCase__ ).eval()
else:
lowerCamelCase_ = ViTHybridForImageClassification(lowerCamelCase__ ).eval()
model.load_state_dict(lowerCamelCase__ )
# create image processor
lowerCamelCase_ = create_transform(**resolve_data_config({} , model=lowerCamelCase__ ) )
lowerCamelCase_ = transform.transforms
lowerCamelCase_ = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
lowerCamelCase_ = ViTHybridImageProcessor(
do_resize=lowerCamelCase__ , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=lowerCamelCase__ , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=lowerCamelCase__ , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
lowerCamelCase_ = prepare_img()
lowerCamelCase_ = transform(lowerCamelCase__ ).unsqueeze(0 )
lowerCamelCase_ = processor(lowerCamelCase__ , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(lowerCamelCase__ , lowerCamelCase__ )
# verify logits
with torch.no_grad():
lowerCamelCase_ = model(lowerCamelCase__ )
lowerCamelCase_ = outputs.logits
print("Predicted class:" , logits.argmax(-1 ).item() )
if base_model:
lowerCamelCase_ = timm_model.forward_features(lowerCamelCase__ )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(lowerCamelCase__ , outputs.pooler_output , atol=1e-3 )
else:
lowerCamelCase_ = timm_model(lowerCamelCase__ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(lowerCamelCase__ , outputs.logits , atol=1e-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(lowerCamelCase__ ).mkdir(exist_ok=lowerCamelCase__ )
print(F'Saving model {vit_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCamelCase__ )
print(F'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(lowerCamelCase__ )
if push_to_hub:
print(F'Pushing model and processor to the hub {vit_name}' )
model.push_to_hub(F'ybelkada/{vit_name}' )
processor.push_to_hub(F'ybelkada/{vit_name}' )
if __name__ == "__main__":
__A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--vit_name''',
default='''vit_base_r50_s16_384''',
type=str,
help='''Name of the hybrid ViT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
)
__A =parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 47 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, 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_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''label_embs_concat''': '''label_embeddings_concat''',
'''mask_emb''': '''masked_spec_embed''',
'''spk_proj''': '''speaker_proj''',
}
__A =[
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
'''label_embeddings_concat''',
'''speaker_proj''',
'''layer_norm_for_extract''',
]
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
for attribute in key.split("." ):
lowerCamelCase_ = getattr(lowerCamelCase__ , lowerCamelCase__ )
if weight_type is not None:
lowerCamelCase_ = getattr(lowerCamelCase__ , lowerCamelCase__ ).shape
else:
lowerCamelCase_ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}' )
if weight_type == "weight":
lowerCamelCase_ = value
elif weight_type == "weight_g":
lowerCamelCase_ = value
elif weight_type == "weight_v":
lowerCamelCase_ = value
elif weight_type == "bias":
lowerCamelCase_ = value
else:
lowerCamelCase_ = value
logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = []
lowerCamelCase_ = fairseq_model.state_dict()
lowerCamelCase_ = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
lowerCamelCase_ = False
if "conv_layers" in name:
load_conv_layer(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , hf_model.config.feat_extract_norm == "group" , )
lowerCamelCase_ = True
else:
for key, mapped_key in MAPPING.items():
lowerCamelCase_ = "unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split("." )[:-1] ) != key):
# special case since naming is very similar
continue
lowerCamelCase_ = True
if "*" in mapped_key:
lowerCamelCase_ = name.split(lowerCamelCase__ )[0].split("." )[-2]
lowerCamelCase_ = mapped_key.replace("*" , lowerCamelCase__ )
if "weight_g" in name:
lowerCamelCase_ = "weight_g"
elif "weight_v" in name:
lowerCamelCase_ = "weight_v"
elif "bias" in name:
lowerCamelCase_ = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowerCamelCase_ = "weight"
else:
lowerCamelCase_ = None
set_recursively(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
continue
if not is_used:
unused_weights.append(lowerCamelCase__ )
logger.warning(F'Unused weights: {unused_weights}' )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = full_name.split("conv_layers." )[-1]
lowerCamelCase_ = name.split("." )
lowerCamelCase_ = int(items[0] )
lowerCamelCase_ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
lowerCamelCase_ = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
lowerCamelCase_ = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F'{full_name} has size {value.shape}, but'
F' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.' )
lowerCamelCase_ = value
logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F'{full_name} has size {value.shape}, but'
F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.' )
lowerCamelCase_ = value
logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(lowerCamelCase__ )
@torch.no_grad()
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=True ):
if config_path is not None:
lowerCamelCase_ = UniSpeechSatConfig.from_pretrained(lowerCamelCase__ )
else:
lowerCamelCase_ = UniSpeechSatConfig()
lowerCamelCase_ = ""
if is_finetuned:
lowerCamelCase_ = UniSpeechSatForCTC(lowerCamelCase__ )
else:
lowerCamelCase_ = UniSpeechSatForPreTraining(lowerCamelCase__ )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
lowerCamelCase_ = model[0].eval()
recursively_load_weights(lowerCamelCase__ , lowerCamelCase__ )
hf_wavavec.save_pretrained(lowerCamelCase__ )
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('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
__A =parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 47 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase : str = get_tests_dir('fixtures/test_sentencepiece_no_bos.model')
@require_sentencepiece
@require_tokenizers
class __UpperCAmelCase ( A__ , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = PegasusTokenizer
__lowerCAmelCase = PegasusTokenizerFast
__lowerCAmelCase = True
__lowerCAmelCase = True
def A (self : Optional[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
A = PegasusTokenizer(_lowerCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def A (self : str ):
return PegasusTokenizer.from_pretrained("""google/pegasus-large""" )
def A (self : Optional[Any] , **_lowerCAmelCase : Dict ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def A (self : Optional[int] , _lowerCAmelCase : Dict ):
return ("This is a test", "This is a test")
def A (self : Dict ):
A = """</s>"""
A = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase ) , _lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase ) , _lowerCAmelCase )
def A (self : List[Any] ):
A = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<pad>""" )
self.assertEqual(vocab_keys[1] , """</s>""" )
self.assertEqual(vocab_keys[-1] , """v""" )
self.assertEqual(len(_lowerCAmelCase ) , 1103 )
def A (self : Tuple ):
self.assertEqual(self.get_tokenizer().vocab_size , 1103 )
def A (self : Optional[int] ):
A = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
A = self.tokenizer_class.from_pretrained(self.tmpdirname )
A = (
"""Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
""" </s> <pad> <pad> <pad>"""
)
A = rust_tokenizer([raw_input_str] , return_tensors=_lowerCAmelCase , add_special_tokens=_lowerCAmelCase ).input_ids[0]
A = py_tokenizer([raw_input_str] , return_tensors=_lowerCAmelCase , add_special_tokens=_lowerCAmelCase ).input_ids[0]
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def A (self : str ):
A = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
A = """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
A = [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
A = tokenizer([raw_input_str] , return_tensors=_lowerCAmelCase ).input_ids[0]
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def A (self : Tuple ):
A = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
A = """To ensure a smooth flow of bank resolutions."""
A = [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
A = tokenizer([raw_input_str] , return_tensors=_lowerCAmelCase ).input_ids[0]
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def A (self : str ):
A = ["""This is going to be way too long.""" * 150, """short example"""]
A = ["""not super long but more than 5 tokens""", """tiny"""]
A = self._large_tokenizer(_lowerCAmelCase , padding=_lowerCAmelCase , truncation=_lowerCAmelCase , return_tensors="""pt""" )
A = self._large_tokenizer(
text_target=_lowerCAmelCase , max_length=5 , padding=_lowerCAmelCase , truncation=_lowerCAmelCase , return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(_lowerCAmelCase ) == 2 # input_ids, attention_mask.
@slow
def A (self : Union[str, Any] ):
# fmt: off
A = {"""input_ids""": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowerCAmelCase , model_name="""google/bigbird-pegasus-large-arxiv""" , revision="""ba85d0851d708441f91440d509690f1ab6353415""" , )
@require_sentencepiece
@require_tokenizers
class __UpperCAmelCase ( A__ , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = PegasusTokenizer
__lowerCAmelCase = PegasusTokenizerFast
__lowerCAmelCase = True
__lowerCAmelCase = True
def A (self : Tuple ):
super().setUp()
# We have a SentencePiece fixture for testing
A = PegasusTokenizer(_lowerCAmelCase , offset=0 , mask_token_sent=_lowerCAmelCase , mask_token="""[MASK]""" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def A (self : int ):
return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv""" )
def A (self : Optional[int] , **_lowerCAmelCase : List[Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def A (self : Optional[int] , _lowerCAmelCase : Union[str, Any] ):
return ("This is a test", "This is a test")
def A (self : Union[str, Any] ):
A = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
A = self.tokenizer_class.from_pretrained(self.tmpdirname )
A = (
"""Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
""" <pad> <pad> <pad>"""
)
A = rust_tokenizer([raw_input_str] , return_tensors=_lowerCAmelCase , add_special_tokens=_lowerCAmelCase ).input_ids[0]
A = py_tokenizer([raw_input_str] , return_tensors=_lowerCAmelCase , add_special_tokens=_lowerCAmelCase ).input_ids[0]
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
@require_torch
def A (self : Optional[int] ):
A = ["""This is going to be way too long.""" * 1000, """short example"""]
A = ["""not super long but more than 5 tokens""", """tiny"""]
A = self._large_tokenizer(_lowerCAmelCase , padding=_lowerCAmelCase , truncation=_lowerCAmelCase , return_tensors="""pt""" )
A = self._large_tokenizer(
text_target=_lowerCAmelCase , max_length=5 , padding=_lowerCAmelCase , truncation=_lowerCAmelCase , return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(_lowerCAmelCase ) == 2 # input_ids, attention_mask.
def A (self : List[str] ):
A = (
"""This is an example string that is used to test the original TF implementation against the HF"""
""" implementation"""
)
A = self._large_tokenizer(_lowerCAmelCase ).input_ids
self.assertListEqual(
_lowerCAmelCase , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] , )
| 258 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class __UpperCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
__lowerCAmelCase = None
class __UpperCAmelCase ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
__lowerCAmelCase = PandasConfig
def A (self : Tuple ):
return datasets.DatasetInfo(features=self.config.features )
def A (self : Optional[int] , _lowerCAmelCase : List[Any] ):
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
A = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_lowerCAmelCase , (str, list, tuple) ):
A = data_files
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
A = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
A = [dl_manager.iter_files(_lowerCAmelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )]
A = []
for split_name, files in data_files.items():
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
A = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
A = [dl_manager.iter_files(_lowerCAmelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=_lowerCAmelCase , gen_kwargs={"""files""": files} ) )
return splits
def A (self : Dict , _lowerCAmelCase : pa.Table ):
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
A = table_cast(_lowerCAmelCase , self.config.features.arrow_schema )
return pa_table
def A (self : List[Any] , _lowerCAmelCase : Optional[Any] ):
for i, file in enumerate(itertools.chain.from_iterable(_lowerCAmelCase ) ):
with open(_lowerCAmelCase , """rb""" ) as f:
A = pa.Table.from_pandas(pd.read_pickle(_lowerCAmelCase ) )
yield i, self._cast_table(_lowerCAmelCase )
| 258 | 1 |
'''simple docstring'''
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : Tuple , lowerCamelCase__ : Optional[int] ) -> int:
# Initialise PyTorch model
A_ : int = AlbertConfig.from_json_file(lowerCamelCase__ )
print(f'Building PyTorch model from configuration: {config}' )
A_ : List[Any] = AlbertForPreTraining(lowerCamelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--albert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained ALBERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
snake_case__ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 363 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'speech_to_text_2'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Optional[Any] , _lowerCamelCase : Optional[Any]=10000 , _lowerCamelCase : List[Any]=6 , _lowerCamelCase : int=2048 , _lowerCamelCase : Dict=4 , _lowerCamelCase : str=0.0 , _lowerCamelCase : int=True , _lowerCamelCase : int="relu" , _lowerCamelCase : Any=256 , _lowerCamelCase : List[Any]=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Union[str, Any]=0.0 , _lowerCamelCase : Optional[Any]=0.02 , _lowerCamelCase : int=2 , _lowerCamelCase : List[str]=True , _lowerCamelCase : str=1 , _lowerCamelCase : List[Any]=0 , _lowerCamelCase : Optional[int]=2 , _lowerCamelCase : Tuple=1024 , **_lowerCamelCase : int , ):
"""simple docstring"""
A_ : Optional[int] = vocab_size
A_ : Tuple = d_model
A_ : List[str] = decoder_ffn_dim
A_ : str = decoder_layers
A_ : Any = decoder_attention_heads
A_ : int = dropout
A_ : str = attention_dropout
A_ : Optional[int] = activation_dropout
A_ : str = activation_function
A_ : List[Any] = init_std
A_ : Union[str, Any] = decoder_layerdrop
A_ : Any = use_cache
A_ : Optional[Any] = decoder_layers
A_ : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
A_ : Optional[Any] = max_target_positions
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
| 4 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
def lowercase_ ( self : List[Any] ) ->str:
snake_case__ : List[str] = pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type, pa.intaa() )
def lowercase_ ( self : Dict ) ->str:
with self.assertRaises(_snake_case ):
snake_case__ : Optional[int] = pa.array(TypedSequence([1, 2, 3] ), type=pa.intaa() )
def lowercase_ ( self : List[Any] ) ->Dict:
with self.assertRaises(_snake_case ):
snake_case__ : Any = pa.array(TypedSequence([1, 2, 3], try_type=Value('bool' ), type=Value('int64' ) ) )
def lowercase_ ( self : int ) ->Optional[int]:
snake_case__ : Union[str, Any] = pa.array(TypedSequence([1, 2, 3], type=Value('int32' ) ) )
self.assertEqual(arr.type, pa.intaa() )
def lowercase_ ( self : List[str] ) ->Optional[Any]:
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
snake_case__ : int = pa.array(TypedSequence(['foo', 'bar'], type=Value('int64' ) ) )
def lowercase_ ( self : Tuple ) ->Any:
snake_case__ : List[str] = pa.array(TypedSequence([1, 2, 3], try_type=Value('int32' ) ) )
self.assertEqual(arr.type, pa.intaa() )
def lowercase_ ( self : Optional[int] ) ->List[str]:
snake_case__ : str = pa.array(TypedSequence(['foo', 'bar'], try_type=Value('int64' ) ) )
self.assertEqual(arr.type, pa.string() )
def lowercase_ ( self : str ) ->List[Any]:
snake_case__ : Tuple = pa.array(TypedSequence([[[1, 2, 3]]], type=ArrayaD((1, 3), 'int64' ) ) )
self.assertEqual(arr.type, ArrayaDExtensionType((1, 3), 'int64' ) )
def lowercase_ ( self : Any ) ->List[Any]:
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
snake_case__ : Any = pa.array(TypedSequence(['foo', 'bar'], type=ArrayaD((1, 3), 'int64' ) ) )
def lowercase_ ( self : Union[str, Any] ) ->Tuple:
snake_case__ : Optional[Any] = pa.array(TypedSequence([[[1, 2, 3]]], try_type=ArrayaD((1, 3), 'int64' ) ) )
self.assertEqual(arr.type, ArrayaDExtensionType((1, 3), 'int64' ) )
def lowercase_ ( self : int ) ->Union[str, Any]:
snake_case__ : int = pa.array(TypedSequence(['foo', 'bar'], try_type=ArrayaD((1, 3), 'int64' ) ) )
self.assertEqual(arr.type, pa.string() )
@require_pil
def lowercase_ ( self : Any ) ->Optional[int]:
import PIL.Image
snake_case__ : Optional[int] = PIL.Image.fromarray(np.arange(1_0, dtype=np.uinta ).reshape(2, 5 ) )
with patch(
'datasets.arrow_writer.cast_to_python_objects', side_effect=_snake_case ) as mock_cast_to_python_objects:
snake_case__ : Dict = pa.array(TypedSequence([{'path': None, 'bytes': b'image_bytes'}, pil_image], type=Image() ) )
snake_case__ , snake_case__ : int = mock_cast_to_python_objects.call_args_list[-1]
self.assertIn('optimize_list_casting', _snake_case )
self.assertFalse(kwargs['optimize_list_casting'] )
def lowercase_ (A : Dict , A : int ):
snake_case__ : str = pa.BufferReader(A ) if isinstance(A , pa.Buffer ) else pa.memory_map(A )
snake_case__ : Optional[int] = pa.ipc.open_stream(A )
snake_case__ : pa.Table = f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize('writer_batch_size' , [None, 1, 1_0] )
@pytest.mark.parametrize(
'fields' , [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def lowercase_ (A : List[str] , A : List[str] ):
snake_case__ : Optional[Any] = pa.BufferOutputStream()
snake_case__ : Optional[int] = pa.schema(A ) if fields else None
with ArrowWriter(stream=A , schema=A , writer_batch_size=A ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
snake_case__ , snake_case__ : Optional[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
snake_case__ : str = {'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(A , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def lowercase_ ():
snake_case__ : int = pa.BufferOutputStream()
snake_case__ : Optional[int] = Features({'labels': ClassLabel(names=['neg', 'pos'] )} )
with ArrowWriter(stream=A , features=A ) as writer:
writer.write({'labels': 0} )
writer.write({'labels': 1} )
snake_case__ , snake_case__ : int = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
snake_case__ : List[str] = pa.BufferReader(output.getvalue() )
snake_case__ : int = pa.ipc.open_stream(A )
snake_case__ : pa.Table = f.read_all()
snake_case__ : List[Any] = pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(A )
@pytest.mark.parametrize('writer_batch_size' , [None, 1, 1_0] )
def lowercase_ (A : int ):
snake_case__ : str = pa.BufferOutputStream()
with ArrowWriter(
stream=A , writer_batch_size=A , hash_salt='split_name' , check_duplicates=A , ) as writer:
with pytest.raises(A ):
writer.write({'col_1': 'foo', 'col_2': 1} , key=[1, 2] )
snake_case__ , snake_case__ : List[Any] = writer.finalize()
@pytest.mark.parametrize('writer_batch_size' , [None, 2, 1_0] )
def lowercase_ (A : Optional[int] ):
snake_case__ : Optional[int] = pa.BufferOutputStream()
with ArrowWriter(
stream=A , writer_batch_size=A , hash_salt='split_name' , check_duplicates=A , ) as writer:
with pytest.raises(A ):
writer.write({'col_1': 'foo', 'col_2': 1} , key=1_0 )
writer.write({'col_1': 'bar', 'col_2': 2} , key=1_0 )
snake_case__ , snake_case__ : int = writer.finalize()
@pytest.mark.parametrize('writer_batch_size' , [None, 2, 1_0] )
def lowercase_ (A : List[Any] ):
snake_case__ : Any = pa.BufferOutputStream()
with ArrowWriter(
stream=A , writer_batch_size=A , hash_salt='split_name' , check_duplicates=A , ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} , key=1 )
writer.write({'col_1': 'bar', 'col_2': 2} , key=2 )
snake_case__ , snake_case__ : List[str] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size' , [None, 1, 1_0] )
@pytest.mark.parametrize(
'fields' , [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def lowercase_ (A : Dict , A : Any ):
snake_case__ : Union[str, Any] = pa.BufferOutputStream()
snake_case__ : Dict = pa.schema(A ) if fields else None
with ArrowWriter(stream=A , schema=A , writer_batch_size=A ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
writer.write_batch({'col_1': [], 'col_2': []} )
snake_case__ , snake_case__ : Optional[int] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
snake_case__ : Union[str, Any] = {'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(A , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size' , [None, 1, 1_0] )
@pytest.mark.parametrize(
'fields' , [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def lowercase_ (A : Tuple , A : str ):
snake_case__ : List[str] = pa.BufferOutputStream()
snake_case__ : int = pa.schema(A ) if fields else None
with ArrowWriter(stream=A , schema=A , writer_batch_size=A ) as writer:
writer.write_table(pa.Table.from_pydict({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} ) )
snake_case__ , snake_case__ : List[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
snake_case__ : List[Any] = {'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(A , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size' , [None, 1, 1_0] )
@pytest.mark.parametrize(
'fields' , [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def lowercase_ (A : List[str] , A : Union[str, Any] ):
snake_case__ : List[Any] = pa.BufferOutputStream()
snake_case__ : int = pa.schema(A ) if fields else None
with ArrowWriter(stream=A , schema=A , writer_batch_size=A ) as writer:
writer.write_row(pa.Table.from_pydict({'col_1': ['foo'], 'col_2': [1]} ) )
writer.write_row(pa.Table.from_pydict({'col_1': ['bar'], 'col_2': [2]} ) )
snake_case__ , snake_case__ : int = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
snake_case__ : List[str] = {'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(A , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def lowercase_ ():
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ : int = {'col_1': pa.string(), 'col_2': pa.intaa()}
snake_case__ : Tuple = os.path.join(A , 'test.arrow' )
with ArrowWriter(path=A , schema=pa.schema(A ) ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
snake_case__ , snake_case__ : Dict = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(A , metadata=writer._schema.metadata )
_check_output(A , 1 )
def lowercase_ (A : Union[str, Any] ):
if pa.types.is_list(A ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def lowercase_ (A : List[Any] , A : Union[str, Any] ):
if isinstance(lst[0] , A ):
change_first_primitive_element_in_list(lst[0] , A )
else:
snake_case__ : Dict = value
@pytest.mark.parametrize('optimized_int_type, expected_dtype' , [(None, pa.intaa()), (Value('int32' ), pa.intaa())] )
@pytest.mark.parametrize('sequence' , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def lowercase_ (A : Tuple , A : List[str] , A : List[Any] ):
snake_case__ : Any = pa.array(TypedSequence(A , optimized_int_type=A ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
'col, expected_dtype' , [
('attention_mask', pa.inta()),
('special_tokens_mask', pa.inta()),
('token_type_ids', pa.inta()),
('input_ids', pa.intaa()),
('other', pa.intaa()),
] , )
@pytest.mark.parametrize('sequence' , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def lowercase_ (A : List[Any] , A : Union[str, Any] , A : str ):
# in range
snake_case__ : Dict = pa.array(OptimizedTypedSequence(A , col=A ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
snake_case__ : Optional[Any] = copy.deepcopy(A )
snake_case__ : str = np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(A , A )
snake_case__ : List[str] = pa.array(OptimizedTypedSequence(A , col=A ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize('raise_exception' , [False, True] )
def lowercase_ (A : Union[str, Any] , A : List[str] ):
snake_case__ : List[str] = str(tmp_path / 'dataset-train.arrow' )
try:
with ArrowWriter(path=A ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def lowercase_ (A : int ):
snake_case__ : Any = 'mock://dataset-train.arrow'
with ArrowWriter(path=A , storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs , type(A ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
snake_case__ , snake_case__ : Union[str, Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(A )
def lowercase_ ():
snake_case__ : List[str] = pa.BufferOutputStream()
with ParquetWriter(stream=A ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
snake_case__ , snake_case__ : Any = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
snake_case__ : Any = pa.BufferReader(output.getvalue() )
snake_case__ : pa.Table = pq.read_table(A )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize('embed_local_files' , [False, True] )
def lowercase_ (A : List[Any] , A : Optional[Any] ):
import PIL.Image
snake_case__ : int = str(tmp_path / 'test_image_rgb.jpg' )
PIL.Image.fromarray(np.zeros((5, 5) , dtype=np.uinta ) ).save(A , format='png' )
snake_case__ : Tuple = pa.BufferOutputStream()
with ParquetWriter(
stream=A , features=Features({'image': Image()} ) , embed_local_files=A ) as writer:
writer.write({'image': image_path} )
writer.finalize()
snake_case__ : Dict = pa.BufferReader(output.getvalue() )
snake_case__ : pa.Table = pq.read_table(A )
snake_case__ : Optional[Any] = pa_table.to_pydict()
if embed_local_files:
assert isinstance(out['image'][0]['path'] , A )
with open(A , 'rb' ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def lowercase_ ():
snake_case__ : Dict = pa.schema([pa.field('col_1' , pa.string() , nullable=A )] )
snake_case__ : Any = pa.BufferOutputStream()
with ArrowWriter(stream=A ) as writer:
writer._build_writer(inferred_schema=A )
assert writer._schema == pa.schema([pa.field('col_1' , pa.string() )] )
| 277 |
from math import factorial
def lowercase_ (A : int , A : int , A : float ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-negative integers' )
if not isinstance(A , A ) or not isinstance(A , A ):
raise ValueError('the function is defined for non-negative integers' )
if not 0 < prob < 1:
raise ValueError('prob has to be in range of 1 - 0' )
snake_case__ : List[Any] = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
snake_case__ : List[str] = float(factorial(A ) )
coefficient /= factorial(A ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print("Probability of 2 successes out of 4 trails")
print("with probability of 0.75 is:", end=" ")
print(binomial_distribution(2, 4, 0.75))
| 277 | 1 |
import warnings
from .generation import TFGenerationMixin
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead." , A__ , )
| 218 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[Any] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
a = "vivit"
def __init__( self : str , __lowerCamelCase : List[Any]=224 , __lowerCamelCase : Optional[int]=32 , __lowerCamelCase : Tuple=[2, 16, 16] , __lowerCamelCase : Union[str, Any]=3 , __lowerCamelCase : Optional[Any]=768 , __lowerCamelCase : Any=12 , __lowerCamelCase : Optional[Any]=12 , __lowerCamelCase : List[str]=3072 , __lowerCamelCase : Any="gelu_fast" , __lowerCamelCase : Union[str, Any]=0.0 , __lowerCamelCase : int=0.0 , __lowerCamelCase : str=0.02 , __lowerCamelCase : Any=1e-06 , __lowerCamelCase : Dict=True , **__lowerCamelCase : Any , ) -> List[str]:
SCREAMING_SNAKE_CASE__ = hidden_size
SCREAMING_SNAKE_CASE__ = num_hidden_layers
SCREAMING_SNAKE_CASE__ = num_attention_heads
SCREAMING_SNAKE_CASE__ = intermediate_size
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = layer_norm_eps
SCREAMING_SNAKE_CASE__ = image_size
SCREAMING_SNAKE_CASE__ = num_frames
SCREAMING_SNAKE_CASE__ = tubelet_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = qkv_bias
super().__init__(**__lowerCamelCase )
| 218 | 1 |
'''simple docstring'''
def lowercase__ ( __lowercase : list[int] ) -> int:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(__lowercase , (list, tuple) ) or not all(
isinstance(__lowercase , __lowercase ) for number in numbers ):
raise ValueError('numbers must be an iterable of integers' )
__UpperCamelCase = __UpperCamelCase = __UpperCamelCase = numbers[0]
for i in range(1 , len(__lowercase ) ):
# update the maximum and minimum subarray products
__UpperCamelCase = numbers[i]
if number < 0:
__UpperCamelCase , __UpperCamelCase = min_till_now, max_till_now
__UpperCamelCase = max(__lowercase , max_till_now * number )
__UpperCamelCase = min(__lowercase , min_till_now * number )
# update the maximum product found till now
__UpperCamelCase = max(__lowercase , __lowercase )
return max_prod
| 53 |
'''simple docstring'''
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 237 | 0 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __snake_case:
'''simple docstring'''
def __init__( self , A_ , A_=13 , A_=32 , A_=2 , A_=3 , A_=16 , A_=[1, 2, 1] , A_=[2, 2, 4] , A_=2 , A_=2.0 , A_=True , A_=0.0 , A_=0.0 , A_=0.1 , A_="gelu" , A_=False , A_=True , A_=0.0_2 , A_=1e-5 , A_=True , A_=None , A_=True , A_=10 , A_=8 , ) -> Dict:
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = image_size
lowerCAmelCase = patch_size
lowerCAmelCase = num_channels
lowerCAmelCase = embed_dim
lowerCAmelCase = depths
lowerCAmelCase = num_heads
lowerCAmelCase = window_size
lowerCAmelCase = mlp_ratio
lowerCAmelCase = qkv_bias
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = drop_path_rate
lowerCAmelCase = hidden_act
lowerCAmelCase = use_absolute_embeddings
lowerCAmelCase = patch_norm
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = initializer_range
lowerCAmelCase = is_training
lowerCAmelCase = scope
lowerCAmelCase = use_labels
lowerCAmelCase = type_sequence_label_size
lowerCAmelCase = encoder_stride
def __snake_case ( self ) -> List[str]:
lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase = None
if self.use_labels:
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def __snake_case ( self ) -> int:
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __snake_case ( self , A_ , A_ , A_ ) -> int:
lowerCAmelCase = SwinvaModel(config=__snake_case )
model.to(__snake_case )
model.eval()
lowerCAmelCase = model(__snake_case )
lowerCAmelCase = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowerCAmelCase = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def __snake_case ( self , A_ , A_ , A_ ) -> List[str]:
lowerCAmelCase = SwinvaForMaskedImageModeling(config=__snake_case )
model.to(__snake_case )
model.eval()
lowerCAmelCase = model(__snake_case )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowerCAmelCase = 1
lowerCAmelCase = SwinvaForMaskedImageModeling(__snake_case )
model.to(__snake_case )
model.eval()
lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase = model(__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __snake_case ( self , A_ , A_ , A_ ) -> Optional[Any]:
lowerCAmelCase = self.type_sequence_label_size
lowerCAmelCase = SwinvaForImageClassification(__snake_case )
model.to(__snake_case )
model.eval()
lowerCAmelCase = model(__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __snake_case ( self ) -> List[Any]:
lowerCAmelCase = self.prepare_config_and_inputs()
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = config_and_inputs
lowerCAmelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __snake_case( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Any = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
UpperCAmelCase : List[str] = (
{'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase : Tuple = False
UpperCAmelCase : str = False
UpperCAmelCase : Dict = False
UpperCAmelCase : Union[str, Any] = False
def __snake_case ( self ) -> Optional[Any]:
lowerCAmelCase = SwinvaModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=__snake_case , embed_dim=37 )
def __snake_case ( self ) -> List[Any]:
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 __snake_case ( self ) -> str:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__snake_case )
@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0.""" )
def __snake_case ( self ) -> Optional[Any]:
pass
@unittest.skip(reason="""Swinv2 does not use inputs_embeds""" )
def __snake_case ( self ) -> Dict:
pass
def __snake_case ( self ) -> Optional[int]:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase = model_class(__snake_case )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowerCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__snake_case , nn.Linear ) )
def __snake_case ( self ) -> List[Any]:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase = model_class(__snake_case )
lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase = [*signature.parameters.keys()]
lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __snake_case )
def __snake_case ( self ) -> Dict:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = True
for model_class in self.all_model_classes:
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = True
lowerCAmelCase = model_class(__snake_case )
model.to(__snake_case )
model.eval()
with torch.no_grad():
lowerCAmelCase = model(**self._prepare_for_class(__snake_case , __snake_case ) )
lowerCAmelCase = outputs.attentions
lowerCAmelCase = len(self.model_tester.depths )
self.assertEqual(len(__snake_case ) , __snake_case )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCAmelCase = True
lowerCAmelCase = config.window_size**2
lowerCAmelCase = model_class(__snake_case )
model.to(__snake_case )
model.eval()
with torch.no_grad():
lowerCAmelCase = model(**self._prepare_for_class(__snake_case , __snake_case ) )
lowerCAmelCase = outputs.attentions
self.assertEqual(len(__snake_case ) , __snake_case )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
lowerCAmelCase = len(__snake_case )
# Check attention is always last and order is fine
lowerCAmelCase = True
lowerCAmelCase = True
lowerCAmelCase = model_class(__snake_case )
model.to(__snake_case )
model.eval()
with torch.no_grad():
lowerCAmelCase = model(**self._prepare_for_class(__snake_case , __snake_case ) )
if hasattr(self.model_tester , """num_hidden_states_types""" ):
lowerCAmelCase = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
lowerCAmelCase = 2
self.assertEqual(out_len + added_hidden_states , len(__snake_case ) )
lowerCAmelCase = outputs.attentions
self.assertEqual(len(__snake_case ) , __snake_case )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def __snake_case ( self , A_ , A_ , A_ , A_ ) -> List[str]:
lowerCAmelCase = model_class(__snake_case )
model.to(__snake_case )
model.eval()
with torch.no_grad():
lowerCAmelCase = model(**self._prepare_for_class(__snake_case , __snake_case ) )
lowerCAmelCase = outputs.hidden_states
lowerCAmelCase = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__snake_case ) , __snake_case )
# Swinv2 has a different seq_length
lowerCAmelCase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
lowerCAmelCase = outputs.reshaped_hidden_states
self.assertEqual(len(__snake_case ) , __snake_case )
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = reshaped_hidden_states[0].shape
lowerCAmelCase = (
reshaped_hidden_states[0].view(__snake_case , __snake_case , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __snake_case ( self ) -> int:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
lowerCAmelCase = True
self.check_hidden_states_output(__snake_case , __snake_case , __snake_case , __snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase = True
self.check_hidden_states_output(__snake_case , __snake_case , __snake_case , __snake_case )
def __snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = 3
lowerCAmelCase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
lowerCAmelCase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowerCAmelCase = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowerCAmelCase = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
lowerCAmelCase = True
self.check_hidden_states_output(__snake_case , __snake_case , __snake_case , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase = True
self.check_hidden_states_output(__snake_case , __snake_case , __snake_case , (padded_height, padded_width) )
def __snake_case ( self ) -> Any:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__snake_case )
def __snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__snake_case )
@slow
def __snake_case ( self ) -> Tuple:
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = SwinvaModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
def __snake_case ( self ) -> Any:
lowerCAmelCase, lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = _config_zero_init(__snake_case )
for model_class in self.all_model_classes:
lowerCAmelCase = model_class(config=__snake_case )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
@require_vision
@require_torch
class __snake_case( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __snake_case ( self ) -> str:
return (
AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" )
if is_vision_available()
else None
)
@slow
def __snake_case ( self ) -> Any:
lowerCAmelCase = SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" ).to(
__snake_case )
lowerCAmelCase = self.default_image_processor
lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowerCAmelCase = image_processor(images=__snake_case , return_tensors="""pt""" ).to(__snake_case )
# forward pass
with torch.no_grad():
lowerCAmelCase = model(**__snake_case )
# verify the logits
lowerCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __snake_case )
lowerCAmelCase = torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6] ).to(__snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __snake_case , atol=1e-4 ) ) | 371 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( _SCREAMING_SNAKE_CASE : int | str ) -> bool:
"""simple docstring"""
lowerCAmelCase = str(_SCREAMING_SNAKE_CASE )
return n == n[::-1]
def _snake_case ( _SCREAMING_SNAKE_CASE : int = 1_000_000 ) -> Dict:
"""simple docstring"""
lowerCAmelCase = 0
for i in range(1 , _SCREAMING_SNAKE_CASE ):
if is_palindrome(_SCREAMING_SNAKE_CASE ) and is_palindrome(bin(_SCREAMING_SNAKE_CASE ).split("""b""" )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip())))) | 187 | 0 |
'''simple docstring'''
import sys
a_ : Tuple = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66896648950445244523161731856403098711121722383113"""
"""62229893423380308135336276614282806444486645238749"""
"""30358907296290491560440772390713810515859307960866"""
"""70172427121883998797908792274921901699720888093776"""
"""65727333001053367881220235421809751254540594752243"""
"""52584907711670556013604839586446706324415722155397"""
"""53697817977846174064955149290862569321978468622482"""
"""83972241375657056057490261407972968652414535100474"""
"""82166370484403199890008895243450658541227588666881"""
"""16427171479924442928230863465674813919123162824586"""
"""17866458359124566529476545682848912883142607690042"""
"""24219022671055626321111109370544217506941658960408"""
"""07198403850962455444362981230987879927244284909188"""
"""84580156166097919133875499200524063689912560717606"""
"""05886116467109405077541002256983155200055935729725"""
"""71636269561882670428252483600823257530420752963450"""
)
def a_ ( __snake_case : str = N ) -> int:
"""simple docstring"""
lowerCamelCase_ =-sys.maxsize - 1
for i in range(len(__snake_case ) - 12 ):
lowerCamelCase_ =1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
lowerCamelCase_ =product
return largest_product
if __name__ == "__main__":
print(F"""{solution() = }""")
| 75 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class lowerCAmelCase__ ( unittest.TestCase ):
def lowercase ( self : Any ):
_snake_case = tempfile.mkdtemp()
# fmt: off
_snake_case = ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
# fmt: on
_snake_case = dict(zip(_lowerCamelCase , range(len(_lowerCamelCase ) ) ) )
_snake_case = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
_snake_case = {'''unk_token''': '''<unk>'''}
_snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_lowerCamelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_lowerCamelCase ) )
_snake_case = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
'''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
_snake_case = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def lowercase ( self : Tuple , **_lowerCamelCase : Any ):
return CLIPTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def lowercase ( self : str , **_lowerCamelCase : Any ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def lowercase ( self : int , **_lowerCamelCase : Optional[int] ):
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def lowercase ( self : Union[str, Any] ):
shutil.rmtree(self.tmpdirname )
def lowercase ( self : Any ):
_snake_case = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
_snake_case = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase ( self : Optional[Any] ):
_snake_case = self.get_tokenizer()
_snake_case = self.get_rust_tokenizer()
_snake_case = self.get_image_processor()
_snake_case = CLIPProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor_slow.save_pretrained(self.tmpdirname )
_snake_case = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=_lowerCamelCase )
_snake_case = CLIPProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor_fast.save_pretrained(self.tmpdirname )
_snake_case = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _lowerCamelCase )
self.assertIsInstance(processor_fast.tokenizer , _lowerCamelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _lowerCamelCase )
self.assertIsInstance(processor_fast.image_processor , _lowerCamelCase )
def lowercase ( self : List[Any] ):
_snake_case = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_snake_case = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
_snake_case = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
_snake_case = CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def lowercase ( self : int ):
_snake_case = self.get_image_processor()
_snake_case = self.get_tokenizer()
_snake_case = CLIPProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
_snake_case = self.prepare_image_inputs()
_snake_case = image_processor(_lowerCamelCase , return_tensors='''np''' )
_snake_case = processor(images=_lowerCamelCase , return_tensors='''np''' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowercase ( self : Any ):
_snake_case = self.get_image_processor()
_snake_case = self.get_tokenizer()
_snake_case = CLIPProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
_snake_case = '''lower newer'''
_snake_case = processor(text=_lowerCamelCase )
_snake_case = tokenizer(_lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase ( self : Any ):
_snake_case = self.get_image_processor()
_snake_case = self.get_tokenizer()
_snake_case = CLIPProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
_snake_case = '''lower newer'''
_snake_case = self.prepare_image_inputs()
_snake_case = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(_lowerCamelCase ):
processor()
def lowercase ( self : List[str] ):
_snake_case = self.get_image_processor()
_snake_case = self.get_tokenizer()
_snake_case = CLIPProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
_snake_case = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_snake_case = processor.batch_decode(_lowerCamelCase )
_snake_case = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def lowercase ( self : List[Any] ):
_snake_case = self.get_image_processor()
_snake_case = self.get_tokenizer()
_snake_case = CLIPProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
_snake_case = '''lower newer'''
_snake_case = self.prepare_image_inputs()
_snake_case = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 288 | 0 |
import re
import string
import numpy as np
import datasets
__UpperCamelCase : Tuple = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
__UpperCamelCase : int = "\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results[\"exact_match\"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 33.3\n\n"
__UpperCamelCase : Optional[int] = "\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class __magic_name__ ( datasets.Metric):
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , reference_urls=[] , )
def UpperCAmelCase__ ( self : List[Any] , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Optional[int]=None , lowerCamelCase__ : Dict=False , lowerCamelCase__ : Dict=False , lowerCamelCase__ : Optional[Any]=False , ) -> List[str]:
'''simple docstring'''
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
UpperCamelCase__ : List[str] = np.array([re.sub(_A , '''''' , _A ) for x in predictions] )
UpperCamelCase__ : int = np.array([re.sub(_A , '''''' , _A ) for x in references] )
else:
UpperCamelCase__ : Optional[Any] = np.asarray(_A )
UpperCamelCase__ : Optional[Any] = np.asarray(_A )
if ignore_case:
UpperCamelCase__ : int = np.char.lower(_A )
UpperCamelCase__ : List[str] = np.char.lower(_A )
if ignore_punctuation:
UpperCamelCase__ : str = string.punctuation.maketrans('''''' , '''''' , string.punctuation )
UpperCamelCase__ : str = np.char.translate(_A , table=_A )
UpperCamelCase__ : Any = np.char.translate(_A , table=_A )
if ignore_numbers:
UpperCamelCase__ : int = string.digits.maketrans('''''' , '''''' , string.digits )
UpperCamelCase__ : Tuple = np.char.translate(_A , table=_A )
UpperCamelCase__ : Optional[Any] = np.char.translate(_A , table=_A )
UpperCamelCase__ : Optional[Any] = predictions == references
return {"exact_match": np.mean(_A ) * 100}
| 371 |
def _a ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest must be >= 0''' )
if years_to_repay <= 0 or not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise Exception('''Years to repay must be an integer > 0''' )
# Yearly rate is divided by 12 to get monthly rate
UpperCamelCase__ : int = rate_per_annum / 12
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
UpperCamelCase__ : int = years_to_repay * 12
return (
principal
* rate_per_month
* (1 + rate_per_month) ** number_of_payments
/ ((1 + rate_per_month) ** number_of_payments - 1)
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 51 | 0 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __SCREAMING_SNAKE_CASE ( ctypes.Structure ):
'''simple docstring'''
_a = [('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def snake_case__ ( ):
"""simple docstring"""
if os.name == "nt":
lowerCamelCase__ : Union[str, Any] =CursorInfo()
lowerCamelCase__ : int =ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(__lowerCamelCase , ctypes.byref(__lowerCamelCase ) )
lowerCamelCase__ : Tuple =False
ctypes.windll.kernelaa.SetConsoleCursorInfo(__lowerCamelCase , ctypes.byref(__lowerCamelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25l''' )
sys.stdout.flush()
def snake_case__ ( ):
"""simple docstring"""
if os.name == "nt":
lowerCamelCase__ : Optional[Any] =CursorInfo()
lowerCamelCase__ : List[Any] =ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(__lowerCamelCase , ctypes.byref(__lowerCamelCase ) )
lowerCamelCase__ : List[str] =True
ctypes.windll.kernelaa.SetConsoleCursorInfo(__lowerCamelCase , ctypes.byref(__lowerCamelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25h''' )
sys.stdout.flush()
@contextmanager
def snake_case__ ( ):
"""simple docstring"""
try:
hide_cursor()
yield
finally:
show_cursor()
| 238 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _SCREAMING_SNAKE_CASE ( lowercase : Union[dict, list, tuple, torch.Tensor] ):
'''simple docstring'''
lowerCamelCase_ = []
if isinstance(lowercase , lowercase ):
for v in tree.values():
shapes.extend(_fetch_dims(lowercase ) )
elif isinstance(lowercase , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(lowercase ) )
elif isinstance(lowercase , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError('Not supported' )
return shapes
@torch.jit.ignore
def _SCREAMING_SNAKE_CASE ( lowercase : int , lowercase : Tuple[int, ...] ):
'''simple docstring'''
lowerCamelCase_ = []
for d in reversed(lowercase ):
idx.append(flat_idx % d )
lowerCamelCase_ = flat_idx // d
return tuple(reversed(lowercase ) )
@torch.jit.ignore
def _SCREAMING_SNAKE_CASE ( lowercase : Sequence[int] , lowercase : Sequence[int] , lowercase : Sequence[int] , lowercase : Optional[Sequence[bool]] = None , lowercase : Optional[Sequence[bool]] = None , ):
'''simple docstring'''
def reduce_edge_list(lowercase : List[bool] ) -> None:
lowerCamelCase_ = True
for i in range(len(lowercase ) ):
lowerCamelCase_ = -1 * (i + 1)
l[reversed_idx] &= tally
lowerCamelCase_ = l[reversed_idx]
if start_edges is None:
lowerCamelCase_ = [s == 0 for s in start]
reduce_edge_list(lowercase )
if end_edges is None:
lowerCamelCase_ = [e == (d - 1) for e, d in zip(lowercase , lowercase )]
reduce_edge_list(lowercase )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(lowercase ) == 0:
return [()]
elif len(lowercase ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
lowerCamelCase_ = []
lowerCamelCase_ = []
# Dimensions common to start and end can be selected directly
for s, e in zip(lowercase , lowercase ):
if s == e:
path_list.append(slice(lowercase , s + 1 ) )
else:
break
lowerCamelCase_ = tuple(lowercase )
lowerCamelCase_ = len(lowercase )
# start == end, and we're done
if divergence_idx == len(lowercase ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
lowerCamelCase_ = start[divergence_idx]
return tuple(
path + (slice(lowercase , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
lowerCamelCase_ = end[divergence_idx]
return tuple(
path + (slice(lowercase , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
lowerCamelCase_ = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def _SCREAMING_SNAKE_CASE ( lowercase : torch.Tensor , lowercase : int , lowercase : int , lowercase : int ):
'''simple docstring'''
lowerCamelCase_ = t.shape[:no_batch_dims]
lowerCamelCase_ = list(_flat_idx_to_idx(lowercase , lowercase ) )
# _get_minimal_slice_set is inclusive
lowerCamelCase_ = list(_flat_idx_to_idx(flat_end - 1 , lowercase ) )
# Get an ordered list of slices to perform
lowerCamelCase_ = _get_minimal_slice_set(
lowercase , lowercase , lowercase , )
lowerCamelCase_ = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def _SCREAMING_SNAKE_CASE ( lowercase : Callable , lowercase : Dict[str, Any] , lowercase : int , lowercase : int , lowercase : bool = False , lowercase : Any = None , lowercase : bool = False , ):
'''simple docstring'''
if not (len(lowercase ) > 0):
raise ValueError('Must provide at least one input' )
lowerCamelCase_ = [shape[:no_batch_dims] for shape in _fetch_dims(lowercase )]
lowerCamelCase_ = tuple([max(lowercase ) for s in zip(*lowercase )] )
def _prep_inputs(lowercase : torch.Tensor ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
lowerCamelCase_ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
lowerCamelCase_ = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
lowerCamelCase_ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
lowerCamelCase_ = tensor_tree_map(_prep_inputs , lowercase )
lowerCamelCase_ = None
if _out is not None:
lowerCamelCase_ = tensor_tree_map(lambda lowercase : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
lowerCamelCase_ = 1
for d in orig_batch_dims:
flat_batch_dim *= d
lowerCamelCase_ = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(lowercase : torch.Tensor ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
lowerCamelCase_ = 0
lowerCamelCase_ = prepped_outputs
for _ in range(lowercase ):
# Chunk the input
if not low_mem:
lowerCamelCase_ = _select_chunk
else:
lowerCamelCase_ = partial(
_chunk_slice , flat_start=lowercase , flat_end=min(lowercase , i + chunk_size ) , no_batch_dims=len(lowercase ) , )
lowerCamelCase_ = tensor_tree_map(lowercase , lowercase )
# Run the layer on the chunk
lowerCamelCase_ = layer(**lowercase )
# Allocate space for the output
if out is None:
lowerCamelCase_ = tensor_tree_map(lambda lowercase : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , lowercase )
# Put the chunk in its pre-allocated space
if isinstance(lowercase , lowercase ):
def assign(lowercase : dict , lowercase : dict ) -> None:
for k, v in da.items():
if isinstance(lowercase , lowercase ):
assign(lowercase , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
lowerCamelCase_ = da[k]
assign(lowercase , lowercase )
elif isinstance(lowercase , lowercase ):
for xa, xa in zip(lowercase , lowercase ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
lowerCamelCase_ = xa
elif isinstance(lowercase , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
lowerCamelCase_ = output_chunk
else:
raise ValueError('Not supported' )
i += chunk_size
lowerCamelCase_ = tensor_tree_map(lambda lowercase : t.view(orig_batch_dims + t.shape[1:] ) , lowercase )
return out
class A:
'''simple docstring'''
def __init__( self : Optional[Any] , A_ : int = 512 , ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = max_chunk_size
lowerCamelCase_ = None
lowerCamelCase_ = None
def a__ ( self : int , A_ : Callable , A_ : tuple , A_ : int ) -> int:
"""simple docstring"""
logging.info('Tuning chunk size...' )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
lowerCamelCase_ = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
lowerCamelCase_ = [c for c in candidates if c > min_chunk_size]
lowerCamelCase_ = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(A_ : int ) -> bool:
try:
with torch.no_grad():
fn(*A_ , chunk_size=A_ )
return True
except RuntimeError:
return False
lowerCamelCase_ = 0
lowerCamelCase_ = len(A_ ) - 1
while i > min_viable_chunk_size_index:
lowerCamelCase_ = test_chunk_size(candidates[i] )
if not viable:
lowerCamelCase_ = (min_viable_chunk_size_index + i) // 2
else:
lowerCamelCase_ = i
lowerCamelCase_ = (i + len(A_ ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def a__ ( self : List[str] , A_ : Iterable , A_ : Iterable ) -> bool:
"""simple docstring"""
lowerCamelCase_ = True
for aa, aa in zip(A_ , A_ ):
assert type(A_ ) == type(A_ )
if isinstance(A_ , (list, tuple) ):
consistent &= self._compare_arg_caches(A_ , A_ )
elif isinstance(A_ , A_ ):
lowerCamelCase_ = [v for _, v in sorted(aa.items() , key=lambda A_ : x[0] )]
lowerCamelCase_ = [v for _, v in sorted(aa.items() , key=lambda A_ : x[0] )]
consistent &= self._compare_arg_caches(A_ , A_ )
else:
consistent &= aa == aa
return consistent
def a__ ( self : int , A_ : Callable , A_ : tuple , A_ : int , ) -> int:
"""simple docstring"""
lowerCamelCase_ = True
lowerCamelCase_ = tree_map(lambda A_ : a.shape if isinstance(A_ , torch.Tensor ) else a , A_ , A_ )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(A_ )
lowerCamelCase_ = self._compare_arg_caches(self.cached_arg_data , A_ )
else:
# Otherwise, we can reuse the precomputed value
lowerCamelCase_ = False
if not consistent:
lowerCamelCase_ = self._determine_favorable_chunk_size(
A_ , A_ , A_ , )
lowerCamelCase_ = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 204 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
# See all MVP models at https://huggingface.co/models?filter=mvp
__snake_case = {
"""vocab_file""": {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
},
"""added_tokens.json""": {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
},
"""merges_file""": {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
},
}
__snake_case = {
"""RUCAIBox/mvp""": 1024,
}
class _lowerCAmelCase ( a__ ):
__UpperCAmelCase : int = VOCAB_FILES_NAMES
__UpperCAmelCase : Any = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : Optional[Any] = ['''input_ids''', '''attention_mask''']
__UpperCAmelCase : Any = MvpTokenizer
def __init__( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__="replace" , UpperCamelCase__="<s>" , UpperCamelCase__="</s>" , UpperCamelCase__="</s>" , UpperCamelCase__="<s>" , UpperCamelCase__="<unk>" , UpperCamelCase__="<pad>" , UpperCamelCase__="<mask>" , UpperCamelCase__=False , UpperCamelCase__=True , **UpperCamelCase__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(
UpperCamelCase__ , UpperCamelCase__ , tokenizer_file=UpperCamelCase__ , errors=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , mask_token=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ , trim_offsets=UpperCamelCase__ , **UpperCamelCase__ , )
snake_case : str = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , UpperCamelCase__ ) != add_prefix_space:
snake_case : List[str] = getattr(UpperCamelCase__ , pre_tok_state.pop("type" ) )
snake_case : Dict = add_prefix_space
snake_case : str = pre_tok_class(**UpperCamelCase__ )
snake_case : Union[str, Any] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
snake_case : int = "post_processor"
snake_case : List[str] = getattr(self.backend_tokenizer , UpperCamelCase__ , UpperCamelCase__ )
if tokenizer_component_instance:
snake_case : Union[str, Any] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
snake_case : List[str] = tuple(state["sep"] )
if "cls" in state:
snake_case : Dict = tuple(state["cls"] )
snake_case : str = False
if state.get("add_prefix_space" , UpperCamelCase__ ) != add_prefix_space:
snake_case : List[str] = add_prefix_space
snake_case : Optional[Any] = True
if state.get("trim_offsets" , UpperCamelCase__ ) != trim_offsets:
snake_case : Dict = trim_offsets
snake_case : Tuple = True
if changes_to_apply:
snake_case : List[Any] = getattr(UpperCamelCase__ , state.pop("type" ) )
snake_case : Union[str, Any] = component_class(**UpperCamelCase__ )
setattr(self.backend_tokenizer , UpperCamelCase__ , UpperCamelCase__ )
@property
def lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def lowerCamelCase ( self , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
snake_case : Dict = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else value
snake_case : List[str] = value
def lowerCamelCase ( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
snake_case : List[str] = kwargs.get("is_split_into_words" , UpperCamelCase__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs." )
return super()._batch_encode_plus(*UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase ( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
snake_case : Optional[Any] = kwargs.get("is_split_into_words" , UpperCamelCase__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs." )
return super()._encode_plus(*UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Any:
'''simple docstring'''
snake_case : Tuple = self._tokenizer.model.save(UpperCamelCase__ , name=UpperCamelCase__ )
return tuple(UpperCamelCase__ )
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__=None ) -> Any:
'''simple docstring'''
snake_case : Dict = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> int:
'''simple docstring'''
snake_case : Union[str, Any] = [self.sep_token_id]
snake_case : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 370 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__snake_case = logging.getLogger(__name__)
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser(
description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
)
parser.add_argument(
"""--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
)
parser.add_argument(
"""--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
)
parser.add_argument("""--vocab_size""", default=30522, type=int)
__snake_case = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, """rb""") as fp:
__snake_case = pickle.load(fp)
logger.info("""Counting occurrences for MLM.""")
__snake_case = Counter()
for tk_ids in data:
counter.update(tk_ids)
__snake_case = [0] * args.vocab_size
for k, v in counter.items():
__snake_case = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, """wb""") as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 112 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = [
'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
'WavLMForAudioFrameClassification',
'WavLMForCTC',
'WavLMForSequenceClassification',
'WavLMForXVector',
'WavLMModel',
'WavLMPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavlm import (
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
WavLMPreTrainedModel,
)
else:
import sys
lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 188 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
__a : int = int(number**0.5 )
return number == sq * sq
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
__a : int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
__a : int = x_den * y_den * z_den
__a : int = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
top //= hcf
bottom //= hcf
return top, bottom
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = 35 ):
__a : set = set()
__a : int
__a : Fraction = Fraction(0 )
__a : tuple[int, int]
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
__a : Union[str, Any] = x_num * y_den + x_den * y_num
__a : Optional[Any] = x_den * y_den
__a : int = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__a : Any = add_three(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
unique_s.add(_SCREAMING_SNAKE_CASE )
# n=2
__a : Optional[int] = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
__a : Union[str, Any] = x_den * x_den * y_den * y_den
if is_sq(_SCREAMING_SNAKE_CASE ) and is_sq(_SCREAMING_SNAKE_CASE ):
__a : List[Any] = int(sqrt(_SCREAMING_SNAKE_CASE ) )
__a : Any = int(sqrt(_SCREAMING_SNAKE_CASE ) )
__a : Optional[int] = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__a : List[Any] = add_three(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
unique_s.add(_SCREAMING_SNAKE_CASE )
# n=-1
__a : int = x_num * y_num
__a : Optional[Any] = x_den * y_num + x_num * y_den
__a : Tuple = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__a : Any = add_three(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
unique_s.add(_SCREAMING_SNAKE_CASE )
# n=2
__a : List[Any] = x_num * x_num * y_num * y_num
__a : List[Any] = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(_SCREAMING_SNAKE_CASE ) and is_sq(_SCREAMING_SNAKE_CASE ):
__a : Optional[Any] = int(sqrt(_SCREAMING_SNAKE_CASE ) )
__a : Union[str, Any] = int(sqrt(_SCREAMING_SNAKE_CASE ) )
__a : int = gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__a : List[str] = add_three(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
unique_s.add(_SCREAMING_SNAKE_CASE )
for num, den in unique_s:
total += Fraction(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return total.denominator + total.numerator
if __name__ == "__main__":
print(f'''{solution() = }''')
| 27 | 0 |
'''simple docstring'''
import functools
def _A ( snake_case , snake_case ) -> int:
_lowercase : List[str] = len(snake_case )
_lowercase : Any = len(snake_case )
@functools.cache
def min_distance(snake_case , snake_case ) -> int:
# if first word index is overflow - delete all from the second word
if indexa >= len_worda:
return len_worda - indexa
# if second word index is overflow - delete all from the first word
if indexa >= len_worda:
return len_worda - indexa
_lowercase : Tuple = int(worda[indexa] != worda[indexa] ) # current letters not identical
return min(
1 + min_distance(indexa + 1 , snake_case ) , 1 + min_distance(snake_case , indexa + 1 ) , diff + min_distance(indexa + 1 , indexa + 1 ) , )
return min_distance(0 , 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 199 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class a__ ( lowerCamelCase_ ):
_SCREAMING_SNAKE_CASE : Any = 'roc_bert'
def __init__( self , _UpperCamelCase=30522 , _UpperCamelCase=768 , _UpperCamelCase=12 , _UpperCamelCase=12 , _UpperCamelCase=3072 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=512 , _UpperCamelCase=2 , _UpperCamelCase=0.0_2 , _UpperCamelCase=1E-1_2 , _UpperCamelCase=True , _UpperCamelCase=0 , _UpperCamelCase="absolute" , _UpperCamelCase=None , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=768 , _UpperCamelCase=910 , _UpperCamelCase=512 , _UpperCamelCase=24858 , _UpperCamelCase=True , **_UpperCamelCase , ):
"""simple docstring"""
_lowercase : str = vocab_size
_lowercase : List[str] = max_position_embeddings
_lowercase : List[Any] = hidden_size
_lowercase : Dict = num_hidden_layers
_lowercase : str = num_attention_heads
_lowercase : int = intermediate_size
_lowercase : Optional[Any] = hidden_act
_lowercase : Union[str, Any] = hidden_dropout_prob
_lowercase : Dict = attention_probs_dropout_prob
_lowercase : Dict = initializer_range
_lowercase : List[Any] = type_vocab_size
_lowercase : Tuple = layer_norm_eps
_lowercase : Optional[int] = use_cache
_lowercase : Tuple = enable_pronunciation
_lowercase : Optional[int] = enable_shape
_lowercase : int = pronunciation_embed_dim
_lowercase : List[str] = pronunciation_vocab_size
_lowercase : int = shape_embed_dim
_lowercase : str = shape_vocab_size
_lowercase : str = concat_input
_lowercase : Dict = position_embedding_type
_lowercase : Optional[Any] = classifier_dropout
super().__init__(pad_token_id=_UpperCamelCase , **_UpperCamelCase )
| 199 | 1 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class lowercase__( __lowercase , unittest.TestCase ):
"""simple docstring"""
a :Dict = RoCBertTokenizer
a :Union[str, Any] = None
a :Optional[int] = False
a :List[Any] = True
a :str = filter_non_english
def _lowercase ( self : Optional[int] ) -> int:
super().setUp()
lowercase_ = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
lowercase_ = {}
lowercase_ = {}
for i, value in enumerate(SCREAMING_SNAKE_CASE_ ):
lowercase_ = i
lowercase_ = i
lowercase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
lowercase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''word_shape_file'''] )
lowercase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''word_pronunciation_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
with open(self.word_shape_file , '''w''' , encoding='''utf-8''' ) as word_shape_writer:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ )
with open(self.word_pronunciation_file , '''w''' , encoding='''utf-8''' ) as word_pronunciation_writer:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Union[str, Any] ) -> Any:
lowercase_ = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
lowercase_ = tokenizer.tokenize('''你好[SEP]你是谁''' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['''你''', '''好''', '''[SEP]''', '''你''', '''是''', '''谁'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE_ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE_ ) , [5, 6, 2, 5, 7, 8] )
def _lowercase ( self : Optional[Any] ) -> List[str]:
lowercase_ = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''' ) , ['''ah''', '''\u535A''', '''\u63A8''', '''zz'''] )
def _lowercase ( self : str ) -> Union[str, Any]:
lowercase_ = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def _lowercase ( self : int ) -> Dict:
lowercase_ = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''h\u00E9llo'''] )
def _lowercase ( self : List[Any] ) -> Dict:
lowercase_ = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def _lowercase ( self : Optional[Any] ) -> Optional[Any]:
lowercase_ = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def _lowercase ( self : int ) -> Dict:
lowercase_ = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def _lowercase ( self : Optional[int] ) -> List[Any]:
lowercase_ = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def _lowercase ( self : int ) -> Optional[Any]:
lowercase_ = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def _lowercase ( self : Optional[Any] ) -> int:
lowercase_ = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , never_split=['''[UNK]'''] )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? [UNK]''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''] )
def _lowercase ( self : Union[str, Any] ) -> List[Any]:
lowercase_ = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
lowercase_ = {}
for i, token in enumerate(SCREAMING_SNAKE_CASE_ ):
lowercase_ = i
lowercase_ = RoCBertWordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE_ , 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'''] )
def _lowercase ( self : Optional[int] ) -> Dict:
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 _lowercase ( self : Dict ) -> Optional[int]:
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 _lowercase ( self : Union[str, Any] ) -> Tuple:
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(''' ''' ) )
def _lowercase ( self : Tuple ) -> List[str]:
lowercase_ = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) for t in ['''Test''', '''\xad''', '''test''']] , [['''[UNK]'''], [], ['''[UNK]''']] )
if self.test_rust_tokenizer:
lowercase_ = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) for t in ['''Test''', '''\xad''', '''test''']] , [['''[UNK]'''], [], ['''[UNK]''']] )
def _lowercase ( self : Optional[Any] ) -> int:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase_ = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
lowercase_ = tokenizer_r.encode_plus(
SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ , return_offsets_mapping=SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , )
lowercase_ = tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE_ , '''do_lower_case''' ) else False
lowercase_ = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 1_5), tokenizer_r.mask_token),
((1_6, 2_1), "Allen"),
((2_1, 2_3), "##NL"),
((2_3, 2_4), "##P"),
((2_5, 3_3), "sentence"),
((3_3, 3_4), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 1_5), tokenizer_r.mask_token),
((1_6, 2_1), "allen"),
((2_1, 2_3), "##nl"),
((2_3, 2_4), "##p"),
((2_5, 3_3), "sentence"),
((3_3, 3_4), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['''input_ids'''] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['''offset_mapping'''] )
def _lowercase ( self : int ) -> Optional[int]:
lowercase_ = ["的", "人", "有"]
lowercase_ = "".join(SCREAMING_SNAKE_CASE_ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase_ = True
lowercase_ = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer_p.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer_r.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase_ = False
lowercase_ = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer_r.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer_p.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
# it is expected that only the first Chinese character is not preceded by "##".
lowercase_ = [
f'''##{token}''' if idx != 0 else token for idx, token in enumerate(SCREAMING_SNAKE_CASE_ )
]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def _lowercase ( self : List[Any] ) -> Optional[int]:
lowercase_ = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
lowercase_ = tokenizer.encode('''你好''' , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer.encode('''你是谁''' , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def _lowercase ( self : List[str] ) -> Any:
lowercase_ = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE_ )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
lowercase_ = "你好，你是谁"
lowercase_ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer.prepare_for_model(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer.encode_plus(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 30 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Any = len(_lowerCamelCase)
# We need to create solution object to save path.
__UpperCamelCase : List[str] = [[0 for _ in range(_lowerCamelCase)] for _ in range(_lowerCamelCase)]
__UpperCamelCase : Optional[int] = run_maze(_lowerCamelCase , 0 , 0 , _lowerCamelCase)
if solved:
print("\n".join(str(_lowerCamelCase) for row in solutions))
else:
print("No solution exists!")
return solved
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]] , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Tuple = len(_lowerCamelCase)
# Final check point.
if i == j == (size - 1):
__UpperCamelCase : Optional[int] = 1
return True
__UpperCamelCase : List[Any] = (not i < 0) and (not j < 0) # Check lower bounds
__UpperCamelCase : List[str] = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
__UpperCamelCase : int = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
__UpperCamelCase : Tuple = 1
# check for directions
if (
run_maze(_lowerCamelCase , i + 1 , _lowerCamelCase , _lowerCamelCase)
or run_maze(_lowerCamelCase , _lowerCamelCase , j + 1 , _lowerCamelCase)
or run_maze(_lowerCamelCase , i - 1 , _lowerCamelCase , _lowerCamelCase)
or run_maze(_lowerCamelCase , _lowerCamelCase , j - 1 , _lowerCamelCase)
):
return True
__UpperCamelCase : Tuple = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod() | 232 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from datasets import load_dataset
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 BeitImageProcessor
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=7 , __UpperCAmelCase=3 , __UpperCAmelCase=18 , __UpperCAmelCase=30 , __UpperCAmelCase=400 , __UpperCAmelCase=True , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase=[0.5, 0.5, 0.5] , __UpperCAmelCase=[0.5, 0.5, 0.5] , __UpperCAmelCase=False , ) -> int:
_a = size if size is not None else {'''height''': 20, '''width''': 20}
_a = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
_a = parent
_a = batch_size
_a = num_channels
_a = image_size
_a = min_resolution
_a = max_resolution
_a = do_resize
_a = size
_a = do_center_crop
_a = crop_size
_a = do_normalize
_a = image_mean
_a = image_std
_a = do_reduce_labels
def _UpperCAmelCase ( self ) -> Dict:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def A_ ( ):
"""simple docstring"""
_a = load_dataset('''hf-internal-testing/fixtures_ade20k''', split='''test''' )
_a = Image.open(dataset[0]['''file'''] )
_a = Image.open(dataset[1]['''file'''] )
return image, map
def A_ ( ):
"""simple docstring"""
_a = load_dataset('''hf-internal-testing/fixtures_ade20k''', split='''test''' )
_a = Image.open(ds[0]['''file'''] )
_a = Image.open(ds[1]['''file'''] )
_a = Image.open(ds[2]['''file'''] )
_a = Image.open(ds[3]['''file'''] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class __lowerCamelCase ( a__ , unittest.TestCase ):
'''simple docstring'''
A_ : Dict = BeitImageProcessor if is_vision_available() else None
def _UpperCAmelCase ( self ) -> Optional[int]:
_a = BeitImageProcessingTester(self )
@property
def _UpperCAmelCase ( self ) -> List[str]:
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCAmelCase ( self ) -> Optional[Any]:
_a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_resize''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''size''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_center_crop''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''center_crop''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''image_mean''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''image_std''' ) )
def _UpperCAmelCase ( self ) -> List[str]:
_a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 20, '''width''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
self.assertEqual(image_processor.do_reduce_labels , __UpperCAmelCase )
_a = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=__UpperCAmelCase )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
self.assertEqual(image_processor.do_reduce_labels , __UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Any:
pass
def _UpperCAmelCase ( self ) -> int:
# Initialize image_processing
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , Image.Image )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
_a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def _UpperCAmelCase ( self ) -> List[str]:
# Initialize image_processing
_a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , numpify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , np.ndarray )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
_a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def _UpperCAmelCase ( self ) -> Optional[int]:
# Initialize image_processing
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , torchify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
_a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def _UpperCAmelCase ( self ) -> List[Any]:
# Initialize image_processing
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , torchify=__UpperCAmelCase )
_a = []
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
_a = image_processing(image_inputs[0] , maps[0] , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched
_a = image_processing(__UpperCAmelCase , __UpperCAmelCase , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test not batched input (PIL images)
_a , _a = prepare_semantic_single_inputs()
_a = image_processing(__UpperCAmelCase , __UpperCAmelCase , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched input (PIL images)
_a , _a = prepare_semantic_batch_inputs()
_a = image_processing(__UpperCAmelCase , __UpperCAmelCase , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
2,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
def _UpperCAmelCase ( self ) -> List[Any]:
# Initialize image_processing
_a = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
_a , _a = prepare_semantic_single_inputs()
_a = image_processing(__UpperCAmelCase , __UpperCAmelCase , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 150 )
_a = True
_a = image_processing(__UpperCAmelCase , __UpperCAmelCase , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 ) | 153 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__snake_case = datasets.load_iris()
__snake_case = np.array(data['''data'''])
__snake_case = np.array(data['''target'''])
__snake_case = data['''target_names''']
__snake_case ,__snake_case ,__snake_case ,__snake_case = train_test_split(X, y)
def A_ ( _lowerCAmelCase : int, _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
return np.linalg.norm(np.array(_lowerCAmelCase ) - np.array(_lowerCAmelCase ) )
def A_ ( _lowerCAmelCase : Optional[int], _lowerCAmelCase : Tuple, _lowerCAmelCase : Optional[Any], _lowerCAmelCase : int, _lowerCAmelCase : str=5 ):
"""simple docstring"""
_a = zip(_lowerCAmelCase, _lowerCAmelCase )
# List of distances of all points from the point to be classified
_a = []
for data_point in data:
_a = euclidean_distance(data_point[0], _lowerCAmelCase )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
_a = [i[1] for i in sorted(_lowerCAmelCase )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
_a = Counter(_lowerCAmelCase ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4])) | 153 | 1 |
from math import factorial
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int = 1_00 ):
return sum(map(SCREAMING_SNAKE_CASE__ , str(factorial(SCREAMING_SNAKE_CASE__ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 62 |
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int = 10**12 ):
__UpperCamelCase =1
__UpperCamelCase =0
__UpperCamelCase =1
__UpperCamelCase =1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev_denominator += 2 * denominator
denominator += 2 * prev_denominator
return (denominator + 1) // 2
if __name__ == "__main__":
print(f"""{solution() = }""")
| 62 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class UpperCamelCase_ ( a_ , a_ , unittest.TestCase ):
_A : Any = IFInpaintingPipeline
_A : Optional[int] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
_A : str = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
_A : Optional[Any] = PipelineTesterMixin.required_optional_params - {'latents'}
def UpperCamelCase_ ( self ) -> int:
"""simple docstring"""
return self._get_dummy_components()
def UpperCamelCase_ ( self , snake_case__ , snake_case__=0 ) -> Union[str, Any]:
"""simple docstring"""
if str(snake_case__ ).startswith("""mps""" ):
UpperCAmelCase = torch.manual_seed(snake_case__ )
else:
UpperCAmelCase = torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
UpperCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(snake_case__ ) ).to(snake_case__ )
UpperCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(snake_case__ ) ).to(snake_case__ )
UpperCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def UpperCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def UpperCamelCase_ ( self ) -> Any:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def UpperCamelCase_ ( self ) -> Any:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1e-1 )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def UpperCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
self._test_save_load_local()
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 351 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
lowerCAmelCase_ : Tuple = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=False , ):
'''simple docstring'''
output_path.parent.mkdir(parents=lowerCAmelCase , exist_ok=lowerCAmelCase )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
lowerCAmelCase , lowerCAmelCase , f=output_path.as_posix() , input_names=lowerCAmelCase , output_names=lowerCAmelCase , dynamic_axes=lowerCAmelCase , do_constant_folding=lowerCAmelCase , use_external_data_format=lowerCAmelCase , enable_onnx_checker=lowerCAmelCase , opset_version=lowerCAmelCase , )
else:
export(
lowerCAmelCase , lowerCAmelCase , f=output_path.as_posix() , input_names=lowerCAmelCase , output_names=lowerCAmelCase , dynamic_axes=lowerCAmelCase , do_constant_folding=lowerCAmelCase , opset_version=lowerCAmelCase , )
@torch.no_grad()
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = False ):
'''simple docstring'''
UpperCAmelCase = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
UpperCAmelCase = """cuda"""
elif fpaa and not torch.cuda.is_available():
raise ValueError("""`float16` model export is only supported on GPUs with CUDA""" )
else:
UpperCAmelCase = """cpu"""
UpperCAmelCase = StableDiffusionPipeline.from_pretrained(lowerCAmelCase , torch_dtype=lowerCAmelCase ).to(lowerCAmelCase )
UpperCAmelCase = Path(lowerCAmelCase )
# TEXT ENCODER
UpperCAmelCase = pipeline.text_encoder.config.max_position_embeddings
UpperCAmelCase = pipeline.text_encoder.config.hidden_size
UpperCAmelCase = pipeline.tokenizer(
"""A sample prompt""" , padding="""max_length""" , max_length=pipeline.tokenizer.model_max_length , truncation=lowerCAmelCase , return_tensors="""pt""" , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=lowerCAmelCase , dtype=torch.intaa )) , output_path=output_path / """text_encoder""" / """model.onnx""" , ordered_input_names=["""input_ids"""] , output_names=["""last_hidden_state""", """pooler_output"""] , dynamic_axes={
"""input_ids""": {0: """batch""", 1: """sequence"""},
} , opset=lowerCAmelCase , )
del pipeline.text_encoder
# UNET
UpperCAmelCase = pipeline.unet.config.in_channels
UpperCAmelCase = pipeline.unet.config.sample_size
UpperCAmelCase = output_path / """unet""" / """model.onnx"""
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ).to(device=lowerCAmelCase , dtype=lowerCAmelCase ),
torch.randn(2 ).to(device=lowerCAmelCase , dtype=lowerCAmelCase ),
torch.randn(2 , lowerCAmelCase , lowerCAmelCase ).to(device=lowerCAmelCase , dtype=lowerCAmelCase ),
False,
) , output_path=lowerCAmelCase , ordered_input_names=["""sample""", """timestep""", """encoder_hidden_states""", """return_dict"""] , output_names=["""out_sample"""] , dynamic_axes={
"""sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
"""timestep""": {0: """batch"""},
"""encoder_hidden_states""": {0: """batch""", 1: """sequence"""},
} , opset=lowerCAmelCase , use_external_data_format=lowerCAmelCase , )
UpperCAmelCase = str(unet_path.absolute().as_posix() )
UpperCAmelCase = os.path.dirname(lowerCAmelCase )
UpperCAmelCase = onnx.load(lowerCAmelCase )
# clean up existing tensor files
shutil.rmtree(lowerCAmelCase )
os.mkdir(lowerCAmelCase )
# collate external tensor files into one
onnx.save_model(
lowerCAmelCase , lowerCAmelCase , save_as_external_data=lowerCAmelCase , all_tensors_to_one_file=lowerCAmelCase , location="""weights.pb""" , convert_attribute=lowerCAmelCase , )
del pipeline.unet
# VAE ENCODER
UpperCAmelCase = pipeline.vae
UpperCAmelCase = vae_encoder.config.in_channels
UpperCAmelCase = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
UpperCAmelCase = lambda lowerCAmelCase , lowerCAmelCase : vae_encoder.encode(lowerCAmelCase , lowerCAmelCase )[0].sample()
onnx_export(
lowerCAmelCase , model_args=(
torch.randn(1 , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ).to(device=lowerCAmelCase , dtype=lowerCAmelCase ),
False,
) , output_path=output_path / """vae_encoder""" / """model.onnx""" , ordered_input_names=["""sample""", """return_dict"""] , output_names=["""latent_sample"""] , dynamic_axes={
"""sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
} , opset=lowerCAmelCase , )
# VAE DECODER
UpperCAmelCase = pipeline.vae
UpperCAmelCase = vae_decoder.config.latent_channels
UpperCAmelCase = vae_decoder.config.out_channels
# forward only through the decoder part
UpperCAmelCase = vae_encoder.decode
onnx_export(
lowerCAmelCase , model_args=(
torch.randn(1 , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ).to(device=lowerCAmelCase , dtype=lowerCAmelCase ),
False,
) , output_path=output_path / """vae_decoder""" / """model.onnx""" , ordered_input_names=["""latent_sample""", """return_dict"""] , output_names=["""sample"""] , dynamic_axes={
"""latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
} , opset=lowerCAmelCase , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
UpperCAmelCase = pipeline.safety_checker
UpperCAmelCase = safety_checker.config.vision_config.num_channels
UpperCAmelCase = safety_checker.config.vision_config.image_size
UpperCAmelCase = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , ).to(device=lowerCAmelCase , dtype=lowerCAmelCase ),
torch.randn(1 , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ).to(device=lowerCAmelCase , dtype=lowerCAmelCase ),
) , output_path=output_path / """safety_checker""" / """model.onnx""" , ordered_input_names=["""clip_input""", """images"""] , output_names=["""out_images""", """has_nsfw_concepts"""] , dynamic_axes={
"""clip_input""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
"""images""": {0: """batch""", 1: """height""", 2: """width""", 3: """channels"""},
} , opset=lowerCAmelCase , )
del pipeline.safety_checker
UpperCAmelCase = OnnxRuntimeModel.from_pretrained(output_path / """safety_checker""" )
UpperCAmelCase = pipeline.feature_extractor
else:
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / """vae_encoder""" ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / """vae_decoder""" ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / """text_encoder""" ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / """unet""" ) , scheduler=pipeline.scheduler , safety_checker=lowerCAmelCase , feature_extractor=lowerCAmelCase , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(lowerCAmelCase )
print("""ONNX pipeline saved to""" , lowerCAmelCase )
del pipeline
del onnx_pipeline
UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(lowerCAmelCase , provider="""CPUExecutionProvider""" )
print("""ONNX pipeline is loadable""" )
if __name__ == "__main__":
lowerCAmelCase_ : Tuple = argparse.ArgumentParser()
parser.add_argument(
'''--model_path''',
type=str,
required=True,
help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
)
parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--opset''',
default=1_4,
type=int,
help='''The version of the ONNX operator set to use.''',
)
parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')
lowerCAmelCase_ : Union[str, Any] = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 248 | 0 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase ( __lowerCamelCase , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ = CodeGenTokenizer
SCREAMING_SNAKE_CASE_ = CodeGenTokenizerFast
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = {"add_prefix_space": True}
SCREAMING_SNAKE_CASE_ = False
def a_ ( self) -> Tuple:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
'<|endoftext|>',
]
snake_case_ = dict(zip(SCREAMING_SNAKE_CASE_, range(len(SCREAMING_SNAKE_CASE_))))
snake_case_ = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
snake_case_ = {'unk_token': '<unk>'}
snake_case_ = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
snake_case_ = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file'])
with open(self.vocab_file, 'w', encoding='utf-8') as fp:
fp.write(json.dumps(SCREAMING_SNAKE_CASE_) + '\n')
with open(self.merges_file, 'w', encoding='utf-8') as fp:
fp.write('\n'.join(SCREAMING_SNAKE_CASE_))
def a_ ( self, **lowerCAmelCase__) -> Optional[int]:
kwargs.update(self.special_tokens_map)
return CodeGenTokenizer.from_pretrained(self.tmpdirname, **SCREAMING_SNAKE_CASE_)
def a_ ( self, **lowerCAmelCase__) -> Union[str, Any]:
kwargs.update(self.special_tokens_map)
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname, **SCREAMING_SNAKE_CASE_)
def a_ ( self, lowerCAmelCase__) -> str:
snake_case_ = 'lower newer'
snake_case_ = 'lower newer'
return input_text, output_text
def a_ ( self) -> List[Any]:
snake_case_ = CodeGenTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
snake_case_ = 'lower newer'
snake_case_ = ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
snake_case_ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_, add_prefix_space=SCREAMING_SNAKE_CASE_)
self.assertListEqual(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_)
snake_case_ = tokens + [tokenizer.unk_token]
snake_case_ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_), SCREAMING_SNAKE_CASE_)
def a_ ( self) -> Optional[Any]:
if not self.test_rust_tokenizer:
return
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_rust_tokenizer(add_prefix_space=SCREAMING_SNAKE_CASE_)
snake_case_ = 'lower newer'
# Testing tokenization
snake_case_ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_, add_prefix_space=SCREAMING_SNAKE_CASE_)
snake_case_ = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_)
self.assertListEqual(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_)
# Testing conversion to ids without special tokens
snake_case_ = tokenizer.encode(SCREAMING_SNAKE_CASE_, add_special_tokens=SCREAMING_SNAKE_CASE_, add_prefix_space=SCREAMING_SNAKE_CASE_)
snake_case_ = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_, add_special_tokens=SCREAMING_SNAKE_CASE_)
self.assertListEqual(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_)
# Testing conversion to ids with special tokens
snake_case_ = self.get_rust_tokenizer(add_prefix_space=SCREAMING_SNAKE_CASE_)
snake_case_ = tokenizer.encode(SCREAMING_SNAKE_CASE_, add_prefix_space=SCREAMING_SNAKE_CASE_)
snake_case_ = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_)
self.assertListEqual(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_)
# Testing the unknown token
snake_case_ = tokens + [rust_tokenizer.unk_token]
snake_case_ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_), SCREAMING_SNAKE_CASE_)
def a_ ( self, *lowerCAmelCase__, **lowerCAmelCase__) -> Optional[int]:
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def a_ ( self, lowerCAmelCase__=15) -> List[str]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
snake_case_ = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_)
# Simple input
snake_case_ = 'This is a simple input'
snake_case_ = ['This is a simple input 1', 'This is a simple input 2']
snake_case_ = ('This is a simple input', 'This is a pair')
snake_case_ = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(SCREAMING_SNAKE_CASE_, tokenizer_r.encode, SCREAMING_SNAKE_CASE_, max_length=SCREAMING_SNAKE_CASE_, padding='max_length')
# Simple input
self.assertRaises(SCREAMING_SNAKE_CASE_, tokenizer_r.encode_plus, SCREAMING_SNAKE_CASE_, max_length=SCREAMING_SNAKE_CASE_, padding='max_length')
# Simple input
self.assertRaises(
SCREAMING_SNAKE_CASE_, tokenizer_r.batch_encode_plus, SCREAMING_SNAKE_CASE_, max_length=SCREAMING_SNAKE_CASE_, padding='max_length', )
# Pair input
self.assertRaises(SCREAMING_SNAKE_CASE_, tokenizer_r.encode, SCREAMING_SNAKE_CASE_, max_length=SCREAMING_SNAKE_CASE_, padding='max_length')
# Pair input
self.assertRaises(SCREAMING_SNAKE_CASE_, tokenizer_r.encode_plus, SCREAMING_SNAKE_CASE_, max_length=SCREAMING_SNAKE_CASE_, padding='max_length')
# Pair input
self.assertRaises(
SCREAMING_SNAKE_CASE_, tokenizer_r.batch_encode_plus, SCREAMING_SNAKE_CASE_, max_length=SCREAMING_SNAKE_CASE_, padding='max_length', )
def a_ ( self) -> List[Any]:
snake_case_ = CodeGenTokenizer.from_pretrained(self.tmpdirname, pad_token='<pad>')
# Simple input
snake_case_ = 'This is a simple input'
snake_case_ = ['This is a simple input looooooooong', 'This is a simple input']
snake_case_ = ('This is a simple input', 'This is a pair')
snake_case_ = [
('This is a simple input loooooong', 'This is a simple input'),
('This is a simple pair loooooong', 'This is a simple pair'),
]
snake_case_ = tokenizer.pad_token_id
snake_case_ = tokenizer(SCREAMING_SNAKE_CASE_, padding='max_length', max_length=30, return_tensors='np')
snake_case_ = tokenizer(SCREAMING_SNAKE_CASE_, padding=SCREAMING_SNAKE_CASE_, truncate=SCREAMING_SNAKE_CASE_, return_tensors='np')
snake_case_ = tokenizer(*SCREAMING_SNAKE_CASE_, padding='max_length', max_length=60, return_tensors='np')
snake_case_ = tokenizer(SCREAMING_SNAKE_CASE_, padding=SCREAMING_SNAKE_CASE_, truncate=SCREAMING_SNAKE_CASE_, return_tensors='np')
# s
# test single string max_length padding
self.assertEqual(out_s['input_ids'].shape[-1], 30)
self.assertTrue(pad_token_id in out_s['input_ids'])
self.assertTrue(0 in out_s['attention_mask'])
# s2
# test automatic padding
self.assertEqual(out_sa['input_ids'].shape[-1], 33)
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['input_ids'][0])
self.assertFalse(0 in out_sa['attention_mask'][0])
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['input_ids'][1])
self.assertTrue(0 in out_sa['attention_mask'][1])
# p
# test single pair max_length padding
self.assertEqual(out_p['input_ids'].shape[-1], 60)
self.assertTrue(pad_token_id in out_p['input_ids'])
self.assertTrue(0 in out_p['attention_mask'])
# p2
# test automatic padding pair
self.assertEqual(out_pa['input_ids'].shape[-1], 52)
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['input_ids'][0])
self.assertFalse(0 in out_pa['attention_mask'][0])
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['input_ids'][1])
self.assertTrue(0 in out_pa['attention_mask'][1])
def a_ ( self) -> str:
snake_case_ = '$$$'
snake_case_ = CodeGenTokenizer.from_pretrained(self.tmpdirname, bos_token=SCREAMING_SNAKE_CASE_, add_bos_token=SCREAMING_SNAKE_CASE_)
snake_case_ = 'This is a simple input'
snake_case_ = ['This is a simple input 1', 'This is a simple input 2']
snake_case_ = tokenizer.bos_token_id
snake_case_ = tokenizer(SCREAMING_SNAKE_CASE_)
snake_case_ = tokenizer(SCREAMING_SNAKE_CASE_)
self.assertEqual(out_s.input_ids[0], SCREAMING_SNAKE_CASE_)
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))
snake_case_ = tokenizer.decode(out_s.input_ids)
snake_case_ = tokenizer.batch_decode(out_sa.input_ids)
self.assertEqual(decode_s.split()[0], SCREAMING_SNAKE_CASE_)
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))
@slow
def a_ ( self) -> Optional[Any]:
snake_case_ = CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono')
snake_case_ = '\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'
snake_case_ = '\nif len_a > len_b: result = a\nelse: result = b'
snake_case_ = tokenizer.encode(SCREAMING_SNAKE_CASE_)
snake_case_ = ['^#', re.escape('<|endoftext|>'), '^\'\'\'', '^\"\"\"', '\n\n\n']
snake_case_ = tokenizer.decode(SCREAMING_SNAKE_CASE_, truncate_before_pattern=SCREAMING_SNAKE_CASE_)
self.assertEqual(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_)
def a_ ( self) -> Optional[int]:
pass
| 69 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowerCamelCase_ = ['''small''', '''medium''', '''large''']
lowerCamelCase_ = '''lm_head.decoder.weight'''
lowerCamelCase_ = '''lm_head.weight'''
def __magic_name__ ( __a : str , __a : str ):
'''simple docstring'''
UpperCamelCase__ = torch.load(__a )
UpperCamelCase__ = d.pop(__a )
os.makedirs(__a , exist_ok=__a )
torch.save(__a , os.path.join(__a , __a ) )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
lowerCamelCase_ = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
lowerCamelCase_ = os.path.join(args.dialogpt_path, f'{MODEL}_ft.pkl')
lowerCamelCase_ = f'./DialoGPT-{MODEL}'
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 244 | 0 |
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=1024 , lowerCAmelCase_=1024 , lowerCAmelCase_=False , **lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = SeqaSeqDataset(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , type_path="train" , **lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = tok.pad_token_id
def get_lens(lowerCAmelCase_ ):
__SCREAMING_SNAKE_CASE = tqdm(
DataLoader(lowerCAmelCase_ , batch_size=512 , num_workers=8 , shuffle=lowerCAmelCase_ , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
__SCREAMING_SNAKE_CASE = []
for batch in dl:
__SCREAMING_SNAKE_CASE = batch["input_ids"].ne(lowerCAmelCase_ ).sum(1 ).tolist()
__SCREAMING_SNAKE_CASE = batch["labels"].ne(lowerCAmelCase_ ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(lowerCAmelCase_ , lowerCAmelCase_ ):
max_lens.append(max(lowerCAmelCase_ , lowerCAmelCase_ ) )
else:
max_lens.extend(lowerCAmelCase_ )
return max_lens
__SCREAMING_SNAKE_CASE = get_lens(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = SeqaSeqDataset(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , type_path="val" , **lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = get_lens(lowerCAmelCase_ )
pickle_save(lowerCAmelCase_ , train_ds.len_file )
pickle_save(lowerCAmelCase_ , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 361 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(lowerCAmelCase_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 195 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}
class lowercase ( UpperCamelCase__ ):
_a = "xlnet"
_a = ["mems"]
_a = {
"n_token": "vocab_size", # Backward compatibility
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , _a=3_2000 , _a=1024 , _a=24 , _a=16 , _a=4096 , _a="gelu" , _a=True , _a="bi" , _a=0.02 , _a=1e-12 , _a=0.1 , _a=512 , _a=None , _a=True , _a=False , _a=False , _a=-1 , _a=False , _a="last" , _a=True , _a="tanh" , _a=0.1 , _a=5 , _a=5 , _a=5 , _a=1 , _a=2 , **_a , ) -> Optional[int]:
_A : Tuple = vocab_size
_A : int = d_model
_A : int = n_layer
_A : Union[str, Any] = n_head
if d_model % n_head != 0:
raise ValueError(F'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0''' )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
F'''`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})''' )
_A : Optional[int] = d_model // n_head
_A : Optional[int] = ff_activation
_A : Dict = d_inner
_A : Union[str, Any] = untie_r
_A : Any = attn_type
_A : Optional[int] = initializer_range
_A : Optional[Any] = layer_norm_eps
_A : List[Any] = dropout
_A : Optional[Any] = mem_len
_A : Union[str, Any] = reuse_len
_A : Tuple = bi_data
_A : Optional[int] = clamp_len
_A : Dict = same_length
_A : Dict = summary_type
_A : Optional[int] = summary_use_proj
_A : Union[str, Any] = summary_activation
_A : Optional[Any] = summary_last_dropout
_A : str = start_n_top
_A : List[Any] = end_n_top
_A : List[str] = bos_token_id
_A : List[Any] = pad_token_id
_A : str = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
"""The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"""
""" instead.""" , snake_case__ , )
_A : Dict = kwargs["use_cache"]
_A : str = use_mems_eval
_A : str = use_mems_train
super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
@property
def a__ ( self ) -> Optional[Any]:
logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
return -1
@max_position_embeddings.setter
def a__ ( self , _a ) -> List[str]:
raise NotImplementedError(
F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
| 26 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-question_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-reader-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-ctx_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
),
'''facebook/dpr-question_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
),
'''facebook/dpr-reader-multiset-base''': (
'''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : int ="dpr"
def __init__( self , snake_case__=30_522 , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3_072 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=2 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__=0 , snake_case__="absolute" , snake_case__ = 0 , **snake_case__ , ):
"""simple docstring"""
super().__init__(pad_token_id=snake_case__ , **snake_case__ )
lowerCAmelCase : Union[str, Any] = vocab_size
lowerCAmelCase : str = hidden_size
lowerCAmelCase : Any = num_hidden_layers
lowerCAmelCase : Optional[int] = num_attention_heads
lowerCAmelCase : Union[str, Any] = hidden_act
lowerCAmelCase : Dict = intermediate_size
lowerCAmelCase : Union[str, Any] = hidden_dropout_prob
lowerCAmelCase : Dict = attention_probs_dropout_prob
lowerCAmelCase : Dict = max_position_embeddings
lowerCAmelCase : Tuple = type_vocab_size
lowerCAmelCase : Any = initializer_range
lowerCAmelCase : Any = layer_norm_eps
lowerCAmelCase : Dict = projection_dim
lowerCAmelCase : Dict = position_embedding_type
| 108 | 0 |
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 lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="pt" )-> int:
"""simple docstring"""
UpperCamelCase_ = {"add_prefix_space": True} if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and not line.startswith(" " ) else {}
UpperCamelCase_ = padding_side
return tokenizer(
[line] , max_length=SCREAMING_SNAKE_CASE_ , padding="max_length" if pad_to_max_length else None , truncation=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , )-> Tuple:
"""simple docstring"""
UpperCamelCase_ = input_ids.ne(SCREAMING_SNAKE_CASE_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __magic_name__ ( snake_case ):
def __init__( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase="train" , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase="" , )-> Dict:
super().__init__()
UpperCamelCase_ = Path(_lowercase ).joinpath(type_path + ".source" )
UpperCamelCase_ = Path(_lowercase ).joinpath(type_path + ".target" )
UpperCamelCase_ = self.get_char_lens(self.src_file )
UpperCamelCase_ = max_source_length
UpperCamelCase_ = max_target_length
assert min(self.src_lens ) > 0, F"found empty line in {self.src_file}"
UpperCamelCase_ = tokenizer
UpperCamelCase_ = prefix
if n_obs is not None:
UpperCamelCase_ = self.src_lens[:n_obs]
UpperCamelCase_ = src_lang
UpperCamelCase_ = tgt_lang
def __len__( self )-> Tuple:
return len(self.src_lens )
def __getitem__( self , _lowercase )-> Dict[str, torch.Tensor]:
UpperCamelCase_ = index + 1 # linecache starts at 1
UpperCamelCase_ = self.prefix + linecache.getline(str(self.src_file ) , _lowercase ).rstrip("\n" )
UpperCamelCase_ = linecache.getline(str(self.tgt_file ) , _lowercase ).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 , _lowercase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
UpperCamelCase_ = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , _lowercase ) else self.tokenizer
)
UpperCamelCase_ = self.tokenizer.generator if isinstance(self.tokenizer , _lowercase ) else self.tokenizer
UpperCamelCase_ = encode_line(_lowercase , _lowercase , self.max_source_length , "right" )
UpperCamelCase_ = encode_line(_lowercase , _lowercase , self.max_target_length , "right" )
UpperCamelCase_ = source_inputs["input_ids"].squeeze()
UpperCamelCase_ = target_inputs["input_ids"].squeeze()
UpperCamelCase_ = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def UpperCAmelCase_ ( _lowercase )-> Any:
return [len(_lowercase ) for x in Path(_lowercase ).open().readlines()]
def UpperCAmelCase_ ( self , _lowercase )-> Dict[str, torch.Tensor]:
UpperCamelCase_ = torch.stack([x["input_ids"] for x in batch] )
UpperCamelCase_ = torch.stack([x["attention_mask"] for x in batch] )
UpperCamelCase_ = torch.stack([x["decoder_input_ids"] for x in batch] )
UpperCamelCase_ = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , _lowercase )
else self.tokenizer.pad_token_id
)
UpperCamelCase_ = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , _lowercase )
else self.tokenizer.pad_token_id
)
UpperCamelCase_ = trim_batch(_lowercase , _lowercase )
UpperCamelCase_ , UpperCamelCase_ = trim_batch(_lowercase , _lowercase , attention_mask=_lowercase )
UpperCamelCase_ = {
"input_ids": source_ids,
"attention_mask": source_mask,
"decoder_input_ids": y,
}
return batch
SCREAMING_SNAKE_CASE :List[str] = getLogger(__name__)
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> Optional[Any]:
"""simple docstring"""
return list(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_ ) )
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> None:
"""simple docstring"""
UpperCamelCase_ = get_git_info()
save_json(SCREAMING_SNAKE_CASE_ , os.path.join(SCREAMING_SNAKE_CASE_ , "git_log.json" ) )
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=4 , **SCREAMING_SNAKE_CASE_ )-> Tuple:
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE_ , "w" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , indent=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE_ ) as f:
return json.load(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase( )-> str:
"""simple docstring"""
UpperCamelCase_ = git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE_ )
UpperCamelCase_ = {
"repo_id": str(SCREAMING_SNAKE_CASE_ ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
"hostname": str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> List:
"""simple docstring"""
return list(map(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE_ , "wb" ) as f:
return pickle.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> Tuple:
"""simple docstring"""
def remove_articles(SCREAMING_SNAKE_CASE_ ):
return re.sub(r"\b(a|an|the)\b" , " " , SCREAMING_SNAKE_CASE_ )
def white_space_fix(SCREAMING_SNAKE_CASE_ ):
return " ".join(text.split() )
def remove_punc(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase_ = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(SCREAMING_SNAKE_CASE_ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_ ) ) ) )
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = normalize_answer(SCREAMING_SNAKE_CASE_ ).split()
UpperCamelCase_ = normalize_answer(SCREAMING_SNAKE_CASE_ ).split()
UpperCamelCase_ = Counter(SCREAMING_SNAKE_CASE_ ) & Counter(SCREAMING_SNAKE_CASE_ )
UpperCamelCase_ = sum(common.values() )
if num_same == 0:
return 0
UpperCamelCase_ = 1.0 * num_same / len(SCREAMING_SNAKE_CASE_ )
UpperCamelCase_ = 1.0 * num_same / len(SCREAMING_SNAKE_CASE_ )
UpperCamelCase_ = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> Tuple:
"""simple docstring"""
return normalize_answer(SCREAMING_SNAKE_CASE_ ) == normalize_answer(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> Dict:
"""simple docstring"""
assert len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ )
UpperCamelCase_ = 0
for hypo, pred in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
em += exact_match_score(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
em /= len(SCREAMING_SNAKE_CASE_ )
return {"em": em}
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> Dict:
"""simple docstring"""
return model_prefix.startswith("rag" )
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
UpperCamelCase_ = "dropout_rate"
for p in extra_params:
if getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and not hasattr(SCREAMING_SNAKE_CASE_ , equivalent_param[p] ):
logger.info("config doesn't have a `{}` attribute".format(SCREAMING_SNAKE_CASE_ ) )
delattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
continue
UpperCamelCase_ = p if hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else equivalent_param[p]
setattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
delattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return hparams, config
| 60 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False )-> str:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase_ = f"Expected string as input, found {type(SCREAMING_SNAKE_CASE_ )}"
raise ValueError(SCREAMING_SNAKE_CASE_ )
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase_ = f"Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE_ )}"
raise ValueError(SCREAMING_SNAKE_CASE_ )
UpperCamelCase_ = input_str.split("_" )
UpperCamelCase_ = 0 if use_pascal else 1
UpperCamelCase_ = words[start_index:]
UpperCamelCase_ = [word[0].upper() + word[1:] for word in words_to_capitalize]
UpperCamelCase_ = "" if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 60 | 1 |
'''simple docstring'''
import os
def _lowerCAmelCase ( _UpperCamelCase : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =len(grid[0] )
_SCREAMING_SNAKE_CASE =len(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(_UpperCamelCase ):
for j in range(n_rows - 3 ):
_SCREAMING_SNAKE_CASE =grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
_SCREAMING_SNAKE_CASE =grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
_SCREAMING_SNAKE_CASE =(
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
_SCREAMING_SNAKE_CASE =(
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
_SCREAMING_SNAKE_CASE =max(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if max_product > largest:
_SCREAMING_SNAKE_CASE =max_product
return largest
def _lowerCAmelCase ( ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =[]
with open(os.path.dirname(_UpperCamelCase ) + '/grid.txt' ) as file:
for line in file:
grid.append(line.strip('\n' ).split(' ' ) )
_SCREAMING_SNAKE_CASE =[[int(_UpperCamelCase ) for i in grid[j]] for j in range(len(_UpperCamelCase ) )]
return largest_product(_UpperCamelCase )
if __name__ == "__main__":
print(solution())
| 47 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : List[Any] = logging.get_logger(__name__)
lowerCamelCase : str = {
"huggingface/time-series-transformer-tourism-monthly": (
"https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class A__ ( A__ ):
A__ = 'time_series_transformer'
A__ = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
'num_hidden_layers': 'encoder_layers',
}
def __init__( self : Optional[int] , _a : Optional[int] = None , _a : Optional[int] = None , _a : str = "student_t" , _a : str = "nll" , _a : int = 1 , _a : List[int] = [1, 2, 3, 4, 5, 6, 7] , _a : Optional[Union[str, bool]] = "mean" , _a : int = 0 , _a : int = 0 , _a : int = 0 , _a : int = 0 , _a : Optional[List[int]] = None , _a : Optional[List[int]] = None , _a : int = 32 , _a : int = 32 , _a : int = 2 , _a : int = 2 , _a : int = 2 , _a : int = 2 , _a : bool = True , _a : str = "gelu" , _a : int = 64 , _a : float = 0.1 , _a : float = 0.1 , _a : float = 0.1 , _a : float = 0.1 , _a : float = 0.1 , _a : int = 100 , _a : float = 0.02 , _a : Union[str, Any]=True , **_a : Optional[Any] , ) -> Optional[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =prediction_length
_SCREAMING_SNAKE_CASE =context_length or prediction_length
_SCREAMING_SNAKE_CASE =distribution_output
_SCREAMING_SNAKE_CASE =loss
_SCREAMING_SNAKE_CASE =input_size
_SCREAMING_SNAKE_CASE =num_time_features
_SCREAMING_SNAKE_CASE =lags_sequence
_SCREAMING_SNAKE_CASE =scaling
_SCREAMING_SNAKE_CASE =num_dynamic_real_features
_SCREAMING_SNAKE_CASE =num_static_real_features
_SCREAMING_SNAKE_CASE =num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(_a ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
_SCREAMING_SNAKE_CASE =cardinality
else:
_SCREAMING_SNAKE_CASE =[0]
if embedding_dimension and num_static_categorical_features > 0:
if len(_a ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
_SCREAMING_SNAKE_CASE =embedding_dimension
else:
_SCREAMING_SNAKE_CASE =[min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
_SCREAMING_SNAKE_CASE =num_parallel_samples
# Transformer architecture configuration
_SCREAMING_SNAKE_CASE =input_size * len(_a ) + self._number_of_features
_SCREAMING_SNAKE_CASE =d_model
_SCREAMING_SNAKE_CASE =encoder_attention_heads
_SCREAMING_SNAKE_CASE =decoder_attention_heads
_SCREAMING_SNAKE_CASE =encoder_ffn_dim
_SCREAMING_SNAKE_CASE =decoder_ffn_dim
_SCREAMING_SNAKE_CASE =encoder_layers
_SCREAMING_SNAKE_CASE =decoder_layers
_SCREAMING_SNAKE_CASE =dropout
_SCREAMING_SNAKE_CASE =attention_dropout
_SCREAMING_SNAKE_CASE =activation_dropout
_SCREAMING_SNAKE_CASE =encoder_layerdrop
_SCREAMING_SNAKE_CASE =decoder_layerdrop
_SCREAMING_SNAKE_CASE =activation_function
_SCREAMING_SNAKE_CASE =init_std
_SCREAMING_SNAKE_CASE =use_cache
super().__init__(is_encoder_decoder=_a , **_a )
@property
def A ( self : List[Any] ) -> int:
'''simple docstring'''
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 47 | 1 |
'''simple docstring'''
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class lowerCAmelCase_ ( lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase_ : Dict = 42
lowerCAmelCase_ : List[Any] = None
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : str=0.9_99 , SCREAMING_SNAKE_CASE__ : Union[str, Any]="cosine" , ):
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(SCREAMING_SNAKE_CASE__ : List[str] ):
return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(SCREAMING_SNAKE_CASE__ : Dict ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
UpperCAmelCase__ = []
for i in range(a__ ):
UpperCAmelCase__ = i / num_diffusion_timesteps
UpperCAmelCase__ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(a__ ) / alpha_bar_fn(a__ ) , a__ ) )
return torch.tensor(a__ , dtype=torch.floataa )
class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase_ : Tuple = 1
@register_to_config
def __init__( self : int , _UpperCAmelCase : Optional[Any] = 10_00 , _UpperCAmelCase : List[Any] = 0.0001 , _UpperCAmelCase : Union[str, Any] = 0.02 , _UpperCAmelCase : Dict = "linear" , _UpperCAmelCase : List[str] = None , _UpperCAmelCase : Optional[Any] = True , _UpperCAmelCase : Any = True , _UpperCAmelCase : int = 0 , _UpperCAmelCase : Optional[Any] = "epsilon" , _UpperCAmelCase : Dict = 1.0 , **_UpperCAmelCase : Optional[int] , ):
"""simple docstring"""
if kwargs.get("""set_alpha_to_one""" , _snake_case ) is not None:
UpperCAmelCase__ = (
"""The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
)
deprecate("""set_alpha_to_one""" , """1.0.0""" , _snake_case , standard_warn=_snake_case )
UpperCAmelCase__ = kwargs["""set_alpha_to_one"""]
if trained_betas is not None:
UpperCAmelCase__ = torch.tensor(_snake_case , dtype=torch.floataa )
elif beta_schedule == "linear":
UpperCAmelCase__ = torch.linspace(_snake_case , _snake_case , _snake_case , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
UpperCAmelCase__ = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , _snake_case , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
UpperCAmelCase__ = betas_for_alpha_bar(_snake_case )
else:
raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )
UpperCAmelCase__ = 1.0 - self.betas
UpperCAmelCase__ = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
UpperCAmelCase__ = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
UpperCAmelCase__ = 1.0
# setable values
UpperCAmelCase__ = None
UpperCAmelCase__ = torch.from_numpy(np.arange(0 , _snake_case ).copy().astype(np.intaa ) )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple = None ):
"""simple docstring"""
return sample
def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] = None ):
"""simple docstring"""
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
f''' maximal {self.config.num_train_timesteps} timesteps.''' )
UpperCAmelCase__ = num_inference_steps
UpperCAmelCase__ = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
UpperCAmelCase__ = (np.arange(0 , _snake_case ) * step_ratio).round().copy().astype(np.intaa )
UpperCAmelCase__ = torch.from_numpy(_snake_case ).to(_snake_case )
self.timesteps += self.config.steps_offset
def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : str = 0.0 , _UpperCAmelCase : Dict = False , _UpperCAmelCase : Union[str, Any] = None , _UpperCAmelCase : Optional[Any] = True , ):
"""simple docstring"""
UpperCAmelCase__ = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
UpperCAmelCase__ = self.alphas_cumprod[timestep]
UpperCAmelCase__ = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
UpperCAmelCase__ = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase__ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
UpperCAmelCase__ = model_output
elif self.config.prediction_type == "sample":
UpperCAmelCase__ = model_output
UpperCAmelCase__ = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
UpperCAmelCase__ = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
UpperCAmelCase__ = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
""" `v_prediction`""" )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase__ = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
UpperCAmelCase__ = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
UpperCAmelCase__ = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=_snake_case , pred_original_sample=_snake_case )
def __len__( self : Any ):
"""simple docstring"""
return self.config.num_train_timesteps
| 361 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : list[list[int]] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : set ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ), len(grid[0] )
if (
min(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
UpperCAmelCase__ = 0
count += depth_first_search(SCREAMING_SNAKE_CASE__ , row + 1 , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
count += depth_first_search(SCREAMING_SNAKE_CASE__ , row - 1 , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
count += depth_first_search(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , col + 1 , SCREAMING_SNAKE_CASE__ )
count += depth_first_search(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , col - 1 , SCREAMING_SNAKE_CASE__ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 61 | 0 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowercase__ :
'''simple docstring'''
def __init__( self, __magic_name__, __magic_name__=13, __magic_name__=30, __magic_name__=2, __magic_name__=3, __magic_name__=True, __magic_name__=True, __magic_name__=32, __magic_name__=5, __magic_name__=4, __magic_name__=37, __magic_name__="gelu", __magic_name__=0.1, __magic_name__=0.1, __magic_name__=10, __magic_name__=0.02, __magic_name__=None, __magic_name__=2, ) -> Tuple:
"""simple docstring"""
UpperCamelCase__ : List[Any] = parent
UpperCamelCase__ : Tuple = batch_size
UpperCamelCase__ : Any = image_size
UpperCamelCase__ : List[str] = patch_size
UpperCamelCase__ : int = num_channels
UpperCamelCase__ : str = is_training
UpperCamelCase__ : Optional[int] = use_labels
UpperCamelCase__ : int = hidden_size
UpperCamelCase__ : Optional[Any] = num_hidden_layers
UpperCamelCase__ : Optional[Any] = num_attention_heads
UpperCamelCase__ : Dict = intermediate_size
UpperCamelCase__ : Optional[int] = hidden_act
UpperCamelCase__ : List[str] = hidden_dropout_prob
UpperCamelCase__ : Union[str, Any] = attention_probs_dropout_prob
UpperCamelCase__ : Any = type_sequence_label_size
UpperCamelCase__ : Optional[Any] = initializer_range
UpperCamelCase__ : List[str] = scope
UpperCamelCase__ : Dict = encoder_stride
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCamelCase__ : Optional[int] = (image_size // patch_size) ** 2
UpperCamelCase__ : Any = num_patches + 1
def UpperCamelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase__ : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase__ : Optional[int] = None
if self.use_labels:
UpperCamelCase__ : Tuple = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCamelCase__ : Dict = self.get_config()
return config, pixel_values, labels
def UpperCamelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
return ViTConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=UpperCAmelCase__, initializer_range=self.initializer_range, encoder_stride=self.encoder_stride, )
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, __magic_name__ ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase__ : int = ViTModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase__ : Tuple = model(UpperCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, __magic_name__ ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase__ : List[str] = ViTForMaskedImageModeling(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase__ : Any = model(UpperCAmelCase__ )
self.parent.assertEqual(
result.reconstruction.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
UpperCamelCase__ : List[str] = 1
UpperCamelCase__ : int = ViTForMaskedImageModeling(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase__ : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase__ : Dict = model(UpperCAmelCase__ )
self.parent.assertEqual(result.reconstruction.shape, (self.batch_size, 1, self.image_size, self.image_size) )
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, __magic_name__ ) -> Tuple:
"""simple docstring"""
UpperCamelCase__ : Any = self.type_sequence_label_size
UpperCamelCase__ : Union[str, Any] = ViTForImageClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase__ : Optional[int] = model(UpperCAmelCase__, labels=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCamelCase__ : Optional[Any] = 1
UpperCamelCase__ : Optional[Any] = ViTForImageClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase__ : List[str] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase__ : Tuple = model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase__ : List[str] = self.prepare_config_and_inputs()
(
(
UpperCamelCase__
) ,(
UpperCamelCase__
) ,(
UpperCamelCase__
) ,
) : List[str] = config_and_inputs
UpperCamelCase__ : Any = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( __lowercase , __lowercase , unittest.TestCase ):
'''simple docstring'''
a : Optional[Any] = (
(
ViTModel,
ViTForImageClassification,
ViTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
a : Union[str, Any] = (
{'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
if is_torch_available()
else {}
)
a : int = True
a : str = False
a : List[str] = False
a : Optional[int] = False
def UpperCamelCase__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase__ : List[Any] = ViTModelTester(self )
UpperCamelCase__ : Tuple = ConfigTester(self, config_class=UpperCAmelCase__, has_text_modality=UpperCAmelCase__, hidden_size=37 )
def UpperCamelCase__ ( self ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCamelCase__ ( self ) -> List[Any]:
"""simple docstring"""
pass
def UpperCamelCase__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ ,UpperCamelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ : Any = model_class(UpperCAmelCase__ )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
UpperCamelCase__ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase__, nn.Linear ) )
def UpperCamelCase__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ ,UpperCamelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ : Any = model_class(UpperCAmelCase__ )
UpperCamelCase__ : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase__ : Optional[Any] = [*signature.parameters.keys()]
UpperCamelCase__ : Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1], UpperCAmelCase__ )
def UpperCamelCase__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def UpperCamelCase__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase__ )
def UpperCamelCase__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__ )
@slow
def UpperCamelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ : Dict = ViTModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def lowerCAmelCase_ ( ) -> List[Any]:
UpperCamelCase__ : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase__ ( self ) -> List[Any]:
"""simple docstring"""
return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224''' ) if is_vision_available() else None
@slow
def UpperCamelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase__ : int = ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224''' ).to(UpperCAmelCase__ )
UpperCamelCase__ : str = self.default_image_processor
UpperCamelCase__ : int = prepare_img()
UpperCamelCase__ : Optional[int] = 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__ : Dict = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape, UpperCAmelCase__ )
UpperCamelCase__ : List[str] = torch.tensor([-0.2744, 0.8215, -0.0836] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3], UpperCAmelCase__, atol=1E-4 ) )
@slow
def UpperCamelCase__ ( self ) -> List[Any]:
"""simple docstring"""
# ViT models have an `interpolate_pos_encoding` argument in their forward method,
# allowing to interpolate the pre-trained position embeddings in order to use
# the model on higher resolutions. The DINO model by Facebook AI leverages this
# to visualize self-attention on higher resolution images.
UpperCamelCase__ : Dict = ViTModel.from_pretrained('''facebook/dino-vits8''' ).to(UpperCAmelCase__ )
UpperCamelCase__ : Optional[Any] = ViTImageProcessor.from_pretrained('''facebook/dino-vits8''', size=480 )
UpperCamelCase__ : Tuple = prepare_img()
UpperCamelCase__ : Union[str, Any] = image_processor(images=UpperCAmelCase__, return_tensors='''pt''' )
UpperCamelCase__ : int = inputs.pixel_values.to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
UpperCamelCase__ : Dict = model(UpperCAmelCase__, interpolate_pos_encoding=UpperCAmelCase__ )
# verify the logits
UpperCamelCase__ : Dict = torch.Size((1, 3601, 384) )
self.assertEqual(outputs.last_hidden_state.shape, UpperCAmelCase__ )
UpperCamelCase__ : str = torch.tensor(
[[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], UpperCAmelCase__, atol=1E-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase__ : Any = ViTModel.from_pretrained('''facebook/dino-vits8''', torch_dtype=torch.floataa, device_map='''auto''' )
UpperCamelCase__ : Optional[Any] = self.default_image_processor
UpperCamelCase__ : List[str] = prepare_img()
UpperCamelCase__ : Optional[int] = image_processor(images=UpperCAmelCase__, return_tensors='''pt''' )
UpperCamelCase__ : Optional[int] = inputs.pixel_values.to(UpperCAmelCase__ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
UpperCamelCase__ : Any = model(UpperCAmelCase__ )
| 201 |
'''simple docstring'''
class UpperCAmelCase_ :
def __init__( self : List[str] , UpperCAmelCase__ : list[int] ) -> None:
lowerCAmelCase = len(UpperCAmelCase__ )
lowerCAmelCase = [0] * len_array
if len_array > 0:
lowerCAmelCase = array[0]
for i in range(1 , UpperCAmelCase__ ):
lowerCAmelCase = self.prefix_sum[i - 1] + array[i]
def __UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> int:
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def __UpperCAmelCase ( self : int , UpperCAmelCase__ : int ) -> bool:
lowerCAmelCase = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(UpperCAmelCase__ )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 | 0 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = RobertaTokenizer
_lowerCamelCase = RobertaTokenizerFast
_lowerCamelCase = True
_lowerCamelCase = {"""cls_token""": """<s>"""}
def UpperCamelCase__( self ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__A : List[str] = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
__A : int = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
__A : str = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
__A : Dict = {'''unk_token''': '''<unk>'''}
__A : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
__A : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__lowerCamelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__lowerCamelCase ) )
def UpperCamelCase__( self , **__lowerCamelCase ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def UpperCamelCase__( self , **__lowerCamelCase ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def UpperCamelCase__( self , __lowerCamelCase ):
'''simple docstring'''
__A : str = '''lower newer'''
__A : int = '''lower newer'''
return input_text, output_text
def UpperCamelCase__( self ):
'''simple docstring'''
__A : Tuple = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
__A : List[Any] = '''lower newer'''
__A : int = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
__A : List[Any] = tokenizer.tokenize(__lowerCamelCase ) # , add_prefix_space=True)
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
__A : str = tokens + [tokenizer.unk_token]
__A : Any = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , __lowerCamelCase )
def UpperCamelCase__( self ):
'''simple docstring'''
__A : Union[str, Any] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode('''Hello world!''' , add_special_tokens=__lowerCamelCase ) , [0, 3_1414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode('''Hello world! cécé herlolip 418''' , add_special_tokens=__lowerCamelCase ) , [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2] , )
@slow
def UpperCamelCase__( self ):
'''simple docstring'''
__A : Dict = self.tokenizer_class.from_pretrained('''roberta-base''' )
__A : Union[str, Any] = tokenizer.encode('''sequence builders''' , add_special_tokens=__lowerCamelCase )
__A : Union[str, Any] = tokenizer.encode('''multi-sequence build''' , add_special_tokens=__lowerCamelCase )
__A : List[Any] = tokenizer.encode(
'''sequence builders''' , add_special_tokens=__lowerCamelCase , add_prefix_space=__lowerCamelCase )
__A : Tuple = tokenizer.encode(
'''sequence builders''' , '''multi-sequence build''' , add_special_tokens=__lowerCamelCase , add_prefix_space=__lowerCamelCase )
__A : str = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase )
__A : List[Any] = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase , __lowerCamelCase )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def UpperCamelCase__( self ):
'''simple docstring'''
__A : Tuple = self.get_tokenizer()
__A : Union[str, Any] = '''Encode this sequence.'''
__A : Optional[Any] = tokenizer.byte_encoder[''' '''.encode('''utf-8''' )[0]]
# Testing encoder arguments
__A : List[Any] = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase , add_prefix_space=__lowerCamelCase )
__A : int = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(__lowerCamelCase , __lowerCamelCase )
__A : Tuple = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase , add_prefix_space=__lowerCamelCase )
__A : str = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
tokenizer.add_special_tokens({'''bos_token''': '''<s>'''} )
__A : int = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
__A : List[Any] = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(__lowerCamelCase , __lowerCamelCase )
# Testing spaces after special tokens
__A : Any = '''<mask>'''
tokenizer.add_special_tokens(
{'''mask_token''': AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase )} ) # mask token has a left space
__A : Union[str, Any] = tokenizer.convert_tokens_to_ids(__lowerCamelCase )
__A : Tuple = '''Encode <mask> sequence'''
__A : str = '''Encode <mask>sequence'''
__A : Optional[Any] = tokenizer.encode(__lowerCamelCase )
__A : Dict = encoded.index(__lowerCamelCase )
__A : Any = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
__A : Dict = tokenizer.encode(__lowerCamelCase )
__A : List[Any] = encoded.index(__lowerCamelCase )
__A : Union[str, Any] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(__lowerCamelCase , __lowerCamelCase )
def UpperCamelCase__( self ):
'''simple docstring'''
pass
def UpperCamelCase__( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__A : str = self.rust_tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
__A : List[Any] = self.tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
__A : List[Any] = '''A, <mask> AllenNLP sentence.'''
__A : Union[str, Any] = tokenizer_r.encode_plus(__lowerCamelCase , add_special_tokens=__lowerCamelCase , return_token_type_ids=__lowerCamelCase )
__A : Any = tokenizer_p.encode_plus(__lowerCamelCase , add_special_tokens=__lowerCamelCase , return_token_type_ids=__lowerCamelCase )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['''token_type_ids'''] ) , sum(tokens_p['''token_type_ids'''] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] ) , sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] ) , )
__A : Tuple = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
__A : Dict = tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['''input_ids'''] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r['''input_ids'''] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(
__lowerCamelCase , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
self.assertSequenceEqual(
__lowerCamelCase , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
def UpperCamelCase__( self ):
'''simple docstring'''
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
__A : str = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
__A : List[Any] = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
__A : List[Any] = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state['''add_prefix_space'''] , __lowerCamelCase )
self.assertEqual(post_processor_state['''add_prefix_space'''] , __lowerCamelCase )
self.assertEqual(post_processor_state['''trim_offsets'''] , __lowerCamelCase )
def UpperCamelCase__( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__A : Optional[int] = '''hello''' # `hello` is a token in the vocabulary of `pretrained_name`
__A : str = F"""{text_of_1_token} {text_of_1_token}"""
__A : int = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
__A : List[Any] = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__lowerCamelCase ) + 1, len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
__A : List[str] = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
__A : Tuple = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__lowerCamelCase ) + 1, len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
__A : int = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
__A : List[Any] = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__lowerCamelCase ), len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
__A : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
__A : List[Any] = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__lowerCamelCase ), len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
__A : List[Any] = F""" {text}"""
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
__A : Optional[int] = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
__A : Optional[int] = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__lowerCamelCase ) + 1, 1 + len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
__A : List[str] = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
__A : Optional[Any] = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__lowerCamelCase ), 1 + len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
__A : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
__lowerCamelCase , use_fast=__lowerCamelCase , add_prefix_space=__lowerCamelCase , trim_offsets=__lowerCamelCase )
__A : Optional[Any] = tokenizer_r(__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(__lowerCamelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__lowerCamelCase ), 1 + len(__lowerCamelCase ) + 1 + len(__lowerCamelCase )) , )
| 359 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase ( snake_case_ : int ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Undefined for non-integers''' )
elif precision < 1:
raise ValueError('''Undefined for non-natural numbers''' )
__A : int = precision
__A : Tuple = ceil(precision / 14 )
__A : Dict = 426880 * Decimal(10005 ).sqrt()
__A : Optional[Any] = 1
__A : int = 13591409
__A : Optional[int] = Decimal(snake_case_ )
for k in range(1 ,snake_case_ ):
__A : int = factorial(6 * k ) // (factorial(3 * k ) * factorial(snake_case_ ) ** 3)
linear_term += 545140134
exponential_term *= -262537412640768000
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
a_ = 50
print(f'''The first {n} digits of pi is: {pi(n)}''')
| 291 | 0 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerCamelCase_ : int = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCamelCase_ : Any = argparse.ArgumentParser(
description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
)
parser.add_argument(
"""--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
)
parser.add_argument(
"""--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
)
parser.add_argument("""--vocab_size""", default=3_0_5_2_2, type=int)
lowerCamelCase_ : int = parser.parse_args()
logger.info(F'Loading data from {args.data_file}')
with open(args.data_file, """rb""") as fp:
lowerCamelCase_ : Optional[int] = pickle.load(fp)
logger.info("""Counting occurrences for MLM.""")
lowerCamelCase_ : Optional[int] = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCamelCase_ : Dict = [0] * args.vocab_size
for k, v in counter.items():
lowerCamelCase_ : Any = v
logger.info(F'Dump to {args.token_counts_dump}')
with open(args.token_counts_dump, """wb""") as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL) | 81 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def _A ( lowercase ):
"""simple docstring"""
a =1
a =2
while i * i <= n:
a =0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def _A ( ):
"""simple docstring"""
return next(i for i in triangle_number_generator() if count_divisors(lowercase ) > 5_00 )
if __name__ == "__main__":
print(solution()) | 81 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
lowercase__ = logging.getLogger(__name__)
class __lowerCamelCase ( A__ ):
'''simple docstring'''
a_ : Union[str, Any] = """summarization"""
a_ : List[str] = ["""loss"""]
a_ : Union[str, Any] = ROUGE_KEYS
a_ : Dict = """rouge2"""
def __init__( self : List[str] , a_ : Any , **a_ : Any ):
if hparams.sortish_sampler and hparams.gpus > 1:
lowerCAmelCase_ : Tuple = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError("Dynamic Batch size does not work for multi-gpu training" )
if hparams.sortish_sampler:
raise ValueError("--sortish_sampler and --max_tokens_per_batch may not be used simultaneously" )
super().__init__(a_ , num_labels=a_ , mode=self.mode , **a_ )
use_task_specific_params(self.model , "summarization" )
save_git_info(self.hparams.output_dir )
lowerCAmelCase_ : str = Path(self.output_dir ) / "metrics.json"
lowerCAmelCase_ : Optional[int] = Path(self.output_dir ) / "hparams.pkl"
pickle_save(self.hparams , self.hparams_save_path )
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : int = defaultdict(a_ )
lowerCAmelCase_ : Dict = self.config.model_type
lowerCAmelCase_ : List[str] = self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size
lowerCAmelCase_ : dict = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
lowerCAmelCase_ : Tuple = {
"train": self.hparams.n_train,
"val": self.hparams.n_val,
"test": self.hparams.n_test,
}
lowerCAmelCase_ : Dict = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
lowerCAmelCase_ : Any = {
"train": self.hparams.max_target_length,
"val": self.hparams.val_max_target_length,
"test": self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], f'''target_lens: {self.target_lens}'''
assert self.target_lens["train"] <= self.target_lens["test"], f'''target_lens: {self.target_lens}'''
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
lowerCAmelCase_ : Optional[int] = get_git_info()["repo_sha"]
lowerCAmelCase_ : List[str] = hparams.num_workers
lowerCAmelCase_ : List[str] = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , a_ ):
lowerCAmelCase_ : Union[str, Any] = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
lowerCAmelCase_ : Any = self.decoder_start_token_id
lowerCAmelCase_ : List[Any] = (
SeqaSeqDataset if hasattr(self.tokenizer , "prepare_seq2seq_batch" ) else LegacySeqaSeqDataset
)
lowerCAmelCase_ : Union[str, Any] = False
lowerCAmelCase_ : Dict = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
lowerCAmelCase_ : Dict = self.hparams.eval_max_gen_length
else:
lowerCAmelCase_ : Optional[int] = self.model.config.max_length
lowerCAmelCase_ : List[Any] = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def lowerCamelCase ( self : List[str] , a_ : Dict[str, torch.Tensor] ):
lowerCAmelCase_ : Any = {
k: self.tokenizer.batch_decode(v.tolist() ) if "mask" not in k else v.shape for k, v in batch.items()
}
save_json(a_ , Path(self.output_dir ) / "text_batch.json" )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / "tok_batch.json" )
lowerCAmelCase_ : Optional[int] = True
return readable_batch
def lowerCamelCase ( self : List[str] , a_ : List[str] , **a_ : Tuple ):
return self.model(a_ , **a_ )
def lowerCamelCase ( self : Any , a_ : List[int] ):
lowerCAmelCase_ : Dict = self.tokenizer.batch_decode(
a_ , skip_special_tokens=a_ , clean_up_tokenization_spaces=a_ )
return lmap(str.strip , a_ )
def lowerCamelCase ( self : Union[str, Any] , a_ : dict ):
lowerCAmelCase_ : str = self.tokenizer.pad_token_id
lowerCAmelCase_ , lowerCAmelCase_ : int = batch["input_ids"], batch["attention_mask"]
lowerCAmelCase_ : Optional[Any] = batch["labels"]
if isinstance(self.model , a_ ):
lowerCAmelCase_ : int = self.model._shift_right(a_ )
else:
lowerCAmelCase_ : Union[str, Any] = shift_tokens_right(a_ , a_ )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
lowerCAmelCase_ : Union[str, Any] = decoder_input_ids
self.save_readable_batch(a_ )
lowerCAmelCase_ : Union[str, Any] = self(a_ , attention_mask=a_ , decoder_input_ids=a_ , use_cache=a_ )
lowerCAmelCase_ : Optional[Any] = outputs["logits"]
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
lowerCAmelCase_ : Dict = nn.CrossEntropyLoss(ignore_index=a_ )
assert lm_logits.shape[-1] == self.vocab_size
lowerCAmelCase_ : Any = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
lowerCAmelCase_ : Dict = nn.functional.log_softmax(a_ , dim=-1 )
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = label_smoothed_nll_loss(
a_ , a_ , self.hparams.label_smoothing , ignore_index=a_ )
return (loss,)
@property
def lowerCamelCase ( self : str ):
return self.tokenizer.pad_token_id
def lowerCamelCase ( self : List[str] , a_ : Optional[Any] , a_ : Union[str, Any] ):
lowerCAmelCase_ : Optional[Any] = self._step(a_ )
lowerCAmelCase_ : Dict = dict(zip(self.loss_names , a_ ) )
# tokens per batch
lowerCAmelCase_ : List[Any] = batch["input_ids"].ne(self.pad ).sum() + batch["labels"].ne(self.pad ).sum()
lowerCAmelCase_ : Tuple = batch["input_ids"].shape[0]
lowerCAmelCase_ : Optional[Any] = batch["input_ids"].eq(self.pad ).sum()
lowerCAmelCase_ : Tuple = batch["input_ids"].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def lowerCamelCase ( self : Tuple , a_ : Union[str, Any] , a_ : Union[str, Any] ):
return self._generative_step(a_ )
def lowerCamelCase ( self : str , a_ : Tuple , a_ : Dict="val" ):
self.step_count += 1
lowerCAmelCase_ : Optional[int] = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
lowerCAmelCase_ : Dict = losses["loss"]
lowerCAmelCase_ : Optional[int] = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["gen_time", "gen_len"]
}
lowerCAmelCase_ : List[Any] = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
lowerCAmelCase_ : torch.FloatTensor = torch.tensor(a_ ).type_as(a_ )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(a_ )
lowerCAmelCase_ : int = {f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
lowerCAmelCase_ : Optional[Any] = self.step_count
self.metrics[prefix].append(a_ ) # callback writes this to self.metrics_save_path
lowerCAmelCase_ : Dict = flatten_list([x["preds"] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
f'''{prefix}_loss''': loss,
f'''{prefix}_{self.val_metric}''': metric_tensor,
}
def lowerCamelCase ( self : str , a_ : Optional[int] , a_ : Optional[Any] ):
return calculate_rouge(a_ , a_ )
def lowerCamelCase ( self : Tuple , a_ : dict ):
lowerCAmelCase_ : Any = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
lowerCAmelCase_ : Optional[int] = self.model.generate(
batch["input_ids"] , attention_mask=batch["attention_mask"] , use_cache=a_ , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
lowerCAmelCase_ : List[str] = (time.time() - ta) / batch["input_ids"].shape[0]
lowerCAmelCase_ : List[str] = self.ids_to_clean_text(a_ )
lowerCAmelCase_ : List[str] = self.ids_to_clean_text(batch["labels"] )
lowerCAmelCase_ : Optional[int] = self._step(a_ )
lowerCAmelCase_ : Union[str, Any] = dict(zip(self.loss_names , a_ ) )
lowerCAmelCase_ : Dict = self.calc_generative_metrics(a_ , a_ )
lowerCAmelCase_ : str = np.mean(lmap(a_ , a_ ) )
base_metrics.update(gen_time=a_ , gen_len=a_ , preds=a_ , target=a_ , **a_ )
return base_metrics
def lowerCamelCase ( self : Tuple , a_ : int , a_ : List[str] ):
return self._generative_step(a_ )
def lowerCamelCase ( self : str , a_ : Optional[int] ):
return self.validation_epoch_end(a_ , prefix="test" )
def lowerCamelCase ( self : Tuple , a_ : Tuple ):
lowerCAmelCase_ : Optional[int] = self.n_obs[type_path]
lowerCAmelCase_ : Optional[int] = self.target_lens[type_path]
lowerCAmelCase_ : int = self.dataset_class(
self.tokenizer , type_path=a_ , n_obs=a_ , max_target_length=a_ , **self.dataset_kwargs , )
return dataset
def lowerCamelCase ( self : List[Any] , a_ : str , a_ : int , a_ : bool = False ):
lowerCAmelCase_ : List[Any] = self.get_dataset(a_ )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
lowerCAmelCase_ : Dict = dataset.make_sortish_sampler(a_ , distributed=self.hparams.gpus > 1 )
return DataLoader(
a_ , batch_size=a_ , collate_fn=dataset.collate_fn , shuffle=a_ , num_workers=self.num_workers , sampler=a_ , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
lowerCAmelCase_ : Tuple = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
a_ , batch_sampler=a_ , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
a_ , batch_size=a_ , collate_fn=dataset.collate_fn , shuffle=a_ , num_workers=self.num_workers , sampler=a_ , )
def lowerCamelCase ( self : int ):
lowerCAmelCase_ : List[str] = self.get_dataloader("train" , batch_size=self.hparams.train_batch_size , shuffle=a_ )
return dataloader
def lowerCamelCase ( self : Tuple ):
return self.get_dataloader("val" , batch_size=self.hparams.eval_batch_size )
def lowerCamelCase ( self : int ):
return self.get_dataloader("test" , batch_size=self.hparams.eval_batch_size )
@staticmethod
def lowerCamelCase ( a_ : Union[str, Any] , a_ : Tuple ):
BaseTransformer.add_model_specific_args(a_ , a_ )
add_generic_args(a_ , a_ )
parser.add_argument(
"--max_source_length" , default=10_24 , type=a_ , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument(
"--max_target_length" , default=56 , type=a_ , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument(
"--val_max_target_length" , default=1_42 , type=a_ , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument(
"--test_max_target_length" , default=1_42 , type=a_ , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument("--freeze_encoder" , action="store_true" )
parser.add_argument("--freeze_embeds" , action="store_true" )
parser.add_argument("--sortish_sampler" , action="store_true" , default=a_ )
parser.add_argument("--overwrite_output_dir" , action="store_true" , default=a_ )
parser.add_argument("--max_tokens_per_batch" , type=a_ , default=a_ )
parser.add_argument("--logger_name" , type=a_ , choices=["default", "wandb", "wandb_shared"] , default="default" )
parser.add_argument("--n_train" , type=a_ , default=-1 , required=a_ , help="# examples. -1 means use all." )
parser.add_argument("--n_val" , type=a_ , default=5_00 , required=a_ , help="# examples. -1 means use all." )
parser.add_argument("--n_test" , type=a_ , default=-1 , required=a_ , help="# examples. -1 means use all." )
parser.add_argument(
"--task" , type=a_ , default="summarization" , required=a_ , help="# examples. -1 means use all." )
parser.add_argument("--label_smoothing" , type=a_ , default=0.0 , required=a_ )
parser.add_argument("--src_lang" , type=a_ , default="" , required=a_ )
parser.add_argument("--tgt_lang" , type=a_ , default="" , required=a_ )
parser.add_argument("--eval_beams" , type=a_ , default=a_ , required=a_ )
parser.add_argument(
"--val_metric" , type=a_ , default=a_ , required=a_ , choices=["bleu", "rouge2", "loss", None] )
parser.add_argument("--eval_max_gen_length" , type=a_ , default=a_ , help="never generate more than n tokens" )
parser.add_argument("--save_top_k" , type=a_ , default=1 , required=a_ , help="How many checkpoints to save" )
parser.add_argument(
"--early_stopping_patience" , type=a_ , default=-1 , required=a_ , help=(
"-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
" val_check_interval will effect it."
) , )
return parser
class __lowerCamelCase ( A__ ):
'''simple docstring'''
a_ : Any = """translation"""
a_ : Any = ["""loss"""]
a_ : List[Any] = ["""bleu"""]
a_ : Dict = """bleu"""
def __init__( self : List[Any] , a_ : Any , **a_ : Optional[Any] ):
super().__init__(a_ , **a_ )
lowerCAmelCase_ : Dict = hparams.src_lang
lowerCAmelCase_ : Optional[int] = hparams.tgt_lang
def lowerCamelCase ( self : List[Any] , a_ : str , a_ : Any ):
return calculate_bleu(a_ , a_ )
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase=None ) -> SummarizationModule:
"""simple docstring"""
Path(args.output_dir ).mkdir(exist_ok=__UpperCamelCase )
check_output_dir(__UpperCamelCase , expected_items=3 )
if model is None:
if "summarization" in args.task:
lowerCAmelCase_ : SummarizationModule = SummarizationModule(__UpperCamelCase )
else:
lowerCAmelCase_ : SummarizationModule = TranslationModule(__UpperCamelCase )
lowerCAmelCase_ : List[str] = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith("/tmp" )
or str(args.output_dir ).startswith("/var" )
):
lowerCAmelCase_ : Optional[Any] = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
lowerCAmelCase_ : List[Any] = os.environ.get("WANDB_PROJECT" , __UpperCamelCase )
lowerCAmelCase_ : List[Any] = WandbLogger(name=model.output_dir.name , project=__UpperCamelCase )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
lowerCAmelCase_ : List[Any] = WandbLogger(name=model.output_dir.name , project=f'''hf_{dataset}''' )
if args.early_stopping_patience >= 0:
lowerCAmelCase_ : List[str] = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
lowerCAmelCase_ : Optional[int] = False
lowerCAmelCase_ : Union[str, Any] = args.val_metric == "loss"
lowerCAmelCase_ : pl.Trainer = generic_train(
__UpperCamelCase , __UpperCamelCase , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , __UpperCamelCase ) , early_stopping_callback=__UpperCamelCase , logger=__UpperCamelCase , )
pickle_save(model.hparams , model.output_dir / "hparams.pkl" )
if not args.do_predict:
return model
lowerCAmelCase_ : Any = ""
lowerCAmelCase_ : int = sorted(glob.glob(os.path.join(args.output_dir , "*.ckpt" ) , recursive=__UpperCamelCase ) )
if checkpoints:
lowerCAmelCase_ : Optional[int] = checkpoints[-1]
lowerCAmelCase_ : Optional[int] = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
lowercase__ = pl.Trainer.add_argparse_args(parser)
lowercase__ = SummarizationModule.add_model_specific_args(parser, os.getcwd())
lowercase__ = parser.parse_args()
main(args)
| 161 |
"""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
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""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 __lowerCamelCase ( A__ ):
'''simple docstring'''
a_ : Union[str, Any] = """gpt_neo"""
a_ : List[Any] = ["""past_key_values"""]
a_ : Optional[Any] = {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self : Optional[int] , a_ : List[str]=5_02_57 , a_ : List[str]=20_48 , a_ : Union[str, Any]=20_48 , a_ : Union[str, Any]=24 , a_ : Optional[int]=[[["global", "local"], 12]] , a_ : str=16 , a_ : Optional[Any]=None , a_ : str=2_56 , a_ : Union[str, Any]="gelu_new" , a_ : Optional[int]=0.0 , a_ : Optional[Any]=0.0 , a_ : List[Any]=0.0 , a_ : List[Any]=0.1 , a_ : Optional[Any]=1e-5 , a_ : Optional[Any]=0.02 , a_ : int=True , a_ : Optional[Any]=5_02_56 , a_ : Tuple=5_02_56 , **a_ : str , ):
lowerCAmelCase_ : Optional[Any] = vocab_size
lowerCAmelCase_ : str = max_position_embeddings
lowerCAmelCase_ : Tuple = hidden_size
lowerCAmelCase_ : Union[str, Any] = num_layers
lowerCAmelCase_ : str = num_heads
lowerCAmelCase_ : List[str] = intermediate_size
lowerCAmelCase_ : Union[str, Any] = window_size
lowerCAmelCase_ : Any = activation_function
lowerCAmelCase_ : str = resid_dropout
lowerCAmelCase_ : Union[str, Any] = embed_dropout
lowerCAmelCase_ : Optional[Any] = attention_dropout
lowerCAmelCase_ : Dict = classifier_dropout
lowerCAmelCase_ : int = layer_norm_epsilon
lowerCAmelCase_ : Dict = initializer_range
lowerCAmelCase_ : List[Any] = use_cache
lowerCAmelCase_ : Optional[int] = bos_token_id
lowerCAmelCase_ : str = eos_token_id
lowerCAmelCase_ : Optional[Any] = attention_types
lowerCAmelCase_ : Optional[Any] = self.expand_attention_types_params(a_ )
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=a_ , eos_token_id=a_ , **a_ )
@staticmethod
def lowerCamelCase ( a_ : Optional[Any] ):
lowerCAmelCase_ : int = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
"""simple docstring"""
import torch
lowerCAmelCase_ : str = input.size()
lowerCAmelCase_ : List[Any] = len(__UpperCamelCase )
lowerCAmelCase_ : Tuple = shape[dimension]
lowerCAmelCase_ : Tuple = torch.arange(0 , __UpperCamelCase , __UpperCamelCase )
lowerCAmelCase_ : List[Any] = torch.div(sizedim - size , __UpperCamelCase , rounding_mode="floor" ) + 1
lowerCAmelCase_ : Dict = torch.arange(__UpperCamelCase ) + low_indices[:min_length][:, None]
lowerCAmelCase_ : Tuple = [slice(__UpperCamelCase )] * rank
lowerCAmelCase_ : List[str] = indices
lowerCAmelCase_ : Dict = input[s]
lowerCAmelCase_ : Tuple = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(__UpperCamelCase )
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase ) -> Any:
"""simple docstring"""
import torch
lowerCAmelCase_ : Optional[int] = torch.arange(1 , __UpperCamelCase )
lowerCAmelCase_ : Tuple = torch.remainder(__UpperCamelCase , __UpperCamelCase )
lowerCAmelCase_ : Tuple = remainders == 0
lowerCAmelCase_ : List[Any] = candidates[divisor_indices]
lowerCAmelCase_ : List[str] = torch.max(__UpperCamelCase )
return largest_divisor, torch.div(__UpperCamelCase , __UpperCamelCase , rounding_mode="floor" )
class __lowerCamelCase ( A__ ):
'''simple docstring'''
@property
def lowerCamelCase ( self : List[str] ):
lowerCAmelCase_ : Any = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(a_ , direction="inputs" )
lowerCAmelCase_ : int = {0: "batch", 1: "past_sequence + sequence"}
else:
lowerCAmelCase_ : str = {0: "batch", 1: "sequence"}
return common_inputs
@property
def lowerCamelCase ( self : int ):
return self._config.num_heads
def lowerCamelCase ( self : Optional[Any] , a_ : PreTrainedTokenizer , a_ : int = -1 , a_ : int = -1 , a_ : bool = False , a_ : Optional[TensorType] = None , ):
lowerCAmelCase_ : int = super(a_ , self ).generate_dummy_inputs(
a_ , batch_size=a_ , seq_length=a_ , is_pair=a_ , framework=a_ )
# We need to order the input in the way they appears in the forward()
lowerCAmelCase_ : 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
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
lowerCAmelCase_ : str = seqlen + 2
lowerCAmelCase_ : Tuple = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCAmelCase_ : Optional[int] = [
(torch.zeros(a_ ), torch.zeros(a_ )) for _ in range(self.num_layers )
]
lowerCAmelCase_ : Tuple = common_inputs["attention_mask"]
if self.use_past:
lowerCAmelCase_ : List[str] = ordered_inputs["attention_mask"].dtype
lowerCAmelCase_ : Optional[Any] = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(a_ , a_ , dtype=a_ )] , dim=1 )
return ordered_inputs
@property
def lowerCamelCase ( self : Union[str, Any] ):
return 13
| 161 | 1 |
from __future__ import annotations
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = get_failure_array(_A )
# 2) Step through text searching for pattern
lowerCAmelCase_ , lowerCAmelCase_ = 0, 0 # index into text, pattern
while i < len(_A ):
if pattern[j] == text[i]:
if j == (len(_A ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
lowerCAmelCase_ = failure[j - 1]
continue
i += 1
return False
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [0]
lowerCAmelCase_ = 0
lowerCAmelCase_ = 1
while j < len(_A ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
lowerCAmelCase_ = failure[i - 1]
continue
j += 1
failure.append(_A )
return failure
if __name__ == "__main__":
# Test 1)
_A = '''abc1abc12'''
_A = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
_A = '''alskfjaldsk23adsfabcabc'''
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
_A = '''ABABX'''
_A = '''ABABZABABYABABX'''
assert kmp(pattern, text)
# Test 3)
_A = '''AAAB'''
_A = '''ABAAAAAB'''
assert kmp(pattern, text)
# Test 4)
_A = '''abcdabcy'''
_A = '''abcxabcdabxabcdabcdabcy'''
assert kmp(pattern, text)
# Test 5)
_A = '''aabaabaaa'''
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 278 |
def __UpperCamelCase ( _A ):
if not numbers:
return 0
if not isinstance(_A , (list, tuple) ) or not all(
isinstance(_A , _A ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
lowerCAmelCase_ = lowerCAmelCase_ = lowerCAmelCase_ = numbers[0]
for i in range(1 , len(_A ) ):
# update the maximum and minimum subarray products
lowerCAmelCase_ = numbers[i]
if number < 0:
lowerCAmelCase_ , lowerCAmelCase_ = min_till_now, max_till_now
lowerCAmelCase_ = max(_A , max_till_now * number )
lowerCAmelCase_ = min(_A , min_till_now * number )
# update the maximum product found till now
lowerCAmelCase_ = max(_A , _A )
return max_prod
| 278 | 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 ShapEPipeline
else:
from .camera import create_pan_cameras
from .pipeline_shap_e import ShapEPipeline
from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
from .renderer import (
BoundingBoxVolume,
ImportanceRaySampler,
MLPNeRFModelOutput,
MLPNeRSTFModel,
ShapEParamsProjModel,
ShapERenderer,
StratifiedRaySampler,
VoidNeRFModel,
)
| 354 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE_ : List[str] = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\n Dorr, Bonnie and\n Schwartz, Rich and\n Micciulla, Linnea and\n Makhoul, John",\n booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n month = aug # " 8-12",\n year = "2006",\n address = "Cambridge, Massachusetts, USA",\n publisher = "Association for Machine Translation in the Americas",\n url = "https://aclanthology.org/2006.amta-papers.25",\n pages = "223--231",\n}\n@inproceedings{post-2018-call,\n title = "A Call for Clarity in Reporting {BLEU} Scores",\n author = "Post, Matt",\n booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n month = oct,\n year = "2018",\n address = "Belgium, Brussels",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W18-6319",\n pages = "186--191",\n}\n'
SCREAMING_SNAKE_CASE_ : List[str] = '\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'
SCREAMING_SNAKE_CASE_ : List[Any] = '\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n predictions (list of str): The system stream (a sequence of segments).\n references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n Only applies if `normalized = True`. Defaults to `False`.\n case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n \'num_edits\' (int): The cumulative number of edits\n \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n Example 1:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?",\n ... "What did the TER metric user say to the developer?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n ... ["Your jokes are...", "...TERrible"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n Example 2:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n Example 3:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... normalized=True,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n Example 4:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... ignore_punct=True,\n ... case_sensitive=False)\n >>> print(results)\n {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n Example 5:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?",\n ... "What did the TER metric user say to the developer?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n ... ["Your jokes are...", "...TERrible"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... ignore_punct=True,\n ... case_sensitive=False)\n >>> print(results)\n {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class a ( datasets.Metric ):
"""simple docstring"""
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
if version.parse(scb.__version__ ) < version.parse("""1.4.12""" ):
raise ImportWarning(
"""To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
"""You can install it with `pip install \"sacrebleu>=1.4.12\"`.""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""http://www.cs.umd.edu/~snover/tercom/""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Sequence(datasets.Value("""string""" , id="""sequence""" ) , id="""references""" ),
} ) , codebase_urls=["""https://github.com/mjpost/sacreBLEU#ter"""] , reference_urls=[
"""https://github.com/jhclark/tercom""",
] , )
def UpperCamelCase ( self: int , UpperCamelCase: Optional[int] , UpperCamelCase: Union[str, Any] , UpperCamelCase: bool = False , UpperCamelCase: bool = False , UpperCamelCase: bool = False , UpperCamelCase: bool = False , ):
"""simple docstring"""
A__ = len(references[0] )
if any(len(UpperCamelCase ) != references_per_prediction for refs in references ):
raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
A__ = [[refs[i] for refs in references] for i in range(UpperCamelCase )]
A__ = TER(
normalized=UpperCamelCase , no_punct=UpperCamelCase , asian_support=UpperCamelCase , case_sensitive=UpperCamelCase , )
A__ = sb_ter.corpus_score(UpperCamelCase , UpperCamelCase )
return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}
| 69 | 0 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowerCamelCase__ = {
# 1536-bit
5: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF""",
base=16,
),
"""generator""": 2,
},
# 2048-bit
14: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AACAA68FFFFFFFFFFFFFFFF""",
base=16,
),
"""generator""": 2,
},
# 3072-bit
15: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
+ """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
+ """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
+ """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
+ """43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF""",
base=16,
),
"""generator""": 2,
},
# 4096-bit
16: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
+ """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
+ """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
+ """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
+ """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
+ """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
+ """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
+ """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
+ """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
+ """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"""
+ """FFFFFFFFFFFFFFFF""",
base=16,
),
"""generator""": 2,
},
# 6144-bit
17: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"""
+ """8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"""
+ """302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"""
+ """A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"""
+ """49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"""
+ """FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"""
+ """180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"""
+ """3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"""
+ """04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"""
+ """B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"""
+ """1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"""
+ """E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"""
+ """99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"""
+ """04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"""
+ """233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"""
+ """D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
+ """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"""
+ """AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"""
+ """DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"""
+ """2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"""
+ """F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"""
+ """BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
+ """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"""
+ """B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"""
+ """387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"""
+ """6DCC4024FFFFFFFFFFFFFFFF""",
base=16,
),
"""generator""": 2,
},
# 8192-bit
18: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
+ """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
+ """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
+ """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
+ """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
+ """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
+ """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
+ """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
+ """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
+ """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
+ """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"""
+ """F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"""
+ """179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"""
+ """DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"""
+ """5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"""
+ """D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"""
+ """23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
+ """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"""
+ """06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"""
+ """DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"""
+ """12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"""
+ """38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"""
+ """741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"""
+ """3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"""
+ """22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"""
+ """4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"""
+ """062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"""
+ """4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"""
+ """B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"""
+ """4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"""
+ """9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"""
+ """60C980DD98EDD3DFFFFFFFFFFFFFFFFF""",
base=16,
),
"""generator""": 2,
},
}
class A__ :
def __init__( self , _SCREAMING_SNAKE_CASE = 14 ):
if group not in primes:
raise ValueError('Unsupported Group' )
__lowerCAmelCase : Dict = primes[group]['prime']
__lowerCAmelCase : Any = primes[group]['generator']
__lowerCAmelCase : Dict = int(hexlify(urandom(32 ) ) , base=16 )
def __lowerCamelCase ( self ):
return hex(self.__private_key )[2:]
def __lowerCamelCase ( self ):
__lowerCAmelCase : str = pow(self.generator , self.__private_key , self.prime )
return hex(_SCREAMING_SNAKE_CASE )[2:]
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= key <= self.prime - 2
and pow(_SCREAMING_SNAKE_CASE , (self.prime - 1) // 2 , self.prime ) == 1
)
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Any = int(_SCREAMING_SNAKE_CASE , base=16 )
if not self.is_valid_public_key(_SCREAMING_SNAKE_CASE ):
raise ValueError('Invalid public key' )
__lowerCAmelCase : Dict = pow(_SCREAMING_SNAKE_CASE , self.__private_key , self.prime )
return shaaaa(str(_SCREAMING_SNAKE_CASE ).encode() ).hexdigest()
@staticmethod
def __lowerCamelCase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= remote_public_key_str <= prime - 2
and pow(_SCREAMING_SNAKE_CASE , (prime - 1) // 2 , _SCREAMING_SNAKE_CASE ) == 1
)
@staticmethod
def __lowerCamelCase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 14 ):
__lowerCAmelCase : Optional[int] = int(_SCREAMING_SNAKE_CASE , base=16 )
__lowerCAmelCase : List[Any] = int(_SCREAMING_SNAKE_CASE , base=16 )
__lowerCAmelCase : Tuple = primes[group]['prime']
if not DiffieHellman.is_valid_public_key_static(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('Invalid public key' )
__lowerCAmelCase : Tuple = pow(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return shaaaa(str(_SCREAMING_SNAKE_CASE ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod() | 86 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
snake_case_ : List[str] = logging.get_logger(__name__)
snake_case_ : Tuple = {
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class __snake_case ( a ):
UpperCAmelCase__ : str = '''codegen'''
UpperCAmelCase__ : int = {
'''max_position_embeddings''': '''n_positions''',
'''hidden_size''': '''n_embd''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Union[str, Any] , _snake_case : Union[str, Any]=50400 , _snake_case : Optional[int]=2048 , _snake_case : Union[str, Any]=2048 , _snake_case : List[str]=4096 , _snake_case : Any=28 , _snake_case : List[str]=16 , _snake_case : int=64 , _snake_case : Tuple=None , _snake_case : Dict="gelu_new" , _snake_case : Union[str, Any]=0.0 , _snake_case : Optional[Any]=0.0 , _snake_case : List[Any]=0.0 , _snake_case : List[Any]=1e-5 , _snake_case : List[str]=0.0_2 , _snake_case : Optional[Any]=True , _snake_case : int=50256 , _snake_case : Tuple=50256 , _snake_case : int=False , **_snake_case : Any , ):
"""simple docstring"""
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = n_ctx
UpperCAmelCase_ = n_positions
UpperCAmelCase_ = n_embd
UpperCAmelCase_ = n_layer
UpperCAmelCase_ = n_head
UpperCAmelCase_ = n_inner
UpperCAmelCase_ = rotary_dim
UpperCAmelCase_ = activation_function
UpperCAmelCase_ = resid_pdrop
UpperCAmelCase_ = embd_pdrop
UpperCAmelCase_ = attn_pdrop
UpperCAmelCase_ = layer_norm_epsilon
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = use_cache
UpperCAmelCase_ = bos_token_id
UpperCAmelCase_ = eos_token_id
super().__init__(
bos_token_id=_snake_case , eos_token_id=_snake_case , tie_word_embeddings=_snake_case , **_snake_case)
class __snake_case ( a ):
def __init__( self : Tuple , _snake_case : PretrainedConfig , _snake_case : str = "default" , _snake_case : List[PatchingSpec] = None , _snake_case : bool = False , ):
"""simple docstring"""
super().__init__(_snake_case , task=_snake_case , patching_specs=_snake_case , use_past=_snake_case)
if not getattr(self._config , '''pad_token_id''' , _snake_case):
# TODO: how to do that better?
UpperCAmelCase_ = 0
@property
def lowerCamelCase ( self : Optional[Any]):
"""simple docstring"""
UpperCAmelCase_ = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}})
if self.use_past:
self.fill_with_past_key_values_(_snake_case , direction='''inputs''')
UpperCAmelCase_ = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
UpperCAmelCase_ = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def lowerCamelCase ( self : List[str]):
"""simple docstring"""
return self._config.n_layer
@property
def lowerCamelCase ( self : int):
"""simple docstring"""
return self._config.n_head
def lowerCamelCase ( self : Optional[int] , _snake_case : PreTrainedTokenizer , _snake_case : int = -1 , _snake_case : int = -1 , _snake_case : bool = False , _snake_case : Optional[TensorType] = None , ):
"""simple docstring"""
UpperCAmelCase_ = super(_snake_case , self).generate_dummy_inputs(
_snake_case , batch_size=_snake_case , seq_length=_snake_case , is_pair=_snake_case , framework=_snake_case)
# We need to order the input in the way they appears in the forward()
UpperCAmelCase_ = 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_ = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase_ = seqlen + 2
UpperCAmelCase_ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
UpperCAmelCase_ = [
(torch.zeros(_snake_case), torch.zeros(_snake_case)) for _ in range(self.num_layers)
]
UpperCAmelCase_ = common_inputs['''attention_mask''']
if self.use_past:
UpperCAmelCase_ = ordered_inputs['''attention_mask'''].dtype
UpperCAmelCase_ = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(_snake_case , _snake_case , dtype=_snake_case)] , dim=1)
return ordered_inputs
@property
def lowerCamelCase ( self : Union[str, Any]):
"""simple docstring"""
return 13
| 51 | 0 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class lowerCamelCase :
def __init__(self : str , _A : Optional[Any] , _A : List[str] , _A : bool = True , _A : bool = False ) -> str:
snake_case = scheduler
snake_case = optimizers if isinstance(_A , (list, tuple) ) else [optimizers]
snake_case = split_batches
snake_case = step_with_optimizer
snake_case = GradientState()
def UpperCAmelCase(self : List[Any] , *_A : int , **_A : Union[str, Any] ) -> int:
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*_A , **_A )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*_A , **_A )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
snake_case = AcceleratorState().num_processes
for _ in range(_A ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , "total_steps" ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*_A , **_A )
else:
self.scheduler.step(*_A , **_A )
def UpperCAmelCase(self : List[str] ) -> str:
return self.scheduler.get_last_lr()
def UpperCAmelCase(self : List[str] ) -> Union[str, Any]:
return self.scheduler.state_dict()
def UpperCAmelCase(self : str , _A : Optional[Any] ) -> Any:
self.scheduler.load_state_dict(_A )
def UpperCAmelCase(self : Union[str, Any] ) -> Tuple:
return self.scheduler.get_lr()
def UpperCAmelCase(self : Tuple , *_A : int , **_A : Union[str, Any] ) -> str:
return self.scheduler.print_lr(*_A , **_A )
| 350 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowercase_ ( A__ ) -> str:
"""simple docstring"""
return getitem, k
def lowercase_ ( A__ , A__ ) -> str:
"""simple docstring"""
return setitem, k, v
def lowercase_ ( A__ ) -> List[Any]:
"""simple docstring"""
return delitem, k
def lowercase_ ( A__ , A__ , *A__ ) -> str:
"""simple docstring"""
try:
return fun(A__ , *A__ ), None
except Exception as e:
return None, e
_A = (
_set("key_a", "val_a"),
_set("key_b", "val_b"),
)
_A = [
_set("key_a", "val_a"),
_set("key_a", "val_b"),
]
_A = [
_set("key_a", "val_a"),
_set("key_b", "val_b"),
_del("key_a"),
_del("key_b"),
_set("key_a", "val_a"),
_del("key_a"),
]
_A = [
_get("key_a"),
_del("key_a"),
_set("key_a", "val_a"),
_del("key_a"),
_del("key_a"),
_get("key_a"),
]
_A = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
_A = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("key_a", "val_b"),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items" ),
pytest.param(_overwrite_items , id="overwrite items" ),
pytest.param(_delete_items , id="delete items" ),
pytest.param(_access_absent_items , id="access absent items" ),
pytest.param(_add_with_resize_up , id="add with resize up" ),
pytest.param(_add_with_resize_down , id="add with resize down" ),
) , )
def lowercase_ ( A__ ) -> List[Any]:
"""simple docstring"""
snake_case = HashMap(initial_block_size=4 )
snake_case = {}
for _, (fun, *args) in enumerate(A__ ):
snake_case , snake_case = _run_operation(A__ , A__ , *A__ )
snake_case , snake_case = _run_operation(A__ , A__ , *A__ )
assert my_res == py_res
assert str(A__ ) == str(A__ )
assert set(A__ ) == set(A__ )
assert len(A__ ) == len(A__ )
assert set(my.items() ) == set(py.items() )
def lowercase_ ( ) -> Optional[int]:
"""simple docstring"""
def is_public(A__ ) -> bool:
return not name.startswith("_" )
snake_case = {name for name in dir({} ) if is_public(A__ )}
snake_case = {name for name in dir(HashMap() ) if is_public(A__ )}
assert dict_public_names > hash_public_names
| 137 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
lowerCamelCase__ =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def SCREAMING_SNAKE_CASE (self , a_ , a_ , a_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = TextaTextGenerationPipeline(model=a_ , tokenizer=a_ )
return generator, ["Something to write", "Something else"]
def SCREAMING_SNAKE_CASE (self , a_ , a_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = generator('''Something there''' )
self.assertEqual(a_ , [{'''generated_text''': ANY(a_ )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there''' ) )
__snake_case : List[Any] = generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=a_ )
self.assertEqual(
a_ , [
[{'''generated_text''': ANY(a_ )}, {'''generated_text''': ANY(a_ )}],
[{'''generated_text''': ANY(a_ )}, {'''generated_text''': ANY(a_ )}],
] , )
__snake_case : Optional[int] = generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=a_ )
self.assertEqual(
a_ , [
[{'''generated_text''': ANY(a_ )}, {'''generated_text''': ANY(a_ )}],
[{'''generated_text''': ANY(a_ )}, {'''generated_text''': ANY(a_ )}],
] , )
with self.assertRaises(a_ ):
generator(4 )
@require_torch
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Any = pipeline('''text2text-generation''' , model='''patrickvonplaten/t5-tiny-random''' , framework='''pt''' )
# do_sample=False necessary for reproducibility
__snake_case : int = generator('''Something there''' , do_sample=a_ )
self.assertEqual(a_ , [{'''generated_text''': ''''''}] )
__snake_case : Optional[int] = 3
__snake_case : int = generator(
'''Something there''' , num_return_sequences=a_ , num_beams=a_ , )
__snake_case : Dict = [
{'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
{'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
{'''generated_text''': ''''''},
]
self.assertEqual(a_ , a_ )
__snake_case : List[Any] = generator('''This is a test''' , do_sample=a_ , num_return_sequences=2 , return_tensors=a_ )
self.assertEqual(
a_ , [
{'''generated_token_ids''': ANY(torch.Tensor )},
{'''generated_token_ids''': ANY(torch.Tensor )},
] , )
__snake_case : Dict = generator.model.config.eos_token_id
__snake_case : Any = '''<pad>'''
__snake_case : Tuple = generator(
['''This is a test''', '''This is a second test'''] , do_sample=a_ , num_return_sequences=2 , batch_size=2 , return_tensors=a_ , )
self.assertEqual(
a_ , [
[
{'''generated_token_ids''': ANY(torch.Tensor )},
{'''generated_token_ids''': ANY(torch.Tensor )},
],
[
{'''generated_token_ids''': ANY(torch.Tensor )},
{'''generated_token_ids''': ANY(torch.Tensor )},
],
] , )
@require_tf
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[int] = pipeline('''text2text-generation''' , model='''patrickvonplaten/t5-tiny-random''' , framework='''tf''' )
# do_sample=False necessary for reproducibility
__snake_case : Union[str, Any] = generator('''Something there''' , do_sample=a_ )
self.assertEqual(a_ , [{'''generated_text''': ''''''}] )
| 102 |
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowercase__ = {
"""facebook/maskformer-swin-base-ade""": (
"""https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowercase__ = logging.get_logger(__name__)
class __lowerCamelCase ( A__ ):
'''simple docstring'''
a_ : Optional[int] = """maskformer"""
a_ : Optional[int] = {"""hidden_size""": """mask_feature_size"""}
a_ : Optional[int] = ["""resnet""", """swin"""]
a_ : int = ["""detr"""]
def __init__( self : str , a_ : int = 2_56 , a_ : int = 2_56 , a_ : float = 0.1 , a_ : bool = False , a_ : Optional[Dict] = None , a_ : Optional[Dict] = None , a_ : float = 0.02 , a_ : float = 1.0 , a_ : float = 1.0 , a_ : float = 1.0 , a_ : float = 20.0 , a_ : Optional[bool] = None , **a_ : str , ):
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowerCAmelCase_ : Tuple = SwinConfig(
image_size=3_84 , in_channels=3 , patch_size=4 , embed_dim=1_28 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(a_ , a_ ):
lowerCAmelCase_ : Optional[Any] = backbone_config.pop("model_type" )
lowerCAmelCase_ : Any = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase_ : str = config_class.from_dict(a_ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
f'''Supported model types: {",".join(self.backbones_supported )}''' )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowerCAmelCase_ : Union[str, Any] = DetrConfig()
else:
# verify that the decoder is supported
lowerCAmelCase_ : Optional[Any] = (
decoder_config.pop("model_type" ) if isinstance(a_ , a_ ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f'''Transformer Decoder {decoder_type} not supported, please use one of'''
f''' {",".join(self.decoders_supported )}''' )
if isinstance(a_ , a_ ):
lowerCAmelCase_ : Optional[int] = CONFIG_MAPPING[decoder_type]
lowerCAmelCase_ : List[Any] = config_class.from_dict(a_ )
lowerCAmelCase_ : str = backbone_config
lowerCAmelCase_ : Tuple = decoder_config
# main feature dimension for the model
lowerCAmelCase_ : str = fpn_feature_size
lowerCAmelCase_ : str = mask_feature_size
# initializer
lowerCAmelCase_ : List[Any] = init_std
lowerCAmelCase_ : Tuple = init_xavier_std
# Hungarian matcher && loss
lowerCAmelCase_ : int = cross_entropy_weight
lowerCAmelCase_ : Dict = dice_weight
lowerCAmelCase_ : int = mask_weight
lowerCAmelCase_ : Any = use_auxiliary_loss
lowerCAmelCase_ : Dict = no_object_weight
lowerCAmelCase_ : Optional[int] = output_auxiliary_logits
lowerCAmelCase_ : int = self.decoder_config.encoder_attention_heads
lowerCAmelCase_ : str = self.decoder_config.num_hidden_layers
super().__init__(**a_ )
@classmethod
def lowerCamelCase ( cls : int , a_ : PretrainedConfig , a_ : PretrainedConfig , **a_ : Tuple ):
return cls(
backbone_config=a_ , decoder_config=a_ , **a_ , )
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ : Optional[int] = copy.deepcopy(self.__dict__ )
lowerCAmelCase_ : Optional[Any] = self.backbone_config.to_dict()
lowerCAmelCase_ : Union[str, Any] = self.decoder_config.to_dict()
lowerCAmelCase_ : List[str] = self.__class__.model_type
return output
| 241 | 0 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
__A = pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
__A = dataset.iloc[:, 1:2].values
__A = dataset.iloc[:, 2].values
__A , __A , __A , __A = train_test_split(X, y, test_size=0.2, random_state=0)
__A = PolynomialFeatures(degree=4)
__A = poly_reg.fit_transform(X)
__A = LinearRegression()
pol_reg.fit(X_poly, y)
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
plt.scatter(UpperCamelCase__ , UpperCamelCase__ , color='red' )
plt.plot(UpperCamelCase__ , pol_reg.predict(poly_reg.fit_transform(UpperCamelCase__ ) ) , color='blue' )
plt.title('Truth or Bluff (Linear Regression)' )
plt.xlabel('Position level' )
plt.ylabel('Salary' )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 348 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
| 348 | 1 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
lowerCamelCase = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class A ( datasets.BuilderConfig ):
UpperCamelCase__ : Optional[datasets.Features] =None
def a_ ( SCREAMING_SNAKE_CASE__ : "pyspark.sql.DataFrame" , SCREAMING_SNAKE_CASE__ : List[int] , ):
'''simple docstring'''
import pyspark
def generate_fn():
_lowerCamelCase : str =df.select('*' , pyspark.sql.functions.spark_partition_id().alias('part_id' ) )
for partition_id in partition_order:
_lowerCamelCase : List[str] =df_with_partition_id.select('*' ).where(F'''part_id = {partition_id}''' ).drop('part_id' )
_lowerCamelCase : Tuple =partition_df.collect()
_lowerCamelCase : int =0
for row in rows:
yield F'''{partition_id}_{row_id}''', row.asDict()
row_id += 1
return generate_fn
class A ( _BaseExamplesIterable ):
def __init__( self : Dict , lowercase_ : "pyspark.sql.DataFrame" , lowercase_ : Optional[Any]=None , ) -> Union[str, Any]:
"""simple docstring"""
_lowerCamelCase : Tuple =df
_lowerCamelCase : int =partition_order or range(self.df.rdd.getNumPartitions() )
_lowerCamelCase : Optional[Any] =_generate_iterable_examples(self.df , self.partition_order )
def __iter__( self : List[str] ) -> List[str]:
"""simple docstring"""
yield from self.generate_examples_fn()
def lowerCamelCase ( self : Optional[Any] , lowercase_ : np.random.Generator ) -> "SparkExamplesIterable":
"""simple docstring"""
_lowerCamelCase : Tuple =list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(lowercase_ )
return SparkExamplesIterable(self.df , partition_order=lowercase_ )
def lowerCamelCase ( self : Tuple , lowercase_ : int , lowercase_ : int ) -> "SparkExamplesIterable":
"""simple docstring"""
_lowerCamelCase : Dict =self.split_shard_indices_by_worker(lowercase_ , lowercase_ )
return SparkExamplesIterable(self.df , partition_order=lowercase_ )
@property
def lowerCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
return len(self.partition_order )
class A ( datasets.DatasetBuilder ):
UpperCamelCase__ : str =SparkConfig
def __init__( self : List[str] , lowercase_ : "pyspark.sql.DataFrame" , lowercase_ : str = None , lowercase_ : str = None , **lowercase_ : Tuple , ) -> Tuple:
"""simple docstring"""
import pyspark
_lowerCamelCase : str =pyspark.sql.SparkSession.builder.getOrCreate()
_lowerCamelCase : int =df
_lowerCamelCase : Union[str, Any] =working_dir
super().__init__(
cache_dir=lowercase_ , config_name=str(self.df.semanticHash() ) , **lowercase_ , )
def lowerCamelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
def create_cache_and_write_probe(lowercase_ : Optional[Any] ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=lowercase_ )
_lowerCamelCase : int =os.path.join(self._cache_dir , 'fs_test' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(lowercase_ , 'a' )
return [probe_file]
if self._spark.conf.get('spark.master' , '' ).startswith('local' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
_lowerCamelCase : List[str] =(
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(lowercase_ ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir' )
def lowerCamelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase ( self : int , lowercase_ : datasets.download.download_manager.DownloadManager ) -> List[Any]:
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def lowerCamelCase ( self : Any , lowercase_ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
import pyspark
def get_arrow_batch_size(lowercase_ : List[str] ):
for batch in it:
yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]} )
_lowerCamelCase : Dict =self.df.count()
_lowerCamelCase : Any =df_num_rows if df_num_rows <= 100 else 100
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_lowerCamelCase : List[Any] =(
self.df.limit(lowercase_ )
.repartition(1 )
.mapInArrow(lowercase_ , 'batch_bytes: long' )
.agg(pyspark.sql.functions.sum('batch_bytes' ).alias('sample_bytes' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_lowerCamelCase : List[Any] =approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_lowerCamelCase : str =min(lowercase_ , int(approx_total_size / max_shard_size ) )
_lowerCamelCase : Any =self.df.repartition(lowercase_ )
def lowerCamelCase ( self : Dict , lowercase_ : str , lowercase_ : str , lowercase_ : int , ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
"""simple docstring"""
import pyspark
_lowerCamelCase : Any =ParquetWriter if file_format == 'parquet' else ArrowWriter
_lowerCamelCase : List[Any] =os.path.join(self._working_dir , os.path.basename(lowercase_ ) ) if self._working_dir else fpath
_lowerCamelCase : Optional[Any] =file_format == 'parquet'
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
_lowerCamelCase : Optional[Any] =self.config.features
_lowerCamelCase : Optional[int] =self._writer_batch_size
_lowerCamelCase : List[Any] =self._fs.storage_options
def write_arrow(lowercase_ : Tuple ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_lowerCamelCase : List[str] =pyspark.TaskContext().taskAttemptId()
_lowerCamelCase : Tuple =next(lowercase_ , lowercase_ )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=['task_id', 'num_examples', 'num_bytes'] , )
_lowerCamelCase : str =0
_lowerCamelCase : Optional[int] =writer_class(
features=lowercase_ , path=working_fpath.replace('SSSSS' , F'''{shard_id:05d}''' ).replace('TTTTT' , F'''{task_id:05d}''' ) , writer_batch_size=lowercase_ , storage_options=lowercase_ , embed_local_files=lowercase_ , )
_lowerCamelCase : Optional[Any] =pa.Table.from_batches([first_batch] )
writer.write_table(lowercase_ )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_lowerCamelCase , _lowerCamelCase : Dict =writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , )
shard_id += 1
_lowerCamelCase : Union[str, Any] =writer_class(
features=writer._features , path=working_fpath.replace('SSSSS' , F'''{shard_id:05d}''' ).replace('TTTTT' , F'''{task_id:05d}''' ) , writer_batch_size=lowercase_ , storage_options=lowercase_ , embed_local_files=lowercase_ , )
_lowerCamelCase : Optional[Any] =pa.Table.from_batches([batch] )
writer.write_table(lowercase_ )
if writer._num_bytes > 0:
_lowerCamelCase , _lowerCamelCase : List[Any] =writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(lowercase_ ) ):
_lowerCamelCase : Tuple =os.path.join(os.path.dirname(lowercase_ ) , os.path.basename(lowercase_ ) )
shutil.move(lowercase_ , lowercase_ )
_lowerCamelCase : Dict =(
self.df.mapInArrow(lowercase_ , 'task_id: long, num_examples: long, num_bytes: long' )
.groupBy('task_id' )
.agg(
pyspark.sql.functions.sum('num_examples' ).alias('total_num_examples' ) , pyspark.sql.functions.sum('num_bytes' ).alias('total_num_bytes' ) , pyspark.sql.functions.count('num_bytes' ).alias('num_shards' ) , pyspark.sql.functions.collect_list('num_examples' ).alias('shard_lengths' ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def lowerCamelCase ( self : Union[str, Any] , lowercase_ : "datasets.SplitGenerator" , lowercase_ : str = "arrow" , lowercase_ : Optional[Union[str, int]] = None , lowercase_ : Optional[int] = None , **lowercase_ : Optional[int] , ) -> List[Any]:
"""simple docstring"""
self._validate_cache_dir()
_lowerCamelCase : Dict =convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(lowercase_ )
_lowerCamelCase : List[Any] =not is_remote_filesystem(self._fs )
_lowerCamelCase : Tuple =os.path.join if is_local else posixpath.join
_lowerCamelCase : Optional[Any] ='-TTTTT-SSSSS-of-NNNNN'
_lowerCamelCase : Optional[int] =F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
_lowerCamelCase : int =path_join(self._output_dir , lowercase_ )
_lowerCamelCase : Union[str, Any] =0
_lowerCamelCase : str =0
_lowerCamelCase : Optional[Any] =0
_lowerCamelCase : Any =[]
_lowerCamelCase : Any =[]
for task_id, content in self._prepare_split_single(lowercase_ , lowercase_ , lowercase_ ):
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) : Tuple =content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(lowercase_ )
_lowerCamelCase : Union[str, Any] =total_num_examples
_lowerCamelCase : Tuple =total_num_bytes
# should rename everything at the end
logger.debug(F'''Renaming {total_shards} shards.''' )
if total_shards > 1:
_lowerCamelCase : Any =all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
_lowerCamelCase : Union[str, Any] =self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
lowercase_ : int , lowercase_ : int , lowercase_ : int , ):
rename(
lowercase_ , fpath.replace('SSSSS' , F'''{shard_id:05d}''' ).replace('TTTTT' , F'''{task_id:05d}''' ) , fpath.replace('TTTTT-SSSSS' , F'''{global_shard_id:05d}''' ).replace('NNNNN' , F'''{total_shards:05d}''' ) , )
_lowerCamelCase : str =[]
_lowerCamelCase : Optional[Any] =0
for i in range(len(lowercase_ ) ):
_lowerCamelCase , _lowerCamelCase : str =task_id_and_num_shards[i]
for shard_id in range(lowercase_ ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(lowercase_ , len(lowercase_ ) ).map(lambda lowercase_ : _rename_shard(*lowercase_ ) ).collect()
else:
# don't use any pattern
_lowerCamelCase : Any =0
_lowerCamelCase : Tuple =task_id_and_num_shards[0][0]
self._rename(
fpath.replace('SSSSS' , F'''{shard_id:05d}''' ).replace('TTTTT' , F'''{task_id:05d}''' ) , fpath.replace(lowercase_ , '' ) , )
def lowerCamelCase ( self : Tuple , lowercase_ : "datasets.SplitGenerator" , ) -> SparkExamplesIterable:
"""simple docstring"""
return SparkExamplesIterable(self.df )
| 199 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class A :
UpperCamelCase__ : Union[str, Any] =XGLMConfig
UpperCamelCase__ : Dict ={}
UpperCamelCase__ : Tuple ='gelu'
def __init__( self : List[Any] , lowercase_ : List[str] , lowercase_ : Union[str, Any]=14 , lowercase_ : Dict=7 , lowercase_ : Union[str, Any]=True , lowercase_ : Optional[Any]=True , lowercase_ : Any=True , lowercase_ : Optional[int]=99 , lowercase_ : List[Any]=32 , lowercase_ : List[Any]=2 , lowercase_ : Dict=4 , lowercase_ : List[str]=37 , lowercase_ : int="gelu" , lowercase_ : List[Any]=0.1 , lowercase_ : Union[str, Any]=0.1 , lowercase_ : List[str]=512 , lowercase_ : Union[str, Any]=0.02 , ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase : Dict =parent
_lowerCamelCase : Optional[Any] =batch_size
_lowerCamelCase : Optional[int] =seq_length
_lowerCamelCase : Union[str, Any] =is_training
_lowerCamelCase : Tuple =use_input_mask
_lowerCamelCase : str =use_labels
_lowerCamelCase : Any =vocab_size
_lowerCamelCase : List[str] =d_model
_lowerCamelCase : List[Any] =num_hidden_layers
_lowerCamelCase : Union[str, Any] =num_attention_heads
_lowerCamelCase : List[Any] =ffn_dim
_lowerCamelCase : Optional[Any] =activation_function
_lowerCamelCase : Dict =activation_dropout
_lowerCamelCase : Tuple =attention_dropout
_lowerCamelCase : List[str] =max_position_embeddings
_lowerCamelCase : int =initializer_range
_lowerCamelCase : Optional[int] =None
_lowerCamelCase : Optional[Any] =0
_lowerCamelCase : List[str] =2
_lowerCamelCase : Any =1
def lowerCamelCase ( self : str ) -> int:
"""simple docstring"""
return XGLMConfig.from_pretrained('facebook/xglm-564M' )
def lowerCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] =tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
_lowerCamelCase : Any =None
if self.use_input_mask:
_lowerCamelCase : str =random_attention_mask([self.batch_size, self.seq_length] )
_lowerCamelCase : Optional[int] =self.get_config()
_lowerCamelCase : Optional[Any] =floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def lowerCamelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=lowercase_ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=lowercase_ , )
def lowerCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
_lowerCamelCase : str =self.prepare_config_and_inputs()
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) : Any =config_and_inputs
_lowerCamelCase : Union[str, Any] ={
'input_ids': input_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_tf
class A ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
UpperCamelCase__ : Union[str, Any] =(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
UpperCamelCase__ : List[str] =(TFXGLMForCausalLM,) if is_tf_available() else ()
UpperCamelCase__ : Any =(
{'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
)
UpperCamelCase__ : str =False
UpperCamelCase__ : int =False
UpperCamelCase__ : int =False
def lowerCamelCase ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
_lowerCamelCase : Tuple =TFXGLMModelTester(self )
_lowerCamelCase : str =ConfigTester(self , config_class=lowercase_ , n_embd=37 )
def lowerCamelCase ( self : str ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def lowerCamelCase ( self : Any ) -> int:
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : int =TFXGLMModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
def lowerCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class A ( unittest.TestCase ):
@slow
def lowerCamelCase ( self : str , lowercase_ : str=True ) -> Tuple:
"""simple docstring"""
_lowerCamelCase : Any =TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
_lowerCamelCase : List[Any] =tf.convert_to_tensor([[2, 268, 9865]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
_lowerCamelCase : int =[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
# fmt: on
_lowerCamelCase : Dict =model.generate(lowercase_ , do_sample=lowercase_ , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , lowercase_ )
@slow
def lowerCamelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
_lowerCamelCase : List[str] =XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
_lowerCamelCase : Any =TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
tf.random.set_seed(0 )
_lowerCamelCase : Tuple =tokenizer('Today is a nice day and' , return_tensors='tf' )
_lowerCamelCase : Optional[int] =tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(':/CPU:0' ):
_lowerCamelCase : List[Any] =model.generate(lowercase_ , do_sample=lowercase_ , seed=[7, 0] )
_lowerCamelCase : Union[str, Any] =tokenizer.decode(output_ids[0] , skip_special_tokens=lowercase_ )
_lowerCamelCase : Union[str, Any] =(
'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
)
self.assertEqual(lowercase_ , lowercase_ )
@slow
def lowerCamelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_lowerCamelCase : int =TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
_lowerCamelCase : Any =XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
_lowerCamelCase : Optional[Any] ='left'
# use different length sentences to test batching
_lowerCamelCase : int =[
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When',
'Hello, my dog is a little',
]
_lowerCamelCase : List[Any] =tokenizer(lowercase_ , return_tensors='tf' , padding=lowercase_ )
_lowerCamelCase : int =inputs['input_ids']
_lowerCamelCase : str =model.generate(input_ids=lowercase_ , attention_mask=inputs['attention_mask'] , max_new_tokens=12 )
_lowerCamelCase : Optional[Any] =tokenizer(sentences[0] , return_tensors='tf' ).input_ids
_lowerCamelCase : List[str] =model.generate(input_ids=lowercase_ , max_new_tokens=12 )
_lowerCamelCase : Tuple =tokenizer(sentences[1] , return_tensors='tf' ).input_ids
_lowerCamelCase : Dict =model.generate(input_ids=lowercase_ , max_new_tokens=12 )
_lowerCamelCase : str =tokenizer.batch_decode(lowercase_ , skip_special_tokens=lowercase_ )
_lowerCamelCase : str =tokenizer.decode(output_non_padded[0] , skip_special_tokens=lowercase_ )
_lowerCamelCase : int =tokenizer.decode(output_padded[0] , skip_special_tokens=lowercase_ )
_lowerCamelCase : List[str] =[
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
'a single',
'Hello, my dog is a little bit of a shy one, but he is very friendly',
]
self.assertListEqual(lowercase_ , lowercase_ )
self.assertListEqual(lowercase_ , [non_padded_sentence, padded_sentence] )
| 199 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _lowercase ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase_ = IFInpaintingPipeline
lowercase_ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
lowercase_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowercase_ = PipelineTesterMixin.required_optional_params - {'latents'}
def _UpperCamelCase ( self ) -> str:
return self._get_dummy_components()
def _UpperCamelCase ( self , UpperCAmelCase_ , UpperCAmelCase_=0 ) -> Union[str, Any]:
if str(UpperCAmelCase_ ).startswith('mps' ):
lowerCamelCase : List[Any] = torch.manual_seed(UpperCAmelCase_ )
else:
lowerCamelCase : int = torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
lowerCamelCase : int = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
lowerCamelCase : List[Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
lowerCamelCase : Dict = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def _UpperCamelCase ( self ) -> Any:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def _UpperCamelCase ( self ) -> Dict:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def _UpperCamelCase ( self ) -> str:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def _UpperCamelCase ( self ) -> str:
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def _UpperCamelCase ( self ) -> int:
self._test_save_load_local()
def _UpperCamelCase ( self ) -> List[str]:
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 205 |
"""simple docstring"""
def UpperCAmelCase ( a_, a_ ):
'''simple docstring'''
while b:
lowerCamelCase , lowerCamelCase : Tuple = b, a % b
return a
def UpperCAmelCase ( a_, a_ ):
'''simple docstring'''
return a if b == 0 else euclidean_gcd_recursive(a_, a % b )
def UpperCAmelCase ( ):
'''simple docstring'''
print(F"""euclidean_gcd(3, 5) = {euclidean_gcd(3, 5 )}""" )
print(F"""euclidean_gcd(5, 3) = {euclidean_gcd(5, 3 )}""" )
print(F"""euclidean_gcd(1, 3) = {euclidean_gcd(1, 3 )}""" )
print(F"""euclidean_gcd(3, 6) = {euclidean_gcd(3, 6 )}""" )
print(F"""euclidean_gcd(6, 3) = {euclidean_gcd(6, 3 )}""" )
print(F"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3, 5 )}""" )
print(F"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5, 3 )}""" )
print(F"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1, 3 )}""" )
print(F"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3, 6 )}""" )
print(F"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6, 3 )}""" )
if __name__ == "__main__":
main()
| 205 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _a :
_lowercase : float
_lowercase : TreeNode | None = None
_lowercase : TreeNode | None = None
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def is_valid_tree(SCREAMING_SNAKE_CASE ) -> bool:
if node is None:
return True
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(__lowerCAmelCase ):
raise ValueError(
'''Each node should be type of TreeNode and data should be float.''' )
def is_binary_search_tree_recursive_check(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , __lowerCAmelCase , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , __lowerCAmelCase )
)
return is_binary_search_tree_recursive_check(__lowerCAmelCase , -float('''inf''' ) , float('''inf''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 110 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowerCamelCase : int ={
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ASTConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Union[str, Any] =[
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ASTForAudioClassification''',
'''ASTModel''',
'''ASTPreTrainedModel''',
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Optional[int] =['''ASTFeatureExtractor''']
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
lowerCamelCase : Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 189 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
A_ : Union[str, Any] = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = ['SpeechEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = ['FlaxSpeechEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
A_ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 141 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class _a (__magic_name__ ):
'''simple docstring'''
UpperCAmelCase__: Optional[Any] = '''MCTCTFeatureExtractor'''
UpperCAmelCase__: Optional[int] = '''AutoTokenizer'''
def __init__( self , A__ , A__ ):
super().__init__(A__ , A__ )
A__ : List[str] = self.feature_extractor
A__ : Optional[int] = False
def __call__( self , *A__ , **A__ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*A__ , **A__ )
if "raw_speech" in kwargs:
warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
A__ : Dict = kwargs.pop("""raw_speech""" )
else:
A__ : Tuple = kwargs.pop("""audio""" , A__ )
A__ : Union[str, Any] = kwargs.pop("""sampling_rate""" , A__ )
A__ : int = kwargs.pop("""text""" , A__ )
if len(A__ ) > 0:
A__ : Optional[int] = args[0]
A__ : Dict = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
A__ : List[str] = self.feature_extractor(A__ , *A__ , sampling_rate=A__ , **A__ )
if text is not None:
A__ : Optional[Any] = self.tokenizer(A__ , **A__ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
A__ : List[Any] = encodings["""input_ids"""]
return inputs
def __A ( self , *A__ , **A__ ):
return self.tokenizer.batch_decode(*A__ , **A__ )
def __A ( self , *A__ , **A__ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*A__ , **A__ )
A__ : Optional[Any] = kwargs.pop("""input_features""" , A__ )
A__ : Union[str, Any] = kwargs.pop("""labels""" , A__ )
if len(A__ ) > 0:
A__ : List[Any] = args[0]
A__ : Optional[int] = args[1:]
if input_features is not None:
A__ : Union[str, Any] = self.feature_extractor.pad(A__ , *A__ , **A__ )
if labels is not None:
A__ : List[Any] = self.tokenizer.pad(A__ , **A__ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
A__ : Dict = labels["""input_ids"""]
return input_features
def __A ( self , *A__ , **A__ ):
return self.tokenizer.decode(*A__ , **A__ )
@contextmanager
def __A ( self ):
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your audio inputs, or in a separate call.""" )
A__ : int = True
A__ : List[Any] = self.tokenizer
yield
A__ : Tuple = self.feature_extractor
A__ : Dict = False
| 141 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.