code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def snake_case ( A__ ):
UpperCAmelCase_ : Union[str, Any] = SwinConfig(image_size=1_92 )
if "base" in model_name:
UpperCAmelCase_ : Any = 6
UpperCAmelCase_ : Dict = 1_28
UpperCAmelCase_ : List[Any] = (2, 2, 18, 2)
UpperCAmelCase_ : Dict = (4, 8, 16, 32)
elif "large" in model_name:
UpperCAmelCase_ : List[Any] = 12
UpperCAmelCase_ : List[str] = 1_92
UpperCAmelCase_ : Optional[int] = (2, 2, 18, 2)
UpperCAmelCase_ : Tuple = (6, 12, 24, 48)
else:
raise ValueError("Model not supported, only supports base and large variants" )
UpperCAmelCase_ : List[Any] = window_size
UpperCAmelCase_ : Union[str, Any] = embed_dim
UpperCAmelCase_ : Any = depths
UpperCAmelCase_ : str = num_heads
return config
def snake_case ( A__ ):
if "encoder.mask_token" in name:
UpperCAmelCase_ : Union[str, Any] = name.replace("encoder.mask_token" ,"embeddings.mask_token" )
if "encoder.patch_embed.proj" in name:
UpperCAmelCase_ : Tuple = name.replace("encoder.patch_embed.proj" ,"embeddings.patch_embeddings.projection" )
if "encoder.patch_embed.norm" in name:
UpperCAmelCase_ : str = name.replace("encoder.patch_embed.norm" ,"embeddings.norm" )
if "attn.proj" in name:
UpperCAmelCase_ : Optional[Any] = name.replace("attn.proj" ,"attention.output.dense" )
if "attn" in name:
UpperCAmelCase_ : Tuple = name.replace("attn" ,"attention.self" )
if "norm1" in name:
UpperCAmelCase_ : Optional[int] = name.replace("norm1" ,"layernorm_before" )
if "norm2" in name:
UpperCAmelCase_ : Tuple = name.replace("norm2" ,"layernorm_after" )
if "mlp.fc1" in name:
UpperCAmelCase_ : Union[str, Any] = name.replace("mlp.fc1" ,"intermediate.dense" )
if "mlp.fc2" in name:
UpperCAmelCase_ : Any = name.replace("mlp.fc2" ,"output.dense" )
if name == "encoder.norm.weight":
UpperCAmelCase_ : Any = "layernorm.weight"
if name == "encoder.norm.bias":
UpperCAmelCase_ : str = "layernorm.bias"
if "decoder" in name:
pass
else:
UpperCAmelCase_ : List[Any] = "swin." + name
return name
def snake_case ( A__ ,A__ ):
for key in orig_state_dict.copy().keys():
UpperCAmelCase_ : Tuple = orig_state_dict.pop(A__ )
if "attn_mask" in key:
pass
elif "qkv" in key:
UpperCAmelCase_ : str = key.split("." )
UpperCAmelCase_ : Optional[int] = int(key_split[2] )
UpperCAmelCase_ : str = int(key_split[4] )
UpperCAmelCase_ : Optional[int] = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
UpperCAmelCase_ : List[str] = val[:dim, :]
UpperCAmelCase_ : Any = val[
dim : dim * 2, :
]
UpperCAmelCase_ : Any = val[-dim:, :]
else:
UpperCAmelCase_ : Any = val[
:dim
]
UpperCAmelCase_ : Optional[Any] = val[
dim : dim * 2
]
UpperCAmelCase_ : Optional[Any] = val[
-dim:
]
else:
UpperCAmelCase_ : Any = val
return orig_state_dict
def snake_case ( A__ ,A__ ,A__ ,A__ ):
UpperCAmelCase_ : Tuple = torch.load(A__ ,map_location="cpu" )["model"]
UpperCAmelCase_ : Dict = get_swin_config(A__ )
UpperCAmelCase_ : int = SwinForMaskedImageModeling(A__ )
model.eval()
UpperCAmelCase_ : Dict = convert_state_dict(A__ ,A__ )
model.load_state_dict(A__ )
UpperCAmelCase_ : Dict = "http://images.cocodataset.org/val2017/000000039769.jpg"
UpperCAmelCase_ : Optional[Any] = ViTImageProcessor(size={"height": 1_92, "width": 1_92} )
UpperCAmelCase_ : Tuple = Image.open(requests.get(A__ ,stream=A__ ).raw )
UpperCAmelCase_ : Union[str, Any] = image_processor(images=A__ ,return_tensors="pt" )
with torch.no_grad():
UpperCAmelCase_ : Tuple = model(**A__ ).logits
print(outputs.keys() )
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(A__ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(A__ )
if push_to_hub:
print(F"""Pushing model and image processor for {model_name} to hub""" )
model.push_to_hub(F"""microsoft/{model_name}""" )
image_processor.push_to_hub(F"""microsoft/{model_name}""" )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''swin-base-simmim-window6-192''',
type=str,
choices=['''swin-base-simmim-window6-192''', '''swin-large-simmim-window12-192'''],
help='''Name of the Swin SimMIM model you\'d like to convert.''',
)
parser.add_argument(
'''--checkpoint_path''',
default='''/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth''',
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 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.'''
)
lowerCamelCase_ = parser.parse_args()
convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 95 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
__lowercase = logging.get_logger(__name__)
if is_vision_available():
import PIL
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : Optional[Any] = ["""pixel_values"""]
def __init__( self , __lowercase = True , __lowercase = None , __lowercase = PILImageResampling.BICUBIC , __lowercase = True , __lowercase = None , __lowercase = True , __lowercase = 1 / 255 , __lowercase = True , __lowercase = None , __lowercase = None , __lowercase = True , **__lowercase , ) -> None:
super().__init__(**__lowercase)
__UpperCamelCase :str = size if size is not None else {'''shortest_edge''': 224}
__UpperCamelCase :Tuple = get_size_dict(__lowercase , default_to_square=__lowercase)
__UpperCamelCase :List[Any] = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__UpperCamelCase :Optional[int] = get_size_dict(__lowercase , default_to_square=__lowercase , param_name='''crop_size''')
__UpperCamelCase :List[str] = do_resize
__UpperCamelCase :Any = size
__UpperCamelCase :Dict = resample
__UpperCamelCase :List[Any] = do_center_crop
__UpperCamelCase :Any = crop_size
__UpperCamelCase :Any = do_rescale
__UpperCamelCase :Optional[Any] = rescale_factor
__UpperCamelCase :List[str] = do_normalize
__UpperCamelCase :List[Any] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__UpperCamelCase :int = image_std if image_std is not None else OPENAI_CLIP_STD
__UpperCamelCase :Tuple = do_convert_rgb
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase = PILImageResampling.BICUBIC , __lowercase = None , **__lowercase , ) -> np.ndarray:
__UpperCamelCase :Union[str, Any] = get_size_dict(__lowercase , default_to_square=__lowercase)
if "shortest_edge" not in size:
raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
__UpperCamelCase :Tuple = get_resize_output_image_size(__lowercase , size=size['''shortest_edge'''] , default_to_square=__lowercase)
return resize(__lowercase , size=__lowercase , resample=__lowercase , data_format=__lowercase , **__lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase = None , **__lowercase , ) -> np.ndarray:
__UpperCamelCase :Optional[Any] = get_size_dict(__lowercase)
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""")
return center_crop(__lowercase , size=(size['''height'''], size['''width''']) , data_format=__lowercase , **__lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase = None , **__lowercase , ) -> List[Any]:
return rescale(__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase , ) -> np.ndarray:
return normalize(__lowercase , mean=__lowercase , std=__lowercase , data_format=__lowercase , **__lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , ) -> PIL.Image.Image:
__UpperCamelCase :List[Any] = do_resize if do_resize is not None else self.do_resize
__UpperCamelCase :Optional[int] = size if size is not None else self.size
__UpperCamelCase :int = get_size_dict(__lowercase , param_name='''size''' , default_to_square=__lowercase)
__UpperCamelCase :str = resample if resample is not None else self.resample
__UpperCamelCase :List[str] = do_center_crop if do_center_crop is not None else self.do_center_crop
__UpperCamelCase :Dict = crop_size if crop_size is not None else self.crop_size
__UpperCamelCase :Dict = get_size_dict(__lowercase , param_name='''crop_size''' , default_to_square=__lowercase)
__UpperCamelCase :Dict = do_rescale if do_rescale is not None else self.do_rescale
__UpperCamelCase :str = rescale_factor if rescale_factor is not None else self.rescale_factor
__UpperCamelCase :Tuple = do_normalize if do_normalize is not None else self.do_normalize
__UpperCamelCase :Dict = image_mean if image_mean is not None else self.image_mean
__UpperCamelCase :List[Any] = image_std if image_std is not None else self.image_std
__UpperCamelCase :List[Any] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__UpperCamelCase :Tuple = 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:
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.''')
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__UpperCamelCase :Optional[int] = [convert_to_rgb(__lowercase) for image in images]
# All transformations expect numpy arrays.
__UpperCamelCase :Optional[int] = [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_center_crop:
__UpperCamelCase :List[Any] = [self.center_crop(image=__lowercase , size=__lowercase) for image in images]
if do_rescale:
__UpperCamelCase :str = [self.rescale(image=__lowercase , scale=__lowercase) for image in images]
if do_normalize:
__UpperCamelCase :Union[str, Any] = [self.normalize(image=__lowercase , mean=__lowercase , std=__lowercase) for image in images]
__UpperCamelCase :str = [to_channel_dimension_format(__lowercase , __lowercase) for image in images]
__UpperCamelCase :Tuple = {'''pixel_values''': images}
return BatchFeature(data=__lowercase , tensor_type=__lowercase)
| 167 | 0 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self , _lowerCAmelCase = None ):
if components is None:
lowerCamelCase__ = []
lowerCamelCase__ = list(_lowerCAmelCase )
def __len__( self ):
return len(self.__components )
def __str__( self ):
return "(" + ",".join(map(_lowerCAmelCase , self.__components ) ) + ")"
def __add__( self , _lowerCAmelCase ):
lowerCamelCase__ = len(self )
if size == len(_lowerCAmelCase ):
lowerCamelCase__ = [self.__components[i] + other.component(_lowerCAmelCase ) for i in range(_lowerCAmelCase )]
return Vector(_lowerCAmelCase )
else:
raise Exception("must have the same size" )
def __sub__( self , _lowerCAmelCase ):
lowerCamelCase__ = len(self )
if size == len(_lowerCAmelCase ):
lowerCamelCase__ = [self.__components[i] - other.component(_lowerCAmelCase ) for i in range(_lowerCAmelCase )]
return Vector(_lowerCAmelCase )
else: # error case
raise Exception("must have the same size" )
@overload
def __mul__( self , _lowerCAmelCase ):
...
@overload
def __mul__( self , _lowerCAmelCase ):
...
def __mul__( self , _lowerCAmelCase ):
if isinstance(_lowerCAmelCase , (float, int) ):
lowerCamelCase__ = [c * other for c in self.__components]
return Vector(_lowerCAmelCase )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ) and len(self ) == len(_lowerCAmelCase ):
lowerCamelCase__ = len(self )
lowerCamelCase__ = [self.__components[i] * other.component(_lowerCAmelCase ) for i in range(_lowerCAmelCase )]
return sum(_lowerCAmelCase )
else: # error case
raise Exception("invalid operand!" )
def __magic_name__ ( self ):
return Vector(self.__components )
def __magic_name__ ( self , _lowerCAmelCase ):
if isinstance(_lowerCAmelCase , _lowerCAmelCase ) and -len(self.__components ) <= i < len(self.__components ):
return self.__components[i]
else:
raise Exception("index out of range" )
def __magic_name__ ( self , _lowerCAmelCase , _lowerCAmelCase ):
assert -len(self.__components ) <= pos < len(self.__components )
lowerCamelCase__ = value
def __magic_name__ ( self ):
if len(self.__components ) == 0:
raise Exception("Vector is empty" )
lowerCamelCase__ = [c**2 for c in self.__components]
return math.sqrt(sum(_lowerCAmelCase ) )
def __magic_name__ ( self , _lowerCAmelCase , _lowerCAmelCase = False ):
lowerCamelCase__ = self * other
lowerCamelCase__ = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den ) )
else:
return math.acos(num / den )
def __UpperCamelCase ( a) ->Vector:
assert isinstance(a, a)
return Vector([0] * dimension)
def __UpperCamelCase ( a, a) ->Vector:
assert isinstance(a, a) and (isinstance(a, a))
lowerCamelCase__ = [0] * dimension
lowerCamelCase__ = 1
return Vector(a)
def __UpperCamelCase ( a, a, a) ->Vector:
assert (
isinstance(a, a)
and isinstance(a, a)
and (isinstance(a, (int, float)))
)
return x * scalar + y
def __UpperCamelCase ( a, a, a) ->Vector:
random.seed(a)
lowerCamelCase__ = [random.randint(a, a) for _ in range(a)]
return Vector(a)
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
lowerCamelCase__ = matrix
lowerCamelCase__ = w
lowerCamelCase__ = h
def __str__( self ):
lowerCamelCase__ = ""
for i in range(self.__height ):
ans += "|"
for j in range(self.__width ):
if j < self.__width - 1:
ans += str(self.__matrix[i][j] ) + ","
else:
ans += str(self.__matrix[i][j] ) + "|\n"
return ans
def __add__( self , _lowerCAmelCase ):
if self.__width == other.width() and self.__height == other.height():
lowerCamelCase__ = []
for i in range(self.__height ):
lowerCamelCase__ = [
self.__matrix[i][j] + other.component(_lowerCAmelCase , _lowerCAmelCase )
for j in range(self.__width )
]
matrix.append(_lowerCAmelCase )
return Matrix(_lowerCAmelCase , self.__width , self.__height )
else:
raise Exception("matrix must have the same dimension!" )
def __sub__( self , _lowerCAmelCase ):
if self.__width == other.width() and self.__height == other.height():
lowerCamelCase__ = []
for i in range(self.__height ):
lowerCamelCase__ = [
self.__matrix[i][j] - other.component(_lowerCAmelCase , _lowerCAmelCase )
for j in range(self.__width )
]
matrix.append(_lowerCAmelCase )
return Matrix(_lowerCAmelCase , self.__width , self.__height )
else:
raise Exception("matrices must have the same dimension!" )
@overload
def __mul__( self , _lowerCAmelCase ):
...
@overload
def __mul__( self , _lowerCAmelCase ):
...
def __mul__( self , _lowerCAmelCase ):
if isinstance(_lowerCAmelCase , _lowerCAmelCase ): # matrix-vector
if len(_lowerCAmelCase ) == self.__width:
lowerCamelCase__ = zero_vector(self.__height )
for i in range(self.__height ):
lowerCamelCase__ = [
self.__matrix[i][j] * other.component(_lowerCAmelCase )
for j in range(self.__width )
]
ans.change_component(_lowerCAmelCase , sum(_lowerCAmelCase ) )
return ans
else:
raise Exception(
"vector must have the same size as the "
"number of columns of the matrix!" )
elif isinstance(_lowerCAmelCase , (int, float) ): # matrix-scalar
lowerCamelCase__ = [
[self.__matrix[i][j] * other for j in range(self.__width )]
for i in range(self.__height )
]
return Matrix(_lowerCAmelCase , self.__width , self.__height )
return None
def __magic_name__ ( self ):
return self.__height
def __magic_name__ ( self ):
return self.__width
def __magic_name__ ( self , _lowerCAmelCase , _lowerCAmelCase ):
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception("change_component: indices out of bounds" )
def __magic_name__ ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
if 0 <= x < self.__height and 0 <= y < self.__width:
lowerCamelCase__ = value
else:
raise Exception("change_component: indices out of bounds" )
def __magic_name__ ( self , _lowerCAmelCase , _lowerCAmelCase ):
if self.__height != self.__width:
raise Exception("Matrix is not square" )
lowerCamelCase__ = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(_lowerCAmelCase ) ):
lowerCamelCase__ = minor[i][:y] + minor[i][y + 1 :]
return Matrix(_lowerCAmelCase , self.__width - 1 , self.__height - 1 ).determinant()
def __magic_name__ ( self , _lowerCAmelCase , _lowerCAmelCase ):
if self.__height != self.__width:
raise Exception("Matrix is not square" )
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(_lowerCAmelCase , _lowerCAmelCase )
else:
raise Exception("Indices out of bounds" )
def __magic_name__ ( self ):
if self.__height != self.__width:
raise Exception("Matrix is not square" )
if self.__height < 1:
raise Exception("Matrix has no element" )
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
lowerCamelCase__ = [
self.__matrix[0][y] * self.cofactor(0 , _lowerCAmelCase ) for y in range(self.__width )
]
return sum(_lowerCAmelCase )
def __UpperCamelCase ( a) ->Matrix:
lowerCamelCase__ = [[0] * n for _ in range(a)]
return Matrix(a, a, a)
def __UpperCamelCase ( a, a, a, a) ->Matrix:
random.seed(a)
lowerCamelCase__ = [
[random.randint(a, a) for _ in range(a)] for _ in range(a)
]
return Matrix(a, a, a)
| 360 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
A_ = logging.get_logger(__name__)
A_ = {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
"allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
),
}
class SCREAMING_SNAKE_CASE_ ( lowercase_ ):
"""simple docstring"""
A__ = "longformer"
def __init__( self , _lowerCAmelCase = 512 , _lowerCAmelCase = 2 , _lowerCAmelCase = 1 , _lowerCAmelCase = 0 , _lowerCAmelCase = 2 , _lowerCAmelCase = 3_0522 , _lowerCAmelCase = 768 , _lowerCAmelCase = 12 , _lowerCAmelCase = 12 , _lowerCAmelCase = 3072 , _lowerCAmelCase = "gelu" , _lowerCAmelCase = 0.1 , _lowerCAmelCase = 0.1 , _lowerCAmelCase = 512 , _lowerCAmelCase = 2 , _lowerCAmelCase = 0.02 , _lowerCAmelCase = 1E-12 , _lowerCAmelCase = False , **_lowerCAmelCase , ):
super().__init__(pad_token_id=_lowerCAmelCase , **_lowerCAmelCase )
lowerCamelCase__ = attention_window
lowerCamelCase__ = sep_token_id
lowerCamelCase__ = bos_token_id
lowerCamelCase__ = eos_token_id
lowerCamelCase__ = vocab_size
lowerCamelCase__ = hidden_size
lowerCamelCase__ = num_hidden_layers
lowerCamelCase__ = num_attention_heads
lowerCamelCase__ = hidden_act
lowerCamelCase__ = intermediate_size
lowerCamelCase__ = hidden_dropout_prob
lowerCamelCase__ = attention_probs_dropout_prob
lowerCamelCase__ = max_position_embeddings
lowerCamelCase__ = type_vocab_size
lowerCamelCase__ = initializer_range
lowerCamelCase__ = layer_norm_eps
lowerCamelCase__ = onnx_export
class SCREAMING_SNAKE_CASE_ ( lowercase_ ):
"""simple docstring"""
def __init__( self , _lowerCAmelCase , _lowerCAmelCase = "default" , _lowerCAmelCase = None ):
super().__init__(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowerCamelCase__ = True
@property
def __magic_name__ ( self ):
if self.task == "multiple-choice":
lowerCamelCase__ = {0: "batch", 1: "choice", 2: "sequence"}
else:
lowerCamelCase__ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("global_attention_mask", dynamic_axis),
] )
@property
def __magic_name__ ( self ):
lowerCamelCase__ = super().outputs
if self.task == "default":
lowerCamelCase__ = {0: "batch"}
return outputs
@property
def __magic_name__ ( self ):
return 1E-4
@property
def __magic_name__ ( self ):
# needs to be >= 14 to support tril operator
return max(super().default_onnx_opset , 14 )
def __magic_name__ ( self , _lowerCAmelCase , _lowerCAmelCase = -1 , _lowerCAmelCase = -1 , _lowerCAmelCase = False , _lowerCAmelCase = None , ):
lowerCamelCase__ = super().generate_dummy_inputs(
preprocessor=_lowerCAmelCase , batch_size=_lowerCAmelCase , seq_length=_lowerCAmelCase , is_pair=_lowerCAmelCase , framework=_lowerCAmelCase )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
lowerCamelCase__ = torch.zeros_like(inputs["input_ids"] )
# make every second token global
lowerCamelCase__ = 1
return inputs
| 360 | 1 |
'''simple docstring'''
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__UpperCamelCase = Mapping[str, np.ndarray]
__UpperCamelCase = Mapping[str, Any] # Is a nested dict.
__UpperCamelCase = 0.01
@dataclasses.dataclass(frozen=__lowercase )
class _A :
lowercase__: np.ndarray # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
lowercase__: np.ndarray # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
lowercase__: np.ndarray # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
lowercase__: np.ndarray # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
lowercase__: np.ndarray # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
lowercase__: Optional[np.ndarray] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
lowercase__: Optional[str] = None
# Templates used to generate this protein (prediction-only)
lowercase__: Optional[Sequence[str]] = None
# Chain corresponding to each parent
lowercase__: Optional[Sequence[int]] = None
def _a ( _lowerCamelCase ) -> Protein:
"""simple docstring"""
__snake_case : List[str] = R"""(\[[A-Z]+\]\n)"""
__snake_case : List[str] = [tag.strip() for tag in re.split(_lowerCamelCase , _lowerCamelCase ) if len(_lowerCamelCase ) > 0]
__snake_case : Iterator[Tuple[str, List[str]]] = zip(tags[0::2] , [l.split("""\n""" ) for l in tags[1::2]] )
__snake_case : List[str] = ["N", "CA", "C"]
__snake_case : Union[str, Any] = None
__snake_case : Optional[Any] = None
__snake_case : str = None
for g in groups:
if "[PRIMARY]" == g[0]:
__snake_case : Any = g[1][0].strip()
for i in range(len(_lowerCamelCase ) ):
if seq[i] not in residue_constants.restypes:
__snake_case : Union[str, Any] = """X""" # FIXME: strings are immutable
__snake_case : Optional[Any] = np.array(
[residue_constants.restype_order.get(_lowerCamelCase , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
__snake_case : List[List[float]] = []
for axis in range(3 ):
tertiary.append(list(map(_lowerCamelCase , g[1][axis].split() ) ) )
__snake_case : Optional[Any] = np.array(_lowerCamelCase )
__snake_case : Any = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(_lowerCamelCase ):
__snake_case : Union[str, Any] = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
__snake_case : Union[str, Any] = np.array(list(map({"""-""": 0, """+""": 1}.get , g[1][0].strip() ) ) )
__snake_case : List[str] = np.zeros(
(
len(_lowerCamelCase ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(_lowerCamelCase ):
__snake_case : Optional[Any] = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=_lowerCamelCase , atom_mask=_lowerCamelCase , aatype=_lowerCamelCase , residue_index=np.arange(len(_lowerCamelCase ) ) , b_factors=_lowerCamelCase , )
def _a ( _lowerCamelCase , _lowerCamelCase = 0 ) -> List[str]:
"""simple docstring"""
__snake_case : List[str] = []
__snake_case : Tuple = prot.remark
if remark is not None:
pdb_headers.append(F'''REMARK {remark}''' )
__snake_case : Optional[int] = prot.parents
__snake_case : Optional[int] = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
__snake_case : Any = [p for i, p in zip(_lowerCamelCase , _lowerCamelCase ) if i == chain_id]
if parents is None or len(_lowerCamelCase ) == 0:
__snake_case : str = ["""N/A"""]
pdb_headers.append(F'''PARENT {" ".join(_lowerCamelCase )}''' )
return pdb_headers
def _a ( _lowerCamelCase , _lowerCamelCase ) -> str:
"""simple docstring"""
__snake_case : List[str] = []
__snake_case : int = pdb_str.split("""\n""" )
__snake_case : Union[str, Any] = prot.remark
if remark is not None:
out_pdb_lines.append(F'''REMARK {remark}''' )
__snake_case : List[List[str]]
if prot.parents is not None and len(prot.parents ) > 0:
__snake_case : Union[str, Any] = []
if prot.parents_chain_index is not None:
__snake_case : Dict[str, List[str]] = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(_lowerCamelCase ) , [] )
parent_dict[str(_lowerCamelCase )].append(_lowerCamelCase )
__snake_case : Optional[int] = max([int(_lowerCamelCase ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
__snake_case : Tuple = parent_dict.get(str(_lowerCamelCase ) , ["""N/A"""] )
parents_per_chain.append(_lowerCamelCase )
else:
parents_per_chain.append(list(prot.parents ) )
else:
__snake_case : Dict = [["""N/A"""]]
def make_parent_line(_lowerCamelCase ) -> str:
return F'''PARENT {" ".join(_lowerCamelCase )}'''
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
__snake_case : Any = 0
for i, l in enumerate(_lowerCamelCase ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(_lowerCamelCase )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(_lowerCamelCase ):
__snake_case : int = parents_per_chain[chain_counter]
else:
__snake_case : int = ["""N/A"""]
out_pdb_lines.append(make_parent_line(_lowerCamelCase ) )
return "\n".join(_lowerCamelCase )
def _a ( _lowerCamelCase ) -> str:
"""simple docstring"""
__snake_case : Optional[int] = residue_constants.restypes + ["""X"""]
def res_atoa(_lowerCamelCase ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , """UNK""" )
__snake_case : Union[str, Any] = residue_constants.atom_types
__snake_case : List[str] = []
__snake_case : int = prot.atom_mask
__snake_case : Optional[int] = prot.aatype
__snake_case : List[str] = prot.atom_positions
__snake_case : str = prot.residue_index.astype(np.intaa )
__snake_case : Optional[int] = prot.b_factors
__snake_case : str = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError("""Invalid aatypes.""" )
__snake_case : Optional[int] = get_pdb_headers(_lowerCamelCase )
if len(_lowerCamelCase ) > 0:
pdb_lines.extend(_lowerCamelCase )
__snake_case : Dict = aatype.shape[0]
__snake_case : Optional[int] = 1
__snake_case : List[str] = 0
__snake_case : Optional[Any] = string.ascii_uppercase
__snake_case : Tuple = None
# Add all atom sites.
for i in range(_lowerCamelCase ):
__snake_case : Optional[Any] = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(_lowerCamelCase , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
__snake_case : int = """ATOM"""
__snake_case : int = atom_name if len(_lowerCamelCase ) == 4 else F''' {atom_name}'''
__snake_case : List[str] = """"""
__snake_case : Optional[Any] = """"""
__snake_case : Any = 1.00
__snake_case : Optional[Any] = atom_name[0] # Protein supports only C, N, O, S, this works.
__snake_case : List[Any] = """"""
__snake_case : Tuple = """A"""
if chain_index is not None:
__snake_case : Optional[int] = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
__snake_case : Optional[int] = (
F'''{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'''
F'''{res_name_a:>3} {chain_tag:>1}'''
F'''{residue_index[i]:>4}{insertion_code:>1} '''
F'''{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'''
F'''{occupancy:>6.2f}{b_factor:>6.2f} '''
F'''{element:>2}{charge:>2}'''
)
pdb_lines.append(_lowerCamelCase )
atom_index += 1
__snake_case : List[str] = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
__snake_case : Optional[Any] = True
__snake_case : Union[str, Any] = chain_index[i + 1]
if should_terminate:
# Close the chain.
__snake_case : Optional[Any] = """TER"""
__snake_case : List[str] = (
F'''{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}'''
)
pdb_lines.append(_lowerCamelCase )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(_lowerCamelCase , _lowerCamelCase ) )
pdb_lines.append("""END""" )
pdb_lines.append("""""" )
return "\n".join(_lowerCamelCase )
def _a ( _lowerCamelCase ) -> np.ndarray:
"""simple docstring"""
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , ) -> Protein:
"""simple docstring"""
return Protein(
aatype=features["""aatype"""] , atom_positions=result["""final_atom_positions"""] , atom_mask=result["""final_atom_mask"""] , residue_index=features["""residue_index"""] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result["""final_atom_mask"""] ) , chain_index=_lowerCamelCase , remark=_lowerCamelCase , parents=_lowerCamelCase , parents_chain_index=_lowerCamelCase , )
| 26 |
'''simple docstring'''
def _a ( _lowerCamelCase ) -> int:
"""simple docstring"""
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("""only integers accepted as input""" )
else:
__snake_case : List[Any] = str(abs(_lowerCamelCase ) )
__snake_case : Union[str, Any] = [list(_lowerCamelCase ) for char in range(len(_lowerCamelCase ) )]
for index in range(len(_lowerCamelCase ) ):
num_transpositions[index].pop(_lowerCamelCase )
return max(
int("""""".join(list(_lowerCamelCase ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("doctest").testmod()
| 26 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Any , snake_case__ : Optional[Any] , snake_case__ : Optional[int]=1_3 , snake_case__ : Dict=1_0 , snake_case__ : int=3 , snake_case__ : Any=2 , snake_case__ : Tuple=2 , snake_case__ : Tuple=True , snake_case__ : Any=True , snake_case__ : Union[str, Any]=3_2 , snake_case__ : Tuple=5 , snake_case__ : List[Any]=4 , snake_case__ : str=3_7 , snake_case__ : Union[str, Any]="gelu" , snake_case__ : List[Any]=0.1 , snake_case__ : List[Any]=0.1 , snake_case__ : Any=1_0 , snake_case__ : Optional[Any]=0.02 , snake_case__ : List[str]="divided_space_time" , snake_case__ : List[str]=None , ) -> Dict:
_lowerCamelCase = parent
_lowerCamelCase = batch_size
_lowerCamelCase = image_size
_lowerCamelCase = num_channels
_lowerCamelCase = patch_size
_lowerCamelCase = num_frames
_lowerCamelCase = is_training
_lowerCamelCase = use_labels
_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 = attention_type
_lowerCamelCase = initializer_range
_lowerCamelCase = scope
_lowerCamelCase = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
_lowerCamelCase = (image_size // patch_size) ** 2
_lowerCamelCase = (num_frames) * self.num_patches_per_frame + 1
def _snake_case ( self : Union[str, Any] ) -> Optional[int]:
_lowerCamelCase = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
_lowerCamelCase = None
if self.use_labels:
_lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
_lowerCamelCase = self.get_config()
return config, pixel_values, labels
def _snake_case ( self : Tuple ) -> Optional[int]:
_lowerCamelCase = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
_lowerCamelCase = self.num_labels
return config
def _snake_case ( self : Union[str, Any] , snake_case__ : Dict , snake_case__ : Optional[Any] , snake_case__ : Union[str, Any] ) -> Tuple:
_lowerCamelCase = TimesformerModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
_lowerCamelCase = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self : Dict , snake_case__ : Optional[Any] , snake_case__ : str , snake_case__ : str ) -> List[Any]:
_lowerCamelCase = TimesformerForVideoClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
_lowerCamelCase = model(snake_case__ )
# verify the logits shape
_lowerCamelCase = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , snake_case__ )
def _snake_case ( self : List[Any] ) -> List[str]:
_lowerCamelCase = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase = config_and_inputs
_lowerCamelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
lowerCAmelCase_ = (
{'feature-extraction': TimesformerModel, 'video-classification': TimesformerForVideoClassification}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def _snake_case ( self : List[Any] ) -> List[Any]:
_lowerCamelCase = TimesformerModelTester(self )
_lowerCamelCase = ConfigTester(
self , config_class=snake_case__ , has_text_modality=snake_case__ , hidden_size=3_7 )
def _snake_case ( self : Tuple , snake_case__ : List[str] , snake_case__ : Tuple , snake_case__ : Tuple=False ) -> List[str]:
_lowerCamelCase = copy.deepcopy(snake_case__ )
if return_labels:
if model_class in get_values(snake_case__ ):
_lowerCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case__ )
return inputs_dict
def _snake_case ( self : List[Any] ) -> str:
self.config_tester.run_common_tests()
@unittest.skip(reason='TimeSformer does not use inputs_embeds' )
def _snake_case ( self : str ) -> int:
pass
def _snake_case ( self : Tuple ) -> Union[str, 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__ )
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[str] ) -> 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 : Tuple ) -> Dict:
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def _snake_case ( self : Dict ) -> Union[str, Any]:
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*snake_case__ )
@slow
def _snake_case ( self : Tuple ) -> Dict:
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase = TimesformerModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def _snake_case ( self : Any ) -> Any:
if not self.has_attentions:
pass
else:
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase = True
for model_class in self.all_model_classes:
_lowerCamelCase = self.model_tester.seq_length
_lowerCamelCase = self.model_tester.num_frames
_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
self.assertEqual(len(snake_case__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_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
self.assertEqual(len(snake_case__ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
_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__ ) )
self.assertEqual(out_len + 1 , len(snake_case__ ) )
_lowerCamelCase = outputs.attentions
self.assertEqual(len(snake_case__ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def _snake_case ( self : Optional[Any] ) -> Optional[int]:
def check_hidden_states_output(snake_case__ : List[str] , snake_case__ : Optional[int] , snake_case__ : 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 = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(snake_case__ ) , snake_case__ )
_lowerCamelCase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = True
check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase = True
check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ )
def lowerCamelCase ( ) -> str:
_lowerCamelCase = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
_lowerCamelCase = np.load(UpperCamelCase )
return list(UpperCamelCase )
@require_torch
@require_vision
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _snake_case ( self : Tuple ) -> Tuple:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def _snake_case ( self : Dict ) -> Any:
_lowerCamelCase = TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400' ).to(
snake_case__ )
_lowerCamelCase = self.default_image_processor
_lowerCamelCase = prepare_video()
_lowerCamelCase = image_processor(video[:8] , return_tensors='pt' ).to(snake_case__ )
# forward pass
with torch.no_grad():
_lowerCamelCase = model(**snake_case__ )
# verify the logits
_lowerCamelCase = torch.Size((1, 4_0_0) )
self.assertEqual(outputs.logits.shape , snake_case__ )
_lowerCamelCase = torch.tensor([-0.3016, -0.7713, -0.4205] ).to(snake_case__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case__ , atol=1e-4 ) ) | 234 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A = logging.get_logger(__name__)
def lowerCamelCase ( UpperCamelCase : str , UpperCamelCase : Any=False , UpperCamelCase : Union[str, Any]=False , UpperCamelCase : List[Any]=False ) -> List[str]:
_lowerCamelCase = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""transformer.blocks.{i}.norm1.weight""", F"""vilt.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""transformer.blocks.{i}.norm1.bias""", F"""vilt.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(F"""transformer.blocks.{i}.attn.proj.weight""", F"""vilt.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append(
(F"""transformer.blocks.{i}.attn.proj.bias""", F"""vilt.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""transformer.blocks.{i}.norm2.weight""", F"""vilt.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""transformer.blocks.{i}.norm2.bias""", F"""vilt.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append(
(F"""transformer.blocks.{i}.mlp.fc1.weight""", F"""vilt.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""transformer.blocks.{i}.mlp.fc1.bias""", F"""vilt.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""transformer.blocks.{i}.mlp.fc2.weight""", F"""vilt.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""transformer.blocks.{i}.mlp.fc2.bias""", F"""vilt.encoder.layer.{i}.output.dense.bias""") )
# embeddings
rename_keys.extend(
[
# text embeddings
('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
(
'text_embeddings.position_embeddings.weight',
'vilt.embeddings.text_embeddings.position_embeddings.weight',
),
('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
(
'text_embeddings.token_type_embeddings.weight',
'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
),
('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
# patch embeddings
('transformer.cls_token', 'vilt.embeddings.cls_token'),
('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
# token type embeddings
('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
] )
# final layernorm + pooler
rename_keys.extend(
[
('transformer.norm.weight', 'vilt.layernorm.weight'),
('transformer.norm.bias', 'vilt.layernorm.bias'),
('pooler.dense.weight', 'vilt.pooler.dense.weight'),
('pooler.dense.bias', 'vilt.pooler.dense.bias'),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('vqa_classifier.0.weight', 'classifier.0.weight'),
('vqa_classifier.0.bias', 'classifier.0.bias'),
('vqa_classifier.1.weight', 'classifier.1.weight'),
('vqa_classifier.1.bias', 'classifier.1.bias'),
('vqa_classifier.3.weight', 'classifier.3.weight'),
('vqa_classifier.3.bias', 'classifier.3.bias'),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('nlvr2_classifier.0.weight', 'classifier.0.weight'),
('nlvr2_classifier.0.bias', 'classifier.0.bias'),
('nlvr2_classifier.1.weight', 'classifier.1.weight'),
('nlvr2_classifier.1.bias', 'classifier.1.bias'),
('nlvr2_classifier.3.weight', 'classifier.3.weight'),
('nlvr2_classifier.3.bias', 'classifier.3.bias'),
] )
else:
pass
return rename_keys
def lowerCamelCase ( UpperCamelCase : str , UpperCamelCase : str ) -> Tuple:
for i in range(config.num_hidden_layers ):
_lowerCamelCase = 'vilt.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowerCamelCase = state_dict.pop(F"""transformer.blocks.{i}.attn.qkv.weight""" )
_lowerCamelCase = state_dict.pop(F"""transformer.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 ( UpperCamelCase : Tuple ) -> List[Any]:
_lowerCamelCase = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(UpperCamelCase , UpperCamelCase )
def lowerCamelCase ( UpperCamelCase : Tuple , UpperCamelCase : Optional[Any] , UpperCamelCase : str ) -> Any:
_lowerCamelCase = dct.pop(UpperCamelCase )
_lowerCamelCase = val
@torch.no_grad()
def lowerCamelCase ( UpperCamelCase : int , UpperCamelCase : int ) -> Optional[int]:
_lowerCamelCase = ViltConfig(image_size=3_84 , patch_size=32 , tie_word_embeddings=UpperCamelCase )
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
if "vqa" in checkpoint_url:
_lowerCamelCase = True
_lowerCamelCase = 31_29
_lowerCamelCase = 'huggingface/label-files'
_lowerCamelCase = 'vqa2-id2label.json'
_lowerCamelCase = json.load(open(hf_hub_download(UpperCamelCase , UpperCamelCase , repo_type='dataset' ) , 'r' ) )
_lowerCamelCase = {int(UpperCamelCase ): v for k, v in idalabel.items()}
_lowerCamelCase = idalabel
_lowerCamelCase = {v: k for k, v in idalabel.items()}
_lowerCamelCase = ViltForQuestionAnswering(UpperCamelCase )
elif "nlvr" in checkpoint_url:
_lowerCamelCase = True
_lowerCamelCase = 2
_lowerCamelCase = {0: 'False', 1: 'True'}
_lowerCamelCase = {v: k for k, v in config.idalabel.items()}
_lowerCamelCase = 3
_lowerCamelCase = ViltForImagesAndTextClassification(UpperCamelCase )
elif "irtr" in checkpoint_url:
_lowerCamelCase = True
_lowerCamelCase = ViltForImageAndTextRetrieval(UpperCamelCase )
elif "mlm_itm" in checkpoint_url:
_lowerCamelCase = True
_lowerCamelCase = ViltForMaskedLM(UpperCamelCase )
else:
raise ValueError('Unknown model type' )
# load state_dict of original model, remove and rename some keys
_lowerCamelCase = torch.hub.load_state_dict_from_url(UpperCamelCase , map_location='cpu' )['state_dict']
_lowerCamelCase = create_rename_keys(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
for src, dest in rename_keys:
rename_key(UpperCamelCase , UpperCamelCase , UpperCamelCase )
read_in_q_k_v(UpperCamelCase , UpperCamelCase )
if mlm_model or irtr_model:
_lowerCamelCase = ['itm_score.fc.weight', 'itm_score.fc.bias']
for k in ignore_keys:
state_dict.pop(UpperCamelCase , UpperCamelCase )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
_lowerCamelCase , _lowerCamelCase = model.load_state_dict(UpperCamelCase , strict=UpperCamelCase )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(UpperCamelCase )
# Define processor
_lowerCamelCase = ViltImageProcessor(size=3_84 )
_lowerCamelCase = BertTokenizer.from_pretrained('bert-base-uncased' )
_lowerCamelCase = ViltProcessor(UpperCamelCase , UpperCamelCase )
# Forward pass on example inputs (image + text)
if nlvr_model:
_lowerCamelCase = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=UpperCamelCase ).raw )
_lowerCamelCase = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=UpperCamelCase ).raw )
_lowerCamelCase = (
'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
' standing.'
)
_lowerCamelCase = processor(UpperCamelCase , UpperCamelCase , return_tensors='pt' )
_lowerCamelCase = processor(UpperCamelCase , UpperCamelCase , return_tensors='pt' )
_lowerCamelCase = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
_lowerCamelCase = Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg' , stream=UpperCamelCase ).raw )
if mlm_model:
_lowerCamelCase = 'a bunch of [MASK] laying on a [MASK].'
else:
_lowerCamelCase = 'How many cats are there?'
_lowerCamelCase = processor(UpperCamelCase , UpperCamelCase , return_tensors='pt' )
_lowerCamelCase = model(**UpperCamelCase )
# Verify outputs
if mlm_model:
_lowerCamelCase = torch.Size([1, 11, 3_05_22] )
_lowerCamelCase = torch.tensor([-12.5_061, -12.5_123, -12.5_174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , UpperCamelCase , atol=1e-4 )
# verify masked token prediction equals "cats"
_lowerCamelCase = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
_lowerCamelCase = torch.Size([1, 31_29] )
_lowerCamelCase = torch.tensor([-15.9_495, -18.1_472, -10.3_041] )
assert torch.allclose(outputs.logits[0, :3] , UpperCamelCase , atol=1e-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , UpperCamelCase , atol=1e-4 )
# verify vqa prediction equals "2"
_lowerCamelCase = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
_lowerCamelCase = torch.Size([1, 2] )
_lowerCamelCase = torch.tensor([-2.8_721, 2.1_291] )
assert torch.allclose(outputs.logits[0, :3] , UpperCamelCase , atol=1e-4 )
assert outputs.logits.shape == expected_shape
Path(UpperCamelCase ).mkdir(exist_ok=UpperCamelCase )
print(F"""Saving model and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCamelCase )
processor.save_pretrained(UpperCamelCase )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt',
type=str,
help='URL of the checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
A = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path) | 234 | 1 |
'''simple docstring'''
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
A : str = None
try:
import msvcrt
except ImportError:
A : int = None
try:
import fcntl
except ImportError:
A : Any = None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
A : str = OSError
# Data
# ------------------------------------------------
A : Union[str, Any] = [
'Timeout',
'BaseFileLock',
'WindowsFileLock',
'UnixFileLock',
'SoftFileLock',
'FileLock',
]
A : List[Any] = '3.0.12'
A : Dict = None
def _a ( ):
global _logger
snake_case : Dict =_logger or logging.getLogger(__name__ )
return _logger
class lowerCAmelCase_ ( __UpperCamelCase ):
def __init__( self : Optional[int], _snake_case : Optional[Any] ):
'''simple docstring'''
snake_case : Optional[int] =lock_file
return None
def __str__( self : Optional[Any] ):
'''simple docstring'''
snake_case : str =f'''The file lock \'{self.lock_file}\' could not be acquired.'''
return temp
class lowerCAmelCase_ :
def __init__( self : int, _snake_case : Optional[int] ):
'''simple docstring'''
snake_case : int =lock
return None
def __enter__( self : str ):
'''simple docstring'''
return self.lock
def __exit__( self : Tuple, _snake_case : Dict, _snake_case : Union[str, Any], _snake_case : str ):
'''simple docstring'''
self.lock.release()
return None
class lowerCAmelCase_ :
def __init__( self : Any, _snake_case : List[Any], _snake_case : List[Any]=-1, _snake_case : Union[str, Any]=None ):
'''simple docstring'''
snake_case : str =max_filename_length if max_filename_length is not None else 255
# Hash the filename if it's too long
snake_case : List[str] =self.hash_filename_if_too_long(__snake_case, __snake_case )
# The path to the lock file.
snake_case : int =lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
snake_case : Union[str, Any] =None
# The default timeout value.
snake_case : List[str] =timeout
# We use this lock primarily for the lock counter.
snake_case : str =threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
snake_case : int =0
return None
@property
def __snake_case ( self : Optional[Any] ):
'''simple docstring'''
return self._lock_file
@property
def __snake_case ( self : str ):
'''simple docstring'''
return self._timeout
@timeout.setter
def __snake_case ( self : Union[str, Any], _snake_case : Optional[Any] ):
'''simple docstring'''
snake_case : List[Any] =float(__snake_case )
return None
def __snake_case ( self : Union[str, Any] ):
'''simple docstring'''
raise NotImplementedError()
def __snake_case ( self : Any ):
'''simple docstring'''
raise NotImplementedError()
@property
def __snake_case ( self : List[str] ):
'''simple docstring'''
return self._lock_file_fd is not None
def __snake_case ( self : Dict, _snake_case : str=None, _snake_case : Dict=0.05 ):
'''simple docstring'''
if timeout is None:
snake_case : Optional[Any] =self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
snake_case : Any =id(self )
snake_case : int =self._lock_file
snake_case : Tuple =time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(f'''Attempting to acquire lock {lock_id} on {lock_filename}''' )
self._acquire()
if self.is_locked:
logger().debug(f'''Lock {lock_id} acquired on {lock_filename}''' )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(f'''Timeout on acquiring lock {lock_id} on {lock_filename}''' )
raise Timeout(self._lock_file )
else:
logger().debug(
f'''Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...''' )
time.sleep(__snake_case )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
snake_case : Tuple =max(0, self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def __snake_case ( self : Union[str, Any], _snake_case : Tuple=False ):
'''simple docstring'''
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
snake_case : str =id(self )
snake_case : int =self._lock_file
logger().debug(f'''Attempting to release lock {lock_id} on {lock_filename}''' )
self._release()
snake_case : Optional[Any] =0
logger().debug(f'''Lock {lock_id} released on {lock_filename}''' )
return None
def __enter__( self : Dict ):
'''simple docstring'''
self.acquire()
return self
def __exit__( self : Dict, _snake_case : List[Any], _snake_case : Any, _snake_case : List[Any] ):
'''simple docstring'''
self.release()
return None
def __del__( self : int ):
'''simple docstring'''
self.release(force=__snake_case )
return None
def __snake_case ( self : Tuple, _snake_case : str, _snake_case : int ):
'''simple docstring'''
snake_case : Dict =os.path.basename(__snake_case )
if len(__snake_case ) > max_length and max_length > 0:
snake_case : Any =os.path.dirname(__snake_case )
snake_case : Dict =str(hash(__snake_case ) )
snake_case : List[Any] =filename[: max_length - len(__snake_case ) - 8] + '''...''' + hashed_filename + '''.lock'''
return os.path.join(__snake_case, __snake_case )
else:
return path
class lowerCAmelCase_ ( __UpperCamelCase ):
def __init__( self : int, _snake_case : Union[str, Any], _snake_case : Union[str, Any]=-1, _snake_case : List[Any]=None ):
'''simple docstring'''
from .file_utils import relative_to_absolute_path
super().__init__(__snake_case, timeout=__snake_case, max_filename_length=__snake_case )
snake_case : List[str] ='''\\\\?\\''' + relative_to_absolute_path(self.lock_file )
def __snake_case ( self : str ):
'''simple docstring'''
snake_case : Optional[Any] =os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
snake_case : str =os.open(self._lock_file, __snake_case )
except OSError:
pass
else:
try:
msvcrt.locking(__snake_case, msvcrt.LK_NBLCK, 1 )
except OSError:
os.close(__snake_case )
else:
snake_case : Union[str, Any] =fd
return None
def __snake_case ( self : str ):
'''simple docstring'''
snake_case : Union[str, Any] =self._lock_file_fd
snake_case : Optional[int] =None
msvcrt.locking(__snake_case, msvcrt.LK_UNLCK, 1 )
os.close(__snake_case )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class lowerCAmelCase_ ( __UpperCamelCase ):
def __init__( self : List[str], _snake_case : Any, _snake_case : List[Any]=-1, _snake_case : List[str]=None ):
'''simple docstring'''
snake_case : str =os.statvfs(os.path.dirname(__snake_case ) ).f_namemax
super().__init__(__snake_case, timeout=__snake_case, max_filename_length=__snake_case )
def __snake_case ( self : Tuple ):
'''simple docstring'''
snake_case : Optional[int] =os.O_RDWR | os.O_CREAT | os.O_TRUNC
snake_case : str =os.open(self._lock_file, __snake_case )
try:
fcntl.flock(__snake_case, fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(__snake_case )
else:
snake_case : List[Any] =fd
return None
def __snake_case ( self : Tuple ):
'''simple docstring'''
snake_case : int =self._lock_file_fd
snake_case : List[str] =None
fcntl.flock(__snake_case, fcntl.LOCK_UN )
os.close(__snake_case )
return None
class lowerCAmelCase_ ( __UpperCamelCase ):
def __snake_case ( self : Union[str, Any] ):
'''simple docstring'''
snake_case : Union[str, Any] =os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
snake_case : Any =os.open(self._lock_file, __snake_case )
except OSError:
pass
else:
snake_case : str =fd
return None
def __snake_case ( self : Any ):
'''simple docstring'''
os.close(self._lock_file_fd )
snake_case : Union[str, Any] =None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
A : Any = None
if msvcrt:
A : Tuple = WindowsFileLock
elif fcntl:
A : int = UnixFileLock
else:
A : List[str] = SoftFileLock
if warnings is not None:
warnings.warn("""only soft file lock is available""")
| 349 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / 'utils'))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
_lowerCAmelCase : Dict = get_tests_dir('fixtures')
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ ( self : str ) -> str:
'''simple docstring'''
lowerCamelCase = mock.Mock()
lowerCamelCase = 500
lowerCamelCase = {}
lowerCamelCase = HTTPError
lowerCamelCase = {}
# Download this model to make sure it's in the cache.
lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=__snake_case ) as mock_head:
lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCamelCase__ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json' )
@is_staging_test
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def lowerCamelCase__ ( cls : Any ) -> List[str]:
'''simple docstring'''
lowerCamelCase = TOKEN
HfFolder.save_token(__snake_case )
@classmethod
def lowerCamelCase__ ( cls : Union[str, Any] ) -> Tuple:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-feature-extractor' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-feature-extractor-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-feature-extractor' )
except HTTPError:
pass
def lowerCamelCase__ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained(__snake_case )
feature_extractor.push_to_hub('test-feature-extractor' , use_auth_token=self._token )
lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(__snake_case , getattr(__snake_case , __snake_case ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-feature-extractor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
__snake_case , repo_id='test-feature-extractor' , push_to_hub=__snake_case , use_auth_token=self._token )
lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(__snake_case , getattr(__snake_case , __snake_case ) )
def lowerCamelCase__ ( self : str ) -> Any:
'''simple docstring'''
lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained(__snake_case )
feature_extractor.push_to_hub('valid_org/test-feature-extractor' , use_auth_token=self._token )
lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(__snake_case , getattr(__snake_case , __snake_case ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-feature-extractor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
__snake_case , repo_id='valid_org/test-feature-extractor-org' , push_to_hub=__snake_case , use_auth_token=self._token )
lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(__snake_case , getattr(__snake_case , __snake_case ) )
def lowerCamelCase__ ( self : int ) -> List[str]:
'''simple docstring'''
CustomFeatureExtractor.register_for_auto_class()
lowerCamelCase = CustomFeatureExtractor.from_pretrained(__snake_case )
feature_extractor.push_to_hub('test-dynamic-feature-extractor' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map , {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'} , )
lowerCamelCase = AutoFeatureExtractor.from_pretrained(
F'''{USER}/test-dynamic-feature-extractor''' , trust_remote_code=__snake_case )
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__ , 'CustomFeatureExtractor' )
| 246 | 0 |
def _UpperCamelCase ( lowercase__ ):
if len(_lowerCamelCase ) <= 1:
return lst
__SCREAMING_SNAKE_CASE : Tuple = 1
while i < len(_lowerCamelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__SCREAMING_SNAKE_CASE : List[Any] = lst[i], lst[i - 1]
i -= 1
if i == 0:
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1
return lst
if __name__ == "__main__":
__lowerCAmelCase : Dict =input('Enter numbers separated by a comma:\n').strip()
__lowerCAmelCase : int =[int(item) for item in user_input.split(',')]
print(gnome_sort(unsorted))
| 719 |
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
__lowerCAmelCase : Dict =logging.get_logger(__name__)
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = ['''pixel_values''']
def __init__( self :str , lowerCAmelCase__ :bool = True , lowerCAmelCase__ :Optional[Dict[str, int]] = None , lowerCAmelCase__ :PILImageResampling = PILImageResampling.BILINEAR , lowerCAmelCase__ :bool = True , lowerCAmelCase__ :Dict[str, int] = None , lowerCAmelCase__ :bool = True , lowerCAmelCase__ :Union[int, float] = 1 / 255 , lowerCAmelCase__ :bool = True , lowerCAmelCase__ :Optional[Union[float, List[float]]] = None , lowerCAmelCase__ :Optional[Union[float, List[float]]] = None , **lowerCAmelCase__ :Tuple , ) -> None:
super().__init__(**lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : int = size if size is not None else {'''shortest_edge''': 256}
__SCREAMING_SNAKE_CASE : Optional[int] = get_size_dict(lowerCAmelCase__ , default_to_square=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__SCREAMING_SNAKE_CASE : Union[str, Any] = get_size_dict(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = do_resize
__SCREAMING_SNAKE_CASE : str = size
__SCREAMING_SNAKE_CASE : Any = resample
__SCREAMING_SNAKE_CASE : Union[str, Any] = do_center_crop
__SCREAMING_SNAKE_CASE : Tuple = crop_size
__SCREAMING_SNAKE_CASE : List[str] = do_rescale
__SCREAMING_SNAKE_CASE : List[Any] = rescale_factor
__SCREAMING_SNAKE_CASE : Any = do_normalize
__SCREAMING_SNAKE_CASE : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__SCREAMING_SNAKE_CASE : Optional[int] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __magic_name__( self :Dict , lowerCAmelCase__ :np.ndarray , lowerCAmelCase__ :Dict[str, int] , lowerCAmelCase__ :PILImageResampling = PILImageResampling.BICUBIC , lowerCAmelCase__ :Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase__ :int , ) -> np.ndarray:
__SCREAMING_SNAKE_CASE : Tuple = get_size_dict(lowerCAmelCase__ , default_to_square=lowerCAmelCase__ )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
__SCREAMING_SNAKE_CASE : Optional[int] = get_resize_output_image_size(lowerCAmelCase__ , size=size['''shortest_edge'''] , default_to_square=lowerCAmelCase__ )
return resize(lowerCAmelCase__ , size=lowerCAmelCase__ , resample=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def __magic_name__( self :str , lowerCAmelCase__ :np.ndarray , lowerCAmelCase__ :Dict[str, int] , lowerCAmelCase__ :Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase__ :Any , ) -> np.ndarray:
__SCREAMING_SNAKE_CASE : str = get_size_dict(lowerCAmelCase__ )
return center_crop(lowerCAmelCase__ , size=(size['''height'''], size['''width''']) , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def __magic_name__( self :List[Any] , lowerCAmelCase__ :np.ndarray , lowerCAmelCase__ :float , lowerCAmelCase__ :Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase__ :str ) -> np.ndarray:
return rescale(lowerCAmelCase__ , scale=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def __magic_name__( self :Dict , lowerCAmelCase__ :np.ndarray , lowerCAmelCase__ :Union[float, List[float]] , lowerCAmelCase__ :Union[float, List[float]] , lowerCAmelCase__ :Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase__ :List[Any] , ) -> np.ndarray:
return normalize(lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def __magic_name__( self :Optional[int] , lowerCAmelCase__ :ImageInput , lowerCAmelCase__ :Optional[bool] = None , lowerCAmelCase__ :Dict[str, int] = None , lowerCAmelCase__ :PILImageResampling = None , lowerCAmelCase__ :bool = None , lowerCAmelCase__ :Dict[str, int] = None , lowerCAmelCase__ :Optional[bool] = None , lowerCAmelCase__ :Optional[float] = None , lowerCAmelCase__ :Optional[bool] = None , lowerCAmelCase__ :Optional[Union[float, List[float]]] = None , lowerCAmelCase__ :Optional[Union[float, List[float]]] = None , lowerCAmelCase__ :Optional[Union[str, TensorType]] = None , lowerCAmelCase__ :Union[str, ChannelDimension] = ChannelDimension.FIRST , **lowerCAmelCase__ :Optional[Any] , ) -> Optional[int]:
__SCREAMING_SNAKE_CASE : Dict = do_resize if do_resize is not None else self.do_resize
__SCREAMING_SNAKE_CASE : Tuple = size if size is not None else self.size
__SCREAMING_SNAKE_CASE : Dict = get_size_dict(lowerCAmelCase__ , default_to_square=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = resample if resample is not None else self.resample
__SCREAMING_SNAKE_CASE : Optional[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
__SCREAMING_SNAKE_CASE : Optional[Any] = crop_size if crop_size is not None else self.crop_size
__SCREAMING_SNAKE_CASE : str = get_size_dict(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = do_rescale if do_rescale is not None else self.do_rescale
__SCREAMING_SNAKE_CASE : Optional[int] = rescale_factor if rescale_factor is not None else self.rescale_factor
__SCREAMING_SNAKE_CASE : Tuple = do_normalize if do_normalize is not None else self.do_normalize
__SCREAMING_SNAKE_CASE : int = image_mean if image_mean is not None else self.image_mean
__SCREAMING_SNAKE_CASE : int = image_std if image_std is not None else self.image_std
__SCREAMING_SNAKE_CASE : int = make_list_of_images(lowerCAmelCase__ )
if not valid_images(lowerCAmelCase__ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
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.
__SCREAMING_SNAKE_CASE : Optional[Any] = [to_numpy_array(lowerCAmelCase__ ) for image in images]
if do_resize:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [self.resize(image=lowerCAmelCase__ , size=lowerCAmelCase__ , resample=lowerCAmelCase__ ) for image in images]
if do_center_crop:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [self.center_crop(image=lowerCAmelCase__ , size=lowerCAmelCase__ ) for image in images]
if do_rescale:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [self.rescale(image=lowerCAmelCase__ , scale=lowerCAmelCase__ ) for image in images]
if do_normalize:
__SCREAMING_SNAKE_CASE : int = [self.normalize(image=lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ ) for image in images]
__SCREAMING_SNAKE_CASE : Tuple = [to_channel_dimension_format(lowerCAmelCase__ , lowerCAmelCase__ ) for image in images]
__SCREAMING_SNAKE_CASE : Dict = {'''pixel_values''': images}
return BatchFeature(data=lowerCAmelCase__ , tensor_type=lowerCAmelCase__ )
| 260 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class __lowerCAmelCase ( UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = "pegasus"
__lowerCamelCase : List[Any] = ["past_key_values"]
__lowerCamelCase : Any = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self: List[str] , UpperCamelCase_: int=5_0265 , UpperCamelCase_: str=1024 , UpperCamelCase_: Union[str, Any]=12 , UpperCamelCase_: int=4096 , UpperCamelCase_: Any=16 , UpperCamelCase_: Optional[int]=12 , UpperCamelCase_: Dict=4096 , UpperCamelCase_: Dict=16 , UpperCamelCase_: Any=0.0 , UpperCamelCase_: Optional[Any]=0.0 , UpperCamelCase_: Optional[int]=True , UpperCamelCase_: Optional[Any]=True , UpperCamelCase_: List[str]="gelu" , UpperCamelCase_: Dict=1024 , UpperCamelCase_: List[str]=0.1 , UpperCamelCase_: Dict=0.0 , UpperCamelCase_: int=0.0 , UpperCamelCase_: List[Any]=0.02 , UpperCamelCase_: List[Any]=0 , UpperCamelCase_: List[str]=False , UpperCamelCase_: Optional[Any]=0 , UpperCamelCase_: List[str]=1 , UpperCamelCase_: int=1 , **UpperCamelCase_: str , ):
UpperCamelCase_ =vocab_size
UpperCamelCase_ =max_position_embeddings
UpperCamelCase_ =d_model
UpperCamelCase_ =encoder_ffn_dim
UpperCamelCase_ =encoder_layers
UpperCamelCase_ =encoder_attention_heads
UpperCamelCase_ =decoder_ffn_dim
UpperCamelCase_ =decoder_layers
UpperCamelCase_ =decoder_attention_heads
UpperCamelCase_ =dropout
UpperCamelCase_ =attention_dropout
UpperCamelCase_ =activation_dropout
UpperCamelCase_ =activation_function
UpperCamelCase_ =init_std
UpperCamelCase_ =encoder_layerdrop
UpperCamelCase_ =decoder_layerdrop
UpperCamelCase_ =use_cache
UpperCamelCase_ =encoder_layers
UpperCamelCase_ =scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , is_encoder_decoder=UpperCamelCase_ , decoder_start_token_id=UpperCamelCase_ , forced_eos_token_id=UpperCamelCase_ , **UpperCamelCase_ , )
@property
def UpperCamelCase__ ( self: List[str] ):
return self.encoder_attention_heads
@property
def UpperCamelCase__ ( self: List[str] ):
return self.d_model
| 391 |
"""simple docstring"""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
A_ = (
"https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"
)
A_ = logging.get_logger(__name__) # pylint: disable=invalid-name
def _UpperCamelCase ( ):
UpperCamelCase_ ="https://pypi.org/pypi/diffusers/json"
UpperCamelCase_ =json.loads(request.urlopen(A ).read() )["releases"].keys()
return sorted(A , key=lambda A : version.Version(A ) )
def _UpperCamelCase ( ):
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(A )
os.makedirs(A , exist_ok=A )
UpperCamelCase_ =Path(A ) / "__init__.py"
if not init_path.exists():
init_path.touch()
def _UpperCamelCase ( A ):
init_hf_modules()
UpperCamelCase_ =Path(A ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(A , exist_ok=A )
UpperCamelCase_ =dynamic_module_path / "__init__.py"
if not init_path.exists():
init_path.touch()
def _UpperCamelCase ( A ):
with open(A , "r" , encoding="utf-8" ) as f:
UpperCamelCase_ =f.read()
# Imports of the form `import .xxx`
UpperCamelCase_ =re.findall("^\s*import\s+\.(\S+)\s*$" , A , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import" , A , flags=re.MULTILINE )
# Unique-ify
return list(set(A ) )
def _UpperCamelCase ( A ):
UpperCamelCase_ =False
UpperCamelCase_ =[module_file]
UpperCamelCase_ =[]
# Let's recurse through all relative imports
while not no_change:
UpperCamelCase_ =[]
for f in files_to_check:
new_imports.extend(get_relative_imports(A ) )
UpperCamelCase_ =Path(A ).parent
UpperCamelCase_ =[str(module_path / m ) for m in new_imports]
UpperCamelCase_ =[f for f in new_import_files if f not in all_relative_imports]
UpperCamelCase_ =[f"""{f}.py""" for f in new_import_files]
UpperCamelCase_ =len(A ) == 0
all_relative_imports.extend(A )
return all_relative_imports
def _UpperCamelCase ( A ):
with open(A , "r" , encoding="utf-8" ) as f:
UpperCamelCase_ =f.read()
# Imports of the form `import xxx`
UpperCamelCase_ =re.findall("^\s*import\s+(\S+)\s*$" , A , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall("^\s*from\s+(\S+)\s+import" , A , flags=re.MULTILINE )
# Only keep the top-level module
UpperCamelCase_ =[imp.split("." )[0] for imp in imports if not imp.startswith("." )]
# Unique-ify and test we got them all
UpperCamelCase_ =list(set(A ) )
UpperCamelCase_ =[]
for imp in imports:
try:
importlib.import_module(A )
except ImportError:
missing_packages.append(A )
if len(A ) > 0:
raise ImportError(
"This modeling file requires the following packages that were not found in your environment: "
f"""{", ".join(A )}. Run `pip install {" ".join(A )}`""" )
return get_relative_imports(A )
def _UpperCamelCase ( A , A ):
UpperCamelCase_ =module_path.replace(os.path.sep , "." )
UpperCamelCase_ =importlib.import_module(A )
if class_name is None:
return find_pipeline_class(A )
return getattr(A , A )
def _UpperCamelCase ( A ):
from ..pipelines import DiffusionPipeline
UpperCamelCase_ =dict(inspect.getmembers(A , inspect.isclass ) )
UpperCamelCase_ =None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , A )
and cls.__module__.split("." )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
f"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"""
f""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"""
f""" {loaded_module}.""" )
UpperCamelCase_ =cls
return pipeline_class
def _UpperCamelCase ( A , A , A = None , A = False , A = False , A = None , A = None , A = None , A = False , ):
UpperCamelCase_ =str(A )
UpperCamelCase_ =os.path.join(A , A )
if os.path.isfile(A ):
UpperCamelCase_ =module_file_or_url
UpperCamelCase_ ="local"
elif pretrained_model_name_or_path.count("/" ) == 0:
UpperCamelCase_ =get_diffusers_versions()
# cut ".dev0"
UpperCamelCase_ ="v" + ".".join(__version__.split("." )[:3] )
# retrieve github version that matches
if revision is None:
UpperCamelCase_ =latest_version if latest_version[1:] in available_versions else "main"
logger.info(f"""Defaulting to latest_version: {revision}.""" )
elif revision in available_versions:
UpperCamelCase_ =f"""v{revision}"""
elif revision == "main":
UpperCamelCase_ =revision
else:
raise ValueError(
f"""`custom_revision`: {revision} does not exist. Please make sure to choose one of"""
f""" {", ".join(available_versions + ["main"] )}.""" )
# community pipeline on GitHub
UpperCamelCase_ =COMMUNITY_PIPELINES_URL.format(revision=A , pipeline=A )
try:
UpperCamelCase_ =cached_download(
A , cache_dir=A , force_download=A , proxies=A , resume_download=A , local_files_only=A , use_auth_token=A , )
UpperCamelCase_ ="git"
UpperCamelCase_ =pretrained_model_name_or_path + ".py"
except EnvironmentError:
logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
raise
else:
try:
# Load from URL or cache if already cached
UpperCamelCase_ =hf_hub_download(
A , A , cache_dir=A , force_download=A , proxies=A , resume_download=A , local_files_only=A , use_auth_token=A , )
UpperCamelCase_ =os.path.join("local" , "--".join(pretrained_model_name_or_path.split("/" ) ) )
except EnvironmentError:
logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
raise
# Check we have all the requirements in our environment
UpperCamelCase_ =check_imports(A )
# Now we move the module inside our cached dynamic modules.
UpperCamelCase_ =DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(A )
UpperCamelCase_ =Path(A ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(A , submodule_path / module_file )
for module_needed in modules_needed:
UpperCamelCase_ =f"""{module_needed}.py"""
shutil.copy(os.path.join(A , A ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(A , A ):
UpperCamelCase_ =use_auth_token
elif use_auth_token is True:
UpperCamelCase_ =HfFolder.get_token()
else:
UpperCamelCase_ =None
UpperCamelCase_ =model_info(A , revision=A , token=A ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
UpperCamelCase_ =submodule_path / commit_hash
UpperCamelCase_ =full_submodule + os.path.sep + commit_hash
create_dynamic_module(A )
if not (submodule_path / module_file).exists():
shutil.copy(A , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
A , f"""{module_needed}.py""" , cache_dir=A , force_download=A , resume_download=A , proxies=A , use_auth_token=A , revision=A , local_files_only=A , )
return os.path.join(A , A )
def _UpperCamelCase ( A , A , A = None , A = None , A = False , A = False , A = None , A = None , A = None , A = False , **A , ):
UpperCamelCase_ =get_cached_module_file(
A , A , cache_dir=A , force_download=A , resume_download=A , proxies=A , use_auth_token=A , revision=A , local_files_only=A , )
return get_class_in_module(A , final_module.replace(".py" , "" ) )
| 391 | 1 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def _a ( _snake_case , _snake_case , _snake_case , _snake_case , _snake_case=True , _snake_case="pt" ):
"""simple docstring"""
UpperCAmelCase = {'add_prefix_space': True} if isinstance(_snake_case , _snake_case ) and not line.startswith(""" """ ) else {}
UpperCAmelCase = padding_side
return tokenizer(
[line] , max_length=_snake_case , padding="""max_length""" if pad_to_max_length else None , truncation=_snake_case , return_tensors=_snake_case , add_special_tokens=_snake_case , **_snake_case , )
def _a ( _snake_case , _snake_case , _snake_case=None , ):
"""simple docstring"""
UpperCAmelCase = input_ids.ne(_snake_case ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class lowerCamelCase__ ( snake_case__ ):
def __init__( self ,A ,A ,A ,A ,A="train" ,A=None ,A=None ,A=None ,A="" ,):
super().__init__()
UpperCAmelCase = Path(_A ).joinpath(type_path + """.source""" )
UpperCAmelCase = Path(_A ).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 ):
return len(self.src_lens )
def __getitem__( self ,A ):
UpperCAmelCase = index + 1 # linecache starts at 1
UpperCAmelCase = self.prefix + linecache.getline(str(self.src_file ) ,_A ).rstrip("""\n""" )
UpperCAmelCase = linecache.getline(str(self.tgt_file ) ,_A ).rstrip("""\n""" )
assert source_line, F'''empty source line for index {index}'''
assert tgt_line, F'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,_A ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
UpperCAmelCase = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,_A ) else self.tokenizer
)
UpperCAmelCase = self.tokenizer.generator if isinstance(self.tokenizer ,_A ) else self.tokenizer
UpperCAmelCase = encode_line(_A ,_A ,self.max_source_length ,"""right""" )
UpperCAmelCase = encode_line(_A ,_A ,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 ( A ):
return [len(_A ) for x in Path(_A ).open().readlines()]
def _UpperCamelCase ( self ,A ):
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 ,_A )
else self.tokenizer.pad_token_id
)
UpperCAmelCase = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,_A )
else self.tokenizer.pad_token_id
)
UpperCAmelCase = trim_batch(_A ,_A )
UpperCAmelCase = trim_batch(_A ,_A ,attention_mask=_A )
UpperCAmelCase = {
'input_ids': source_ids,
'attention_mask': source_mask,
'decoder_input_ids': y,
}
return batch
_UpperCamelCase = getLogger(__name__)
def _a ( _snake_case ):
"""simple docstring"""
return list(itertools.chain.from_iterable(_snake_case ) )
def _a ( _snake_case ):
"""simple docstring"""
UpperCAmelCase = get_git_info()
save_json(_snake_case , os.path.join(_snake_case , """git_log.json""" ) )
def _a ( _snake_case , _snake_case , _snake_case=4 , **_snake_case ):
"""simple docstring"""
with open(_snake_case , """w""" ) as f:
json.dump(_snake_case , _snake_case , indent=_snake_case , **_snake_case )
def _a ( _snake_case ):
"""simple docstring"""
with open(_snake_case ) as f:
return json.load(_snake_case )
def _a ( ):
"""simple docstring"""
UpperCAmelCase = git.Repo(search_parent_directories=_snake_case )
UpperCAmelCase = {
'repo_id': str(_snake_case ),
'repo_sha': str(repo.head.object.hexsha ),
'repo_branch': str(repo.active_branch ),
'hostname': str(socket.gethostname() ),
}
return repo_infos
def _a ( _snake_case , _snake_case ):
"""simple docstring"""
return list(map(_snake_case , _snake_case ) )
def _a ( _snake_case , _snake_case ):
"""simple docstring"""
with open(_snake_case , """wb""" ) as f:
return pickle.dump(_snake_case , _snake_case )
def _a ( _snake_case ):
"""simple docstring"""
def remove_articles(_snake_case ):
return re.sub(R"""\b(a|an|the)\b""" , """ """ , _snake_case )
def white_space_fix(_snake_case ):
return " ".join(text.split() )
def remove_punc(_snake_case ):
UpperCAmelCase = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_snake_case ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_snake_case ) ) ) )
def _a ( _snake_case , _snake_case ):
"""simple docstring"""
UpperCAmelCase = normalize_answer(_snake_case ).split()
UpperCAmelCase = normalize_answer(_snake_case ).split()
UpperCAmelCase = Counter(_snake_case ) & Counter(_snake_case )
UpperCAmelCase = sum(common.values() )
if num_same == 0:
return 0
UpperCAmelCase = 1.0 * num_same / len(_snake_case )
UpperCAmelCase = 1.0 * num_same / len(_snake_case )
UpperCAmelCase = (2 * precision * recall) / (precision + recall)
return fa
def _a ( _snake_case , _snake_case ):
"""simple docstring"""
return normalize_answer(_snake_case ) == normalize_answer(_snake_case )
def _a ( _snake_case , _snake_case ):
"""simple docstring"""
assert len(_snake_case ) == len(_snake_case )
UpperCAmelCase = 0
for hypo, pred in zip(_snake_case , _snake_case ):
em += exact_match_score(_snake_case , _snake_case )
if len(_snake_case ) > 0:
em /= len(_snake_case )
return {"em": em}
def _a ( _snake_case ):
"""simple docstring"""
return model_prefix.startswith("""rag""" )
def _a ( _snake_case , _snake_case , _snake_case ):
"""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(_snake_case , _snake_case , _snake_case ):
if not hasattr(_snake_case , _snake_case ) and not hasattr(_snake_case , equivalent_param[p] ):
logger.info("""config doesn\'t have a `{}` attribute""".format(_snake_case ) )
delattr(_snake_case , _snake_case )
continue
UpperCAmelCase = p if hasattr(_snake_case , _snake_case ) else equivalent_param[p]
setattr(_snake_case , _snake_case , getattr(_snake_case , _snake_case ) )
delattr(_snake_case , _snake_case )
return hparams, config
| 710 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_UpperCamelCase = abspath(join(dirname(dirname(__file__)), """src"""))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)
def _a ( _snake_case ):
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_snake_case )
def _a ( _snake_case ):
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_terminal_summary_main
UpperCAmelCase = terminalreporter.config.getoption("""--make-reports""" )
if make_reports:
pytest_terminal_summary_main(_snake_case , id=_snake_case )
| 74 | 0 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def A_ ( ) -> int:
_snake_case : Optional[int] = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 20, '''a ''' * 30, '''b ''' * 7],
}
_snake_case : Tuple = Dataset.from_dict(lowercase_ )
return dataset
class A (__UpperCAmelCase ):
def __a ( self ) -> Optional[Any]:
'''simple docstring'''
_snake_case : int = get_dataset()
_snake_case : Dict = make_duplicate_clusters(lowercase_ , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def __a ( self ) -> Tuple:
'''simple docstring'''
_snake_case : Tuple = get_dataset()
_snake_case , _snake_case : Optional[int] = deduplicate_dataset(lowercase_ )
self.assertEqual(len(lowercase_ ) , 2 )
print(lowercase_ )
self.assertEqual(duplicate_clusters[0][0]['''copies'''] , 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''] , lowercase_ )
| 326 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class A (__UpperCAmelCase ,unittest.TestCase ):
_SCREAMING_SNAKE_CASE = MvpTokenizer
_SCREAMING_SNAKE_CASE = MvpTokenizerFast
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = filter_roberta_detectors
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
_snake_case : Union[str, Any] = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
_snake_case : Tuple = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
_snake_case : List[str] = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
_snake_case : str = {'''unk_token''': '''<unk>'''}
_snake_case : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_snake_case : Optional[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(lowercase_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(lowercase_ ) )
def __a ( self , **lowercase_ ) -> List[Any]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowercase_ )
def __a ( self , **lowercase_ ) -> Dict:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **lowercase_ )
def __a ( self , lowercase_ ) -> Any:
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def __a ( self ) -> int:
'''simple docstring'''
return MvpTokenizer.from_pretrained('''RUCAIBox/mvp''' )
@cached_property
def __a ( self ) -> str:
'''simple docstring'''
return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp''' )
@require_torch
def __a ( self ) -> List[str]:
'''simple docstring'''
_snake_case : int = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
_snake_case : Optional[Any] = [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_snake_case : Optional[int] = tokenizer(lowercase_ , max_length=len(lowercase_ ) , padding=lowercase_ , return_tensors='''pt''' )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
_snake_case : int = batch.input_ids.tolist()[0]
self.assertListEqual(lowercase_ , lowercase_ )
# Test that special tokens are reset
@require_torch
def __a ( self ) -> Optional[int]:
'''simple docstring'''
_snake_case : Dict = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_snake_case : str = tokenizer(lowercase_ , padding=lowercase_ , return_tensors='''pt''' )
# check if input_ids are returned and no labels
self.assertIn('''input_ids''' , lowercase_ )
self.assertIn('''attention_mask''' , lowercase_ )
self.assertNotIn('''labels''' , lowercase_ )
self.assertNotIn('''decoder_attention_mask''' , lowercase_ )
@require_torch
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
_snake_case : Tuple = [
'''Summary of the text.''',
'''Another summary.''',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_snake_case : List[str] = tokenizer(text_target=lowercase_ , max_length=32 , padding='''max_length''' , return_tensors='''pt''' )
self.assertEqual(32 , targets['''input_ids'''].shape[1] )
@require_torch
def __a ( self ) -> Tuple:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_snake_case : Union[str, Any] = tokenizer(
['''I am a small frog''' * 1024, '''I am a small frog'''] , padding=lowercase_ , truncation=lowercase_ , return_tensors='''pt''' )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertEqual(batch.input_ids.shape , (2, 1024) )
@require_torch
def __a ( self ) -> int:
'''simple docstring'''
_snake_case : Dict = ['''A long paragraph for summarization.''']
_snake_case : List[str] = [
'''Summary of the text.''',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_snake_case : Dict = tokenizer(lowercase_ , text_target=lowercase_ , return_tensors='''pt''' )
_snake_case : List[Any] = inputs['''input_ids''']
_snake_case : Dict = inputs['''labels''']
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
def __a ( self ) -> List[Any]:
'''simple docstring'''
pass
def __a ( self ) -> List[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_snake_case : Dict = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
_snake_case : str = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
_snake_case : Optional[Any] = '''A, <mask> AllenNLP sentence.'''
_snake_case : Optional[Any] = tokenizer_r.encode_plus(lowercase_ , add_special_tokens=lowercase_ , return_token_type_ids=lowercase_ )
_snake_case : str = tokenizer_p.encode_plus(lowercase_ , add_special_tokens=lowercase_ , return_token_type_ids=lowercase_ )
# 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'''] ) , )
_snake_case : List[str] = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
_snake_case : 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(
lowercase_ , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
self.assertSequenceEqual(
lowercase_ , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
| 326 | 1 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self, lowerCamelCase__, lowerCamelCase__=None, lowerCamelCase__=None, lowerCamelCase__=None, lowerCamelCase__="resnet50", lowerCamelCase__=3, lowerCamelCase__=32, lowerCamelCase__=3, lowerCamelCase__=True, lowerCamelCase__=True, ):
A : List[Any] = parent
A : str = out_indices if out_indices is not None else [4]
A : List[Any] = stage_names
A : int = out_features
A : Dict = backbone
A : Any = batch_size
A : Dict = image_size
A : List[Any] = num_channels
A : int = use_pretrained_backbone
A : Union[str, Any] = is_training
def _lowerCAmelCase ( self ):
A : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A : Dict = self.get_config()
return config, pixel_values
def _lowerCAmelCase ( self ):
return TimmBackboneConfig(
image_size=self.image_size, num_channels=self.num_channels, out_features=self.out_features, out_indices=self.out_indices, stage_names=self.stage_names, use_pretrained_backbone=self.use_pretrained_backbone, backbone=self.backbone, )
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__ ):
A : Dict = TimmBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
A : int = model(_lowerCAmelCase )
self.parent.assertEqual(
result.feature_map[-1].shape, (self.batch_size, model.channels[-1], 14, 14), )
def _lowerCAmelCase ( self ):
A : int = self.prepare_config_and_inputs()
A , A : List[Any] = config_and_inputs
A : Optional[int] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class SCREAMING_SNAKE_CASE__ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = (TimmBackbone,) if is_torch_available() else ()
__lowerCamelCase : Dict = {'feature-extraction': TimmBackbone} if is_torch_available() else {}
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : List[Any] = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
def _lowerCAmelCase ( self ):
A : List[Any] = TimmBackboneModelTester(self )
A : Dict = ConfigTester(self, config_class=_lowerCAmelCase, has_text_modality=_lowerCAmelCase )
def _lowerCAmelCase ( self ):
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 _lowerCAmelCase ( self ):
A : Optional[int] = """resnet18"""
A : Tuple = """microsoft/resnet-18"""
A : Optional[Any] = AutoBackbone.from_pretrained(_lowerCAmelCase, use_timm_backbone=_lowerCAmelCase )
A : Tuple = AutoBackbone.from_pretrained(_lowerCAmelCase )
self.assertEqual(len(timm_model.out_features ), len(transformers_model.out_features ) )
self.assertEqual(len(timm_model.stage_names ), len(transformers_model.stage_names ) )
self.assertEqual(timm_model.channels, transformers_model.channels )
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices, (-1,) )
self.assertEqual(transformers_model.out_indices, [len(timm_model.stage_names ) - 1] )
A : Optional[Any] = AutoBackbone.from_pretrained(_lowerCAmelCase, use_timm_backbone=_lowerCAmelCase, out_indices=[1, 2, 3] )
A : Optional[Any] = AutoBackbone.from_pretrained(_lowerCAmelCase, out_indices=[1, 2, 3] )
self.assertEqual(timm_model.out_indices, transformers_model.out_indices )
self.assertEqual(len(timm_model.out_features ), len(transformers_model.out_features ) )
self.assertEqual(timm_model.channels, transformers_model.channels )
@unittest.skip("""TimmBackbone doesn't support feed forward chunking""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""TimmBackbone initialization is managed on the timm side""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""model weights aren't tied in TimmBackbone.""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""model weights aren't tied in TimmBackbone.""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration.""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""TimmBackbone doesn't support output_attentions.""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""Safetensors is not supported by timm.""" )
def _lowerCAmelCase ( self ):
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _lowerCAmelCase ( self ):
pass
def _lowerCAmelCase ( self ):
A , A : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A : List[str] = model_class(_lowerCAmelCase )
A : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A : List[str] = [*signature.parameters.keys()]
A : Any = ["""pixel_values"""]
self.assertListEqual(arg_names[:1], _lowerCAmelCase )
def _lowerCAmelCase ( self ):
A , A : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
A : str = True
A : List[Any] = self.has_attentions
# no need to test all models as different heads yield the same functionality
A : List[Any] = self.all_model_classes[0]
A : List[Any] = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
A : Union[str, Any] = self._prepare_for_class(_lowerCAmelCase, _lowerCAmelCase )
A : Tuple = model(**_lowerCAmelCase )
A : Any = outputs[0][-1]
# Encoder-/Decoder-only models
A : Any = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
A : List[str] = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=_lowerCAmelCase )
self.assertIsNotNone(hidden_states.grad )
if self.has_attentions:
self.assertIsNotNone(attentions.grad )
def _lowerCAmelCase ( self ):
A , A : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A : Tuple = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
A : List[Any] = model(**_lowerCAmelCase )
self.assertEqual(len(result.feature_maps ), len(config.out_indices ) )
self.assertEqual(len(model.channels ), len(config.out_indices ) )
# Check output of last stage is taken if out_features=None, out_indices=None
A : List[str] = copy.deepcopy(_lowerCAmelCase )
A : str = None
A : List[str] = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
A : Tuple = model(**_lowerCAmelCase )
self.assertEqual(len(result.feature_maps ), 1 )
self.assertEqual(len(model.channels ), 1 )
# Check backbone can be initialized with fresh weights
A : Optional[int] = copy.deepcopy(_lowerCAmelCase )
A : List[Any] = False
A : List[str] = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
A : Dict = model(**_lowerCAmelCase )
| 709 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
SCREAMING_SNAKE_CASE_:Union[str, Any] = random.Random()
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase=1.0 , _lowerCAmelCase=None , _lowerCAmelCase=None ) -> Optional[Any]:
"""simple docstring"""
if rng is None:
A : str = global_rng
A : Optional[int] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self, lowerCamelCase__, lowerCamelCase__=7, lowerCamelCase__=400, lowerCamelCase__=2000, lowerCamelCase__=24, lowerCamelCase__=24, lowerCamelCase__=0.0, lowerCamelCase__=1_6000, lowerCamelCase__=True, lowerCamelCase__=True, ):
A : Optional[int] = parent
A : List[Any] = batch_size
A : str = min_seq_length
A : str = max_seq_length
A : List[str] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
A : Dict = feature_size
A : Any = num_mel_bins
A : int = padding_value
A : Optional[int] = sampling_rate
A : str = return_attention_mask
A : int = do_normalize
def _lowerCAmelCase ( self ):
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def _lowerCAmelCase ( self, lowerCamelCase__=False, lowerCamelCase__=False ):
def _flatten(lowerCamelCase__ ):
return list(itertools.chain(*lowerCamelCase__ ) )
if equal_length:
A : Optional[Any] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
A : Optional[Any] = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff )
]
if numpify:
A : Tuple = [np.asarray(lowerCamelCase__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[str] = SpeechaTextFeatureExtractor if is_speech_available() else None
def _lowerCAmelCase ( self ):
A : Tuple = SpeechaTextFeatureExtractionTester(self )
def _lowerCAmelCase ( self, lowerCamelCase__ ):
self.assertTrue(np.all(np.mean(lowerCamelCase__, axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(lowerCamelCase__, axis=0 ) - 1 ) < 1e-3 ) )
def _lowerCAmelCase ( self ):
# Tests that all call wrap to encode_plus and batch_encode_plus
A : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
A : List[str] = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
A : List[Any] = [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]
# Test feature size
A : Any = feature_extractor(lowerCamelCase__, padding=lowerCamelCase__, return_tensors="""np""" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
A : List[str] = feature_extractor(speech_inputs[0], return_tensors="""np""" ).input_features
A : Tuple = feature_extractor(np_speech_inputs[0], return_tensors="""np""" ).input_features
self.assertTrue(np.allclose(lowerCamelCase__, lowerCamelCase__, atol=1e-3 ) )
# Test batched
A : List[Any] = feature_extractor(lowerCamelCase__, return_tensors="""np""" ).input_features
A : int = feature_extractor(lowerCamelCase__, return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCamelCase__, lowerCamelCase__ ):
self.assertTrue(np.allclose(lowerCamelCase__, lowerCamelCase__, atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
A : Tuple = [floats_list((1, x) )[0] for x in (800, 800, 800)]
A : Optional[Any] = np.asarray(lowerCamelCase__ )
A : List[Any] = feature_extractor(lowerCamelCase__, return_tensors="""np""" ).input_features
A : str = feature_extractor(lowerCamelCase__, return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCamelCase__, lowerCamelCase__ ):
self.assertTrue(np.allclose(lowerCamelCase__, lowerCamelCase__, atol=1e-3 ) )
def _lowerCAmelCase ( self ):
A : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A : Dict = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
A : Any = ["""longest""", """max_length""", """do_not_pad"""]
A : int = [None, 16, None]
for max_length, padding in zip(lowerCamelCase__, lowerCamelCase__ ):
A : Tuple = feature_extractor(
lowerCamelCase__, padding=lowerCamelCase__, max_length=lowerCamelCase__, return_attention_mask=lowerCamelCase__ )
A : Tuple = inputs.input_features
A : Union[str, Any] = inputs.attention_mask
A : Optional[Any] = [np.sum(lowerCamelCase__ ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def _lowerCAmelCase ( self ):
A : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A : str = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
A : List[str] = ["""longest""", """max_length""", """do_not_pad"""]
A : Tuple = [None, 16, None]
for max_length, padding in zip(lowerCamelCase__, lowerCamelCase__ ):
A : Union[str, Any] = feature_extractor(
lowerCamelCase__, max_length=lowerCamelCase__, padding=lowerCamelCase__, return_tensors="""np""", return_attention_mask=lowerCamelCase__ )
A : Optional[int] = inputs.input_features
A : List[Any] = inputs.attention_mask
A : str = [np.sum(lowerCamelCase__ ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def _lowerCAmelCase ( self ):
A : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A : str = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
A : Optional[Any] = feature_extractor(
lowerCamelCase__, padding="""max_length""", max_length=4, truncation=lowerCamelCase__, return_tensors="""np""", return_attention_mask=lowerCamelCase__, )
A : Union[str, Any] = inputs.input_features
A : Optional[Any] = inputs.attention_mask
A : Dict = np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def _lowerCAmelCase ( self ):
A : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A : Dict = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
A : List[Any] = feature_extractor(
lowerCamelCase__, padding="""longest""", max_length=4, truncation=lowerCamelCase__, return_tensors="""np""", return_attention_mask=lowerCamelCase__, )
A : List[Any] = inputs.input_features
A : Optional[Any] = inputs.attention_mask
A : List[str] = np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 4, 24) )
A : int = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
A : List[str] = feature_extractor(
lowerCamelCase__, padding="""longest""", max_length=16, truncation=lowerCamelCase__, return_tensors="""np""", return_attention_mask=lowerCamelCase__, )
A : List[Any] = inputs.input_features
A : List[str] = inputs.attention_mask
A : List[str] = np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 6, 24) )
def _lowerCAmelCase ( self ):
import torch
A : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A : Dict = np.random.rand(100, 32 ).astype(np.floataa )
A : str = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
A : Optional[Any] = feature_extractor.pad([{"""input_features""": inputs}], return_tensors="""np""" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
A : str = feature_extractor.pad([{"""input_features""": inputs}], return_tensors="""pt""" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def _lowerCAmelCase ( self, lowerCamelCase__ ):
from datasets import load_dataset
A : List[str] = load_dataset("""hf-internal-testing/librispeech_asr_dummy""", """clean""", split="""validation""" )
# automatic decoding with librispeech
A : int = ds.sort("""id""" ).select(range(lowerCamelCase__ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def _lowerCAmelCase ( self ):
# fmt: off
A : Optional[Any] = np.array([
-1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
-1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
-1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
] )
# fmt: on
A : Optional[Any] = self._load_datasamples(1 )
A : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A : Optional[Any] = feature_extractor(lowerCamelCase__, return_tensors="""pt""" ).input_features
self.assertEquals(input_features.shape, (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30], lowerCamelCase__, atol=1e-4 ) )
| 520 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@slow
def _A ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE : Any = AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small" , return_dict=UpperCAmelCase_ ).to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = AutoTokenizer.from_pretrained("google/mt5-small" )
SCREAMING_SNAKE_CASE : str = tokenizer("Hello there" , return_tensors="pt" ).input_ids
SCREAMING_SNAKE_CASE : Any = tokenizer("Hi I am" , return_tensors="pt" ).input_ids
SCREAMING_SNAKE_CASE : str = model(input_ids.to(UpperCAmelCase_ ) , labels=labels.to(UpperCAmelCase_ ) ).loss
SCREAMING_SNAKE_CASE : List[str] = -(labels.shape[-1] * loss.item())
SCREAMING_SNAKE_CASE : Any = -84.9_127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 62 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase_ : int = StableDiffusionControlNetImgaImgPipeline
UpperCamelCase_ : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
UpperCamelCase_ : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCamelCase_ : Tuple = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''} )
UpperCamelCase_ : Any = IMAGE_TO_IMAGE_IMAGE_PARAMS
def _A ( self : List[str] ):
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=UpperCAmelCase_ , set_alpha_to_one=UpperCAmelCase_ , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPTextModel(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
SCREAMING_SNAKE_CASE : str = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def _A ( self : str , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int=0 ):
if str(UpperCAmelCase_ ).startswith("mps" ):
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(UpperCAmelCase_ )
else:
SCREAMING_SNAKE_CASE : Optional[Any] = torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = 2
SCREAMING_SNAKE_CASE : Union[str, Any] = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=UpperCAmelCase_ , device=torch.device(UpperCAmelCase_ ) , )
SCREAMING_SNAKE_CASE : Tuple = floats_tensor(control_image.shape , rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE : str = Image.fromarray(np.uinta(UpperCAmelCase_ ) ).convert("RGB" ).resize((64, 64) )
SCREAMING_SNAKE_CASE : List[str] = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": image,
"control_image": control_image,
}
return inputs
def _A ( self : int ):
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _A ( self : str ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def _A ( self : Union[str, Any] ):
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = StableDiffusionControlNetImgaImgPipeline
UpperCamelCase_ : str = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
UpperCamelCase_ : Optional[int] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCamelCase_ : Dict = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def _A ( self : Optional[Any] ):
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
torch.manual_seed(0 )
def init_weights(UpperCAmelCase_ : List[Any] ):
if isinstance(UpperCAmelCase_ , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
SCREAMING_SNAKE_CASE : List[str] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(UpperCAmelCase_ )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(UpperCAmelCase_ )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=UpperCAmelCase_ , set_alpha_to_one=UpperCAmelCase_ , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Dict = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
SCREAMING_SNAKE_CASE : Any = CLIPTextModel(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
SCREAMING_SNAKE_CASE : Tuple = MultiControlNetModel([controlneta, controlneta] )
SCREAMING_SNAKE_CASE : Optional[int] = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def _A ( self : List[str] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Any=0 ):
if str(UpperCAmelCase_ ).startswith("mps" ):
SCREAMING_SNAKE_CASE : Dict = torch.manual_seed(UpperCAmelCase_ )
else:
SCREAMING_SNAKE_CASE : str = torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = 2
SCREAMING_SNAKE_CASE : Tuple = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=UpperCAmelCase_ , device=torch.device(UpperCAmelCase_ ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=UpperCAmelCase_ , device=torch.device(UpperCAmelCase_ ) , ),
]
SCREAMING_SNAKE_CASE : Optional[int] = floats_tensor(control_image[0].shape , rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = Image.fromarray(np.uinta(UpperCAmelCase_ ) ).convert("RGB" ).resize((64, 64) )
SCREAMING_SNAKE_CASE : Optional[Any] = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": image,
"control_image": control_image,
}
return inputs
def _A ( self : Tuple ):
SCREAMING_SNAKE_CASE : Any = self.get_dummy_components()
SCREAMING_SNAKE_CASE : str = self.pipeline_class(**UpperCAmelCase_ )
pipe.to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = 10.0
SCREAMING_SNAKE_CASE : Any = 4
SCREAMING_SNAKE_CASE : Optional[int] = self.get_dummy_inputs(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = steps
SCREAMING_SNAKE_CASE : int = scale
SCREAMING_SNAKE_CASE : Optional[int] = pipe(**UpperCAmelCase_ )[0]
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_dummy_inputs(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = steps
SCREAMING_SNAKE_CASE : Any = scale
SCREAMING_SNAKE_CASE : List[str] = pipe(**UpperCAmelCase_ , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_inputs(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = steps
SCREAMING_SNAKE_CASE : int = scale
SCREAMING_SNAKE_CASE : List[Any] = pipe(**UpperCAmelCase_ , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_dummy_inputs(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = steps
SCREAMING_SNAKE_CASE : Dict = scale
SCREAMING_SNAKE_CASE : Dict = pipe(**UpperCAmelCase_ , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
def _A ( self : Union[str, Any] ):
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _A ( self : str ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def _A ( self : List[Any] ):
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
def _A ( self : Any ):
SCREAMING_SNAKE_CASE : Dict = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Dict = self.pipeline_class(**UpperCAmelCase_ )
pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(UpperCAmelCase_ )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _A ( self : Optional[Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _A ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE : str = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny" )
SCREAMING_SNAKE_CASE : Union[str, Any] = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , safety_checker=UpperCAmelCase_ , controlnet=UpperCAmelCase_ )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = torch.Generator(device="cpu" ).manual_seed(0 )
SCREAMING_SNAKE_CASE : str = "evil space-punk bird"
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" ).resize((512, 512) )
SCREAMING_SNAKE_CASE : Optional[int] = load_image(
"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png" ).resize((512, 512) )
SCREAMING_SNAKE_CASE : str = pipe(
UpperCAmelCase_ , UpperCAmelCase_ , control_image=UpperCAmelCase_ , generator=UpperCAmelCase_ , output_type="np" , num_inference_steps=50 , strength=0.6 , )
SCREAMING_SNAKE_CASE : int = output.images[0]
assert image.shape == (512, 512, 3)
SCREAMING_SNAKE_CASE : Dict = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy" )
assert np.abs(expected_image - image ).max() < 9E-2
| 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A_ = {
"configuration_layoutlmv3": [
"LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP",
"LayoutLMv3Config",
"LayoutLMv3OnnxConfig",
],
"processing_layoutlmv3": ["LayoutLMv3Processor"],
"tokenization_layoutlmv3": ["LayoutLMv3Tokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["LayoutLMv3TokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
"LayoutLMv3ForQuestionAnswering",
"LayoutLMv3ForSequenceClassification",
"LayoutLMv3ForTokenClassification",
"LayoutLMv3Model",
"LayoutLMv3PreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLayoutLMv3ForQuestionAnswering",
"TFLayoutLMv3ForSequenceClassification",
"TFLayoutLMv3ForTokenClassification",
"TFLayoutLMv3Model",
"TFLayoutLMv3PreTrainedModel",
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["LayoutLMv3FeatureExtractor"]
A_ = ["LayoutLMv3ImageProcessor"]
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 360 |
def __UpperCamelCase ( a, a, a=False) ->Dict:
if isinstance(a, a) and isinstance(a, a):
lowerCamelCase__ = len(set_a.intersection(a))
if alternative_union:
lowerCamelCase__ = len(a) + len(a)
else:
lowerCamelCase__ = len(set_a.union(a))
return intersection / union
if isinstance(a, (list, tuple)) and isinstance(a, (list, tuple)):
lowerCamelCase__ = [element for element in set_a if element in set_b]
if alternative_union:
lowerCamelCase__ = len(a) + len(a)
return len(a) / union
else:
lowerCamelCase__ = set_a + [element for element in set_b if element not in set_a]
return len(a) / len(a)
return len(a) / len(a)
return None
if __name__ == "__main__":
A_ = {"a", "b", "c", "d", "e"}
A_ = {"c", "d", "e", "f", "h", "i"}
print(jaccard_similarity(set_a, set_b))
| 360 | 1 |
from __future__ import annotations
def a_ ( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float ):
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance < 0:
raise ValueError('Resistance cannot be negative' )
if voltage == 0:
return {"voltage": float(current * resistance )}
elif current == 0:
return {"current": voltage / resistance}
elif resistance == 0:
return {"resistance": voltage / current}
else:
raise ValueError('Exactly one argument must be 0' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 464 |
from math import isqrt
def a_ ( SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
_lowerCamelCase : Optional[int] =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
_lowerCamelCase : str =False
return [i for i in range(2 , SCREAMING_SNAKE_CASE__ ) if is_prime[i]]
def a_ ( SCREAMING_SNAKE_CASE__ : int = 10**8 ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] =calculate_prime_numbers(max_number // 2 )
_lowerCamelCase : Dict =0
_lowerCamelCase : int =0
_lowerCamelCase : Optional[Any] =len(SCREAMING_SNAKE_CASE__ ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F"""{solution() = }""")
| 464 | 1 |
'''simple docstring'''
from manim import *
class __UpperCAmelCase ( __a ):
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
lowerCAmelCase_ = [mem.copy() for i in range(6 )]
lowerCAmelCase_ = [mem.copy() for i in range(6 )]
lowerCAmelCase_ = VGroup(*_lowerCamelCase ).arrange(_lowerCamelCase , buff=0 )
lowerCAmelCase_ = VGroup(*_lowerCamelCase ).arrange(_lowerCamelCase , buff=0 )
lowerCAmelCase_ = VGroup(_lowerCamelCase , _lowerCamelCase ).arrange(_lowerCamelCase , buff=0 )
lowerCAmelCase_ = Text('''CPU''' , font_size=24 )
lowerCAmelCase_ = Group(_lowerCamelCase , _lowerCamelCase ).arrange(_lowerCamelCase , buff=0.5 , aligned_edge=_lowerCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_lowerCamelCase )
lowerCAmelCase_ = [mem.copy() for i in range(4 )]
lowerCAmelCase_ = VGroup(*_lowerCamelCase ).arrange(_lowerCamelCase , buff=0 )
lowerCAmelCase_ = Text('''GPU''' , font_size=24 )
lowerCAmelCase_ = Group(_lowerCamelCase , _lowerCamelCase ).arrange(_lowerCamelCase , buff=0.5 , aligned_edge=_lowerCamelCase )
gpu.move_to([-1, -1, 0] )
self.add(_lowerCamelCase )
lowerCAmelCase_ = [mem.copy() for i in range(6 )]
lowerCAmelCase_ = VGroup(*_lowerCamelCase ).arrange(_lowerCamelCase , buff=0 )
lowerCAmelCase_ = Text('''Model''' , font_size=24 )
lowerCAmelCase_ = Group(_lowerCamelCase , _lowerCamelCase ).arrange(_lowerCamelCase , buff=0.5 , aligned_edge=_lowerCamelCase )
model.move_to([3, -1.0, 0] )
self.add(_lowerCamelCase )
lowerCAmelCase_ = []
for i, rect in enumerate(_lowerCamelCase ):
rect.set_stroke(_lowerCamelCase )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
lowerCAmelCase_ = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(_lowerCamelCase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=_lowerCamelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=_lowerCamelCase , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=_lowerCamelCase , buff=0.0 )
self.add(_lowerCamelCase )
cpu_targs.append(_lowerCamelCase )
lowerCAmelCase_ = [mem.copy() for i in range(6 )]
lowerCAmelCase_ = VGroup(*_lowerCamelCase ).arrange(_lowerCamelCase , buff=0 )
lowerCAmelCase_ = Text('''Loaded Checkpoint''' , font_size=24 )
lowerCAmelCase_ = Group(_lowerCamelCase , _lowerCamelCase ).arrange(_lowerCamelCase , aligned_edge=_lowerCamelCase , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
lowerCAmelCase_ = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowerCAmelCase_ = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(_lowerCamelCase , _lowerCamelCase )
lowerCAmelCase_ = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(_lowerCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
lowerCAmelCase_ = MarkupText(
F'''Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_lowerCamelCase ) , Write(_lowerCamelCase ) )
self.play(Write(_lowerCamelCase , run_time=1 ) , Create(_lowerCamelCase , run_time=1 ) )
lowerCAmelCase_ = []
lowerCAmelCase_ = []
for i, rect in enumerate(_lowerCamelCase ):
lowerCAmelCase_ = fill.copy().set_fill(_lowerCamelCase , opacity=0.7 )
target.move_to(_lowerCamelCase )
first_animations.append(GrowFromCenter(_lowerCamelCase , run_time=1 ) )
lowerCAmelCase_ = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(_lowerCamelCase , run_time=1.5 ) )
self.play(*_lowerCamelCase )
self.play(*_lowerCamelCase )
self.wait()
| 606 | '''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
A_ : Optional[int] =logging.get_logger(__name__)
@add_end_docstrings(__a )
class __UpperCAmelCase ( __a ):
def __init__( self , *_lowerCamelCase , **_lowerCamelCase ):
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
requires_backends(self , '''decord''' )
self.check_model_type(_lowerCamelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=None ):
lowerCAmelCase_ = {}
if frame_sampling_rate is not None:
lowerCAmelCase_ = frame_sampling_rate
if num_frames is not None:
lowerCAmelCase_ = num_frames
lowerCAmelCase_ = {}
if top_k is not None:
lowerCAmelCase_ = top_k
return preprocess_params, {}, postprocess_params
def __call__( self , _lowerCamelCase , **_lowerCamelCase ):
return super().__call__(_lowerCamelCase , **_lowerCamelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase=None , _lowerCamelCase=1 ):
if num_frames is None:
lowerCAmelCase_ = self.model.config.num_frames
if video.startswith('''http://''' ) or video.startswith('''https://''' ):
lowerCAmelCase_ = BytesIO(requests.get(_lowerCamelCase ).content )
lowerCAmelCase_ = VideoReader(_lowerCamelCase )
videoreader.seek(0 )
lowerCAmelCase_ = 0
lowerCAmelCase_ = num_frames * frame_sampling_rate - 1
lowerCAmelCase_ = np.linspace(_lowerCamelCase , _lowerCamelCase , num=_lowerCamelCase , dtype=np.intaa )
lowerCAmelCase_ = videoreader.get_batch(_lowerCamelCase ).asnumpy()
lowerCAmelCase_ = list(_lowerCamelCase )
lowerCAmelCase_ = self.image_processor(_lowerCamelCase , return_tensors=self.framework )
return model_inputs
def UpperCAmelCase_ ( self , _lowerCamelCase ):
lowerCAmelCase_ = self.model(**_lowerCamelCase )
return model_outputs
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase=5 ):
if top_k > self.model.config.num_labels:
lowerCAmelCase_ = self.model.config.num_labels
if self.framework == "pt":
lowerCAmelCase_ = model_outputs.logits.softmax(-1 )[0]
lowerCAmelCase_ ,lowerCAmelCase_ = probs.topk(_lowerCamelCase )
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
lowerCAmelCase_ = scores.tolist()
lowerCAmelCase_ = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_lowerCamelCase , _lowerCamelCase )]
| 606 | 1 |
'''simple docstring'''
class lowercase__ :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
UpperCamelCase = {}
def UpperCAmelCase ( self ):
'''simple docstring'''
print(self.vertex )
for i in self.vertex:
print(lowerCamelCase__ , ''' -> ''' , ''' -> '''.join([str(lowerCamelCase__ ) for j in self.vertex[i]] ) )
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
if from_vertex in self.vertex:
self.vertex[from_vertex].append(lowerCamelCase__ )
else:
# else make a new vertex
UpperCamelCase = [to_vertex]
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(lowerCamelCase__ , lowerCamelCase__ )
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
UpperCamelCase = True
print(lowerCamelCase__ , end=''' ''' )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(lowerCamelCase__ , lowerCamelCase__ )
if __name__ == "__main__":
snake_case_ : Optional[int] = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print('DFS:')
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 212 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=1_3 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=9_9 , lowerCamelCase__=3_2 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=3_7 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=5_1_2 , lowerCamelCase__=1_6 , lowerCamelCase__=2 , lowerCamelCase__=0.02 , lowerCamelCase__=4 , ):
'''simple docstring'''
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_attention_mask
UpperCamelCase = use_token_type_ids
UpperCamelCase = use_labels
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
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 = type_vocab_size
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = num_choices
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_attention_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase = None
if self.use_token_type_ids:
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase = RobertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.prepare_config_and_inputs()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = config_and_inputs
UpperCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.prepare_config_and_inputs()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = config_and_inputs
UpperCamelCase = True
UpperCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class lowercase__ ( snake_case_, unittest.TestCase ):
'''simple docstring'''
_snake_case = True
_snake_case = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = FlaxRobertaModelTester(self )
@slow
def UpperCAmelCase ( self ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCamelCase = model_class_name.from_pretrained('''roberta-base''' , from_pt=lowerCamelCase__ )
UpperCamelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
| 212 | 1 |
'''simple docstring'''
from collections import defaultdict
def UpperCamelCase__ ( _lowercase : str , _lowercase : str ) -> bool:
__UpperCAmelCase: Dict = first_str.lower().strip()
__UpperCAmelCase: List[Any] = second_str.lower().strip()
# Remove whitespace
__UpperCAmelCase: Optional[int] = first_str.replace(""" """ , """""" )
__UpperCAmelCase: Optional[int] = second_str.replace(""" """ , """""" )
# Strings of different lengths are not anagrams
if len(_A ) != len(_A ):
return False
# Default values for count should be 0
__UpperCAmelCase: Tuple = defaultdict(_A )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(_A ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
SCREAMING_SNAKE_CASE_ = input('Enter the first string ').strip()
SCREAMING_SNAKE_CASE_ = input('Enter the second string ').strip()
SCREAMING_SNAKE_CASE_ = check_anagrams(input_a, input_b)
print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""") | 709 | '''simple docstring'''
def UpperCamelCase__ ( _lowercase : list ) -> list:
if len(_lowercase ) <= 1:
return lst
__UpperCAmelCase: List[str] = 1
while i < len(_lowercase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__UpperCAmelCase, __UpperCAmelCase: Tuple = lst[i], lst[i - 1]
i -= 1
if i == 0:
__UpperCAmelCase: List[str] = 1
return lst
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE_ = [int(item) for item in user_input.split(',')]
print(gnome_sort(unsorted)) | 466 | 0 |
"""simple docstring"""
import math
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase = 1 / 12_345 ) -> int:
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 3
while True:
SCREAMING_SNAKE_CASE__ = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = int(__UpperCAmelCase )
total_partitions += 1
if check_partition_perfect(__UpperCAmelCase ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__UpperCAmelCase )
integer += 1
if __name__ == "__main__":
print(F'{solution() = }')
| 159 | """simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowerCamelCase (_SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Optional[Any] , _snake_case : str ) -> List[str]:
with open(_snake_case , encoding="utf-8" ) as input_file:
SCREAMING_SNAKE_CASE__ = re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" )
SCREAMING_SNAKE_CASE__ = input_file.read()
SCREAMING_SNAKE_CASE__ = regexp.search(_snake_case )
return match
def lowerCAmelCase_ ( self : str , _snake_case : str ) -> List[str]:
with open(_snake_case , encoding="utf-8" ) as input_file:
SCREAMING_SNAKE_CASE__ = re.compile(r"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL )
SCREAMING_SNAKE_CASE__ = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
SCREAMING_SNAKE_CASE__ = regexp.finditer(_snake_case )
SCREAMING_SNAKE_CASE__ = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def lowerCAmelCase_ ( self : Tuple ) -> str:
SCREAMING_SNAKE_CASE__ = Path("./datasets" )
SCREAMING_SNAKE_CASE__ = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(_snake_case ) ):
raise AssertionError(F"""open(...) must use utf-8 encoding in {dataset}""" )
def lowerCAmelCase_ ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ = Path("./datasets" )
SCREAMING_SNAKE_CASE__ = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_print_statements(str(_snake_case ) ):
raise AssertionError(F"""print statement found in {dataset}. Use datasets.logger/logging instead.""" )
| 159 | 1 |
'''simple docstring'''
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 __UpperCamelCase( _A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = 3_84
if "tiny" in model_name:
UpperCAmelCase__ : List[Any] = [3, 3, 9, 3]
UpperCAmelCase__ : List[Any] = [96, 1_92, 3_84, 7_68]
if "small" in model_name:
UpperCAmelCase__ : Union[str, Any] = [3, 3, 27, 3]
UpperCAmelCase__ : Any = [96, 1_92, 3_84, 7_68]
if "base" in model_name:
UpperCAmelCase__ : List[Any] = [3, 3, 27, 3]
UpperCAmelCase__ : Dict = [1_28, 2_56, 5_12, 10_24]
UpperCAmelCase__ : Optional[int] = 5_12
if "large" in model_name:
UpperCAmelCase__ : str = [3, 3, 27, 3]
UpperCAmelCase__ : Tuple = [1_92, 3_84, 7_68, 15_36]
UpperCAmelCase__ : int = 7_68
if "xlarge" in model_name:
UpperCAmelCase__ : int = [3, 3, 27, 3]
UpperCAmelCase__ : Union[str, Any] = [2_56, 5_12, 10_24, 20_48]
UpperCAmelCase__ : Any = 10_24
# set label information
UpperCAmelCase__ : List[Any] = 1_50
UpperCAmelCase__ : Union[str, Any] = '''huggingface/label-files'''
UpperCAmelCase__ : Optional[Any] = '''ade20k-id2label.json'''
UpperCAmelCase__ : Union[str, Any] = json.load(open(hf_hub_download(_A , _A , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase__ : List[Any] = {int(_A ): v for k, v in idalabel.items()}
UpperCAmelCase__ : Tuple = {v: k for k, v in idalabel.items()}
UpperCAmelCase__ : int = ConvNextConfig(
depths=_A , hidden_sizes=_A , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
UpperCAmelCase__ : Optional[int] = UperNetConfig(
backbone_config=_A , auxiliary_in_channels=_A , num_labels=_A , idalabel=_A , labelaid=_A , )
return config
def __UpperCamelCase( _A : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Any = []
# 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 __UpperCamelCase( _A : str , _A : Any , _A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = dct.pop(_A )
UpperCAmelCase__ : Dict = val
def __UpperCamelCase( _A : List[str] , _A : Dict , _A : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = {
'''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__ : Optional[int] = model_name_to_url[model_name]
UpperCAmelCase__ : Any = torch.hub.load_state_dict_from_url(_A , map_location='''cpu''' )['''state_dict''']
UpperCAmelCase__ : Union[str, Any] = get_upernet_config(_A )
UpperCAmelCase__ : str = UperNetForSemanticSegmentation(_A )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
UpperCAmelCase__ : Optional[Any] = state_dict.pop(_A )
if "bn" in key:
UpperCAmelCase__ : int = key.replace('''bn''' , '''batch_norm''' )
UpperCAmelCase__ : Union[str, Any] = val
# rename keys
UpperCAmelCase__ : int = create_rename_keys(_A )
for src, dest in rename_keys:
rename_key(_A , _A , _A )
model.load_state_dict(_A )
# verify on image
UpperCAmelCase__ : str = '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
UpperCAmelCase__ : Union[str, Any] = Image.open(requests.get(_A , stream=_A ).raw ).convert('''RGB''' )
UpperCAmelCase__ : Union[str, Any] = SegformerImageProcessor()
UpperCAmelCase__ : Tuple = processor(_A , return_tensors='''pt''' ).pixel_values
with torch.no_grad():
UpperCAmelCase__ : Dict = model(_A )
if model_name == "upernet-convnext-tiny":
UpperCAmelCase__ : Dict = torch.tensor(
[[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]] )
elif model_name == "upernet-convnext-small":
UpperCAmelCase__ : Any = torch.tensor(
[[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]] )
elif model_name == "upernet-convnext-base":
UpperCAmelCase__ : Dict = torch.tensor(
[[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]] )
elif model_name == "upernet-convnext-large":
UpperCAmelCase__ : Optional[Any] = torch.tensor(
[[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]] )
elif model_name == "upernet-convnext-xlarge":
UpperCAmelCase__ : Tuple = torch.tensor(
[[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]] )
print('''Logits:''' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , _A , 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(_A )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(_A )
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__":
UpperCamelCase__ : Tuple = 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.'
)
UpperCamelCase__ : List[Any] = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 496 | '''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class _lowercase :
'''simple docstring'''
def __init__( self ,lowerCamelCase_ ,lowerCamelCase_=13 ,lowerCamelCase_=32 ,lowerCamelCase_=2 ,lowerCamelCase_=3 ,lowerCamelCase_=16 ,lowerCamelCase_=[1, 2, 1] ,lowerCamelCase_=[2, 2, 4] ,lowerCamelCase_=2 ,lowerCamelCase_=2.0 ,lowerCamelCase_=True ,lowerCamelCase_=0.0 ,lowerCamelCase_=0.0 ,lowerCamelCase_=0.1 ,lowerCamelCase_="gelu" ,lowerCamelCase_=False ,lowerCamelCase_=True ,lowerCamelCase_=0.02 ,lowerCamelCase_=1e-5 ,lowerCamelCase_=True ,lowerCamelCase_=None ,lowerCamelCase_=True ,lowerCamelCase_=10 ,lowerCamelCase_=8 ,lowerCamelCase_=["stage1", "stage2", "stage3"] ,lowerCamelCase_=[1, 2, 3] ,) -> str:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = parent
UpperCAmelCase__ : Optional[Any] = batch_size
UpperCAmelCase__ : Tuple = image_size
UpperCAmelCase__ : Tuple = patch_size
UpperCAmelCase__ : int = num_channels
UpperCAmelCase__ : str = embed_dim
UpperCAmelCase__ : List[str] = depths
UpperCAmelCase__ : List[Any] = num_heads
UpperCAmelCase__ : Optional[Any] = window_size
UpperCAmelCase__ : Tuple = mlp_ratio
UpperCAmelCase__ : List[str] = qkv_bias
UpperCAmelCase__ : List[str] = hidden_dropout_prob
UpperCAmelCase__ : str = attention_probs_dropout_prob
UpperCAmelCase__ : Any = drop_path_rate
UpperCAmelCase__ : List[Any] = hidden_act
UpperCAmelCase__ : Optional[int] = use_absolute_embeddings
UpperCAmelCase__ : int = patch_norm
UpperCAmelCase__ : Dict = layer_norm_eps
UpperCAmelCase__ : Tuple = initializer_range
UpperCAmelCase__ : Optional[int] = is_training
UpperCAmelCase__ : List[Any] = scope
UpperCAmelCase__ : Any = use_labels
UpperCAmelCase__ : Any = type_sequence_label_size
UpperCAmelCase__ : Any = encoder_stride
UpperCAmelCase__ : Optional[int] = out_features
UpperCAmelCase__ : Optional[Any] = out_indices
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase__ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Any = None
if self.use_labels:
UpperCAmelCase__ : List[str] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
UpperCAmelCase__ : Tuple = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
return MaskFormerSwinConfig(
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 ,out_features=self.out_features ,out_indices=self.out_indices ,)
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = MaskFormerSwinModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
UpperCAmelCase__ : Union[str, Any] = model(lowerCamelCase_ )
UpperCAmelCase__ : Any = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
UpperCAmelCase__ : Optional[int] = 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 lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = MaskFormerSwinBackbone(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
UpperCAmelCase__ : Optional[Any] = model(lowerCamelCase_ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) ,len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,[13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) ,len(config.out_features ) )
self.parent.assertListEqual(model.channels ,[16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(lowerCamelCase_ ):
UpperCAmelCase__ : List[Any] = ['''stem''']
UpperCAmelCase__ : int = MaskFormerSwinBackbone(config=lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = config_and_inputs
UpperCAmelCase__ : str = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( lowerCAmelCase ,lowerCAmelCase ,unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : str = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
UpperCAmelCase_ : Union[str, Any] = {'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
UpperCAmelCase_ : Optional[Any] = False
UpperCAmelCase_ : Any = False
UpperCAmelCase_ : str = False
UpperCAmelCase_ : Union[str, Any] = False
UpperCAmelCase_ : List[str] = False
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase__ : Tuple = MaskFormerSwinModelTester(self )
UpperCAmelCase__ : List[str] = ConfigTester(self ,config_class=lowerCamelCase_ ,embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
'''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
''' `nn.DataParallel`'''
) )
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
pass
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowerCamelCase_ )
@unittest.skip('''Swin does not use inputs_embeds''' )
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
pass
@unittest.skip('''Swin does not support feedforward chunking''' )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
pass
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Optional[int] = model_class(lowerCamelCase_ )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
UpperCAmelCase__ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase_ ,nn.Linear ) )
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[Any] = model_class(lowerCamelCase_ )
UpperCAmelCase__ : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Dict = [*signature.parameters.keys()]
UpperCAmelCase__ : Optional[int] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,lowerCamelCase_ )
@unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions''' )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone''' )
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
pass
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ : str = model_class(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
with torch.no_grad():
UpperCAmelCase__ : List[Any] = model(**self._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_ ) )
UpperCAmelCase__ : Optional[int] = outputs.hidden_states
UpperCAmelCase__ : Union[str, Any] = getattr(
self.model_tester ,'''expected_num_hidden_layers''' ,len(self.model_tester.depths ) + 1 )
self.assertEqual(len(lowerCamelCase_ ) ,lowerCamelCase_ )
# Swin has a different seq_length
UpperCAmelCase__ : Tuple = (
config.patch_size
if isinstance(config.patch_size ,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
UpperCAmelCase__ : Union[str, Any] = (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] ,)
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : Optional[int] = (
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:
UpperCAmelCase__ : Tuple = True
self.check_hidden_states_output(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase__ : Optional[Any] = True
self.check_hidden_states_output(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : List[Any] = 3
UpperCAmelCase__ : List[Any] = (
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)
)
UpperCAmelCase__ : Optional[Any] = (
config.patch_size
if isinstance(config.patch_size ,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
UpperCAmelCase__ : List[Any] = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
UpperCAmelCase__ : Optional[int] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
UpperCAmelCase__ : Optional[int] = True
self.check_hidden_states_output(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,(padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase__ : int = True
self.check_hidden_states_output(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,(padded_height, padded_width) )
@unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints''' )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin''' )
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin''' )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
pass
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(lowerCamelCase_ ):
UpperCAmelCase__ : int = 0
return t
def check_equivalence(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_={} ):
with torch.no_grad():
UpperCAmelCase__ : Any = model(**lowerCamelCase_ ,return_dict=lowerCamelCase_ ,**lowerCamelCase_ )
UpperCAmelCase__ : Optional[int] = model(**lowerCamelCase_ ,return_dict=lowerCamelCase_ ,**lowerCamelCase_ ).to_tuple()
def recursive_check(lowerCamelCase_ ,lowerCamelCase_ ):
if isinstance(lowerCamelCase_ ,(List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(lowerCamelCase_ ,lowerCamelCase_ ):
recursive_check(lowerCamelCase_ ,lowerCamelCase_ )
elif isinstance(lowerCamelCase_ ,lowerCamelCase_ ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() ,dict_object.values() ):
recursive_check(lowerCamelCase_ ,lowerCamelCase_ )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(lowerCamelCase_ ) ,set_nan_tensor_to_zero(lowerCamelCase_ ) ,atol=1e-5 ) ,msg=(
'''Tuple and dict output are not equal. Difference:'''
f''' {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:'''
f''' {torch.isnan(lowerCamelCase_ ).any()} and `inf`: {torch.isinf(lowerCamelCase_ )}. Dict has'''
f''' `nan`: {torch.isnan(lowerCamelCase_ ).any()} and `inf`: {torch.isinf(lowerCamelCase_ )}.'''
) ,)
recursive_check(lowerCamelCase_ ,lowerCamelCase_ )
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
UpperCAmelCase__ : Optional[int] = self._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_ )
UpperCAmelCase__ : Any = self._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_ )
check_equivalence(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ )
UpperCAmelCase__ : List[str] = self._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_ ,return_labels=lowerCamelCase_ )
UpperCAmelCase__ : Tuple = self._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_ ,return_labels=lowerCamelCase_ )
check_equivalence(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ )
UpperCAmelCase__ : int = self._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_ )
UpperCAmelCase__ : Optional[Any] = self._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_ )
check_equivalence(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,{'''output_hidden_states''': True} )
UpperCAmelCase__ : List[Any] = self._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_ ,return_labels=lowerCamelCase_ )
UpperCAmelCase__ : List[str] = self._prepare_for_class(lowerCamelCase_ ,lowerCamelCase_ ,return_labels=lowerCamelCase_ )
check_equivalence(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,{'''output_hidden_states''': True} )
@require_torch
class _lowercase ( unittest.TestCase ,lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase_ : Dict = (MaskFormerSwinBackbone,) if is_torch_available() else ()
UpperCAmelCase_ : Optional[int] = MaskFormerSwinConfig
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ : Tuple = MaskFormerSwinModelTester(self )
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : List[str] = inputs_dict['''pixel_values'''].shape[0]
for backbone_class in self.all_model_classes:
UpperCAmelCase__ : List[str] = backbone_class(lowerCamelCase_ )
backbone.to(lowerCamelCase_ )
backbone.eval()
UpperCAmelCase__ : List[Any] = backbone(**lowerCamelCase_ )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps ,lowerCamelCase_ )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps ,backbone.channels ):
self.assertTrue(feature_map.shape[:2] ,(batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
UpperCAmelCase__ : str = backbone(**lowerCamelCase_ ,output_hidden_states=lowerCamelCase_ )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) ,len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] ,backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : int = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) ,(batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
UpperCAmelCase__ : List[str] = backbone(**lowerCamelCase_ ,output_attentions=lowerCamelCase_ )
self.assertIsNotNone(outputs.attentions )
| 496 | 1 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ = 100_0000 ):
lowercase__ = set(range(3 , SCREAMING_SNAKE_CASE_ , 2 ) )
primes.add(2 )
for p in range(3 , SCREAMING_SNAKE_CASE_ , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) )
lowercase__ = [float(SCREAMING_SNAKE_CASE_ ) for n in range(limit + 1 )]
for p in primes:
for n in range(SCREAMING_SNAKE_CASE_ , limit + 1 , SCREAMING_SNAKE_CASE_ ):
phi[n] *= 1 - 1 / p
return int(sum(phi[2:] ) )
if __name__ == "__main__":
print(F'{solution() = }')
| 413 |
import math
def __lowerCAmelCase ( ):
lowercase__ = input("Enter message: " )
lowercase__ = int(input(f'''Enter key [2-{len(SCREAMING_SNAKE_CASE_ ) - 1}]: ''' ) )
lowercase__ = input("Encryption/Decryption [e/d]: " )
if mode.lower().startswith("e" ):
lowercase__ = encrypt_message(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif mode.lower().startswith("d" ):
lowercase__ = decrypt_message(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(f'''Output:\n{text + "|"}''' )
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase__ = [""] * key
for col in range(SCREAMING_SNAKE_CASE_ ):
lowercase__ = col
while pointer < len(SCREAMING_SNAKE_CASE_ ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(SCREAMING_SNAKE_CASE_ )
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase__ = math.ceil(len(SCREAMING_SNAKE_CASE_ ) / key )
lowercase__ = key
lowercase__ = (num_cols * num_rows) - len(SCREAMING_SNAKE_CASE_ )
lowercase__ = [""] * num_cols
lowercase__ = 0
lowercase__ = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
lowercase__ = 0
row += 1
return "".join(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 413 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase ( __snake_case , unittest.TestCase ):
__UpperCamelCase =LDMTextToImagePipeline
__UpperCamelCase =TEXT_TO_IMAGE_PARAMS - {
"negative_prompt",
"negative_prompt_embeds",
"cross_attention_kwargs",
"prompt_embeds",
}
__UpperCamelCase =PipelineTesterMixin.required_optional_params - {
"num_images_per_prompt",
"callback",
"callback_steps",
}
__UpperCamelCase =TEXT_TO_IMAGE_BATCH_PARAMS
__UpperCamelCase =False
def UpperCamelCase ( self : Dict ):
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=3_2 , )
SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=_lowercase , set_alpha_to_one=_lowercase , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = AutoencoderKL(
block_out_channels=(3_2, 6_4) , in_channels=3 , out_channels=3 , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , latent_channels=4 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
SCREAMING_SNAKE_CASE = CLIPTextModel(_lowercase )
SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
SCREAMING_SNAKE_CASE = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vqvae""": vae,
"""bert""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def UpperCamelCase ( self : Any , snake_case__ : List[str] , snake_case__ : List[str]=0 ):
"""simple docstring"""
if str(_lowercase ).startswith('mps' ):
SCREAMING_SNAKE_CASE = torch.manual_seed(_lowercase )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def UpperCamelCase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = LDMTextToImagePipeline(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(_lowercase )
SCREAMING_SNAKE_CASE = pipe(**_lowercase ).images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_6, 1_6, 3)
SCREAMING_SNAKE_CASE = np.array([0.6_101, 0.6_156, 0.5_622, 0.4_895, 0.6_661, 0.3_804, 0.5_748, 0.6_136, 0.5_014] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@slow
@require_torch_gpu
class UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase ( self : Any ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self : Union[str, Any] , snake_case__ : List[str] , snake_case__ : Optional[int]=torch.floataa , snake_case__ : int=0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = torch.manual_seed(_lowercase )
SCREAMING_SNAKE_CASE = np.random.RandomState(_lowercase ).standard_normal((1, 4, 3_2, 3_2) )
SCREAMING_SNAKE_CASE = torch.from_numpy(_lowercase ).to(device=_lowercase , dtype=_lowercase )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def UpperCamelCase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
SCREAMING_SNAKE_CASE = self.get_inputs(_lowercase )
SCREAMING_SNAKE_CASE = pipe(**_lowercase ).images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 2_5_6, 2_5_6, 3)
SCREAMING_SNAKE_CASE = np.array([0.51_825, 0.52_850, 0.52_543, 0.54_258, 0.52_304, 0.52_569, 0.54_363, 0.55_276, 0.56_878] )
SCREAMING_SNAKE_CASE = np.abs(expected_slice - image_slice ).max()
assert max_diff < 1E-3
@nightly
@require_torch_gpu
class UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self : Optional[int] , snake_case__ : Any , snake_case__ : Dict=torch.floataa , snake_case__ : Union[str, Any]=0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = torch.manual_seed(_lowercase )
SCREAMING_SNAKE_CASE = np.random.RandomState(_lowercase ).standard_normal((1, 4, 3_2, 3_2) )
SCREAMING_SNAKE_CASE = torch.from_numpy(_lowercase ).to(device=_lowercase , dtype=_lowercase )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 5_0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def UpperCamelCase ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
SCREAMING_SNAKE_CASE = self.get_inputs(_lowercase )
SCREAMING_SNAKE_CASE = pipe(**_lowercase ).images[0]
SCREAMING_SNAKE_CASE = load_numpy(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy' )
SCREAMING_SNAKE_CASE = np.abs(expected_image - image ).max()
assert max_diff < 1E-3
| 719 |
import heapq
import sys
import numpy as np
a_ : Optional[int] = tuple[int, int]
class UpperCamelCase :
def __init__( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = set()
def UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
if not self.empty():
return self.elements[0][0]
else:
return float('inf' )
def UpperCamelCase ( self : List[str] ):
"""simple docstring"""
return len(self.elements ) == 0
def UpperCamelCase ( self : Union[str, Any] , snake_case__ : Optional[Any] , snake_case__ : List[Any] ):
"""simple docstring"""
if item not in self.set:
heapq.heappush(self.elements , (priority, item) )
self.set.add(snake_case__ )
else:
# update
# print("update", item)
SCREAMING_SNAKE_CASE = []
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx) )
def UpperCamelCase ( self : Dict , snake_case__ : Dict ):
"""simple docstring"""
if item in self.set:
self.set.remove(snake_case__ )
SCREAMING_SNAKE_CASE = []
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy) )
def UpperCamelCase ( self : str ):
"""simple docstring"""
return self.elements[0][1]
def UpperCamelCase ( self : Tuple ):
"""simple docstring"""
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) = heapq.heappop(self.elements )
self.set.remove(snake_case__ )
return (priority, item)
def __lowerCAmelCase ( _UpperCamelCase : TPos , _UpperCamelCase : TPos ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = np.array(_UpperCamelCase )
SCREAMING_SNAKE_CASE = np.array(_UpperCamelCase )
return np.linalg.norm(a - b )
def __lowerCAmelCase ( _UpperCamelCase : TPos , _UpperCamelCase : TPos ) -> Dict:
'''simple docstring'''
return consistent_heuristic(_UpperCamelCase , _UpperCamelCase ) // t
def __lowerCAmelCase ( _UpperCamelCase : TPos , _UpperCamelCase : TPos ) -> Optional[int]:
'''simple docstring'''
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def __lowerCAmelCase ( _UpperCamelCase : TPos , _UpperCamelCase : int , _UpperCamelCase : TPos , _UpperCamelCase : dict[TPos, float] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = g_function[start] + Wa * heuristics[i](_UpperCamelCase , _UpperCamelCase )
return ans
def __lowerCAmelCase ( _UpperCamelCase : List[str] , _UpperCamelCase : int , _UpperCamelCase : Tuple ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = np.chararray((n, n) )
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
SCREAMING_SNAKE_CASE = '*'
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
if (j, (n - 1) - i) in blocks:
SCREAMING_SNAKE_CASE = '#'
SCREAMING_SNAKE_CASE = '-'
SCREAMING_SNAKE_CASE = back_pointer[goal]
while x != start:
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) = x
# print(x)
SCREAMING_SNAKE_CASE = '-'
SCREAMING_SNAKE_CASE = back_pointer[x]
SCREAMING_SNAKE_CASE = '-'
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=' ' )
print('<-- End position' , end=' ' )
else:
print(grid[i][j] , end=' ' )
print()
print('^' )
print('Start position' )
print()
print('# is an obstacle' )
print('- is the path taken by algorithm' )
print('PATH TAKEN BY THE ALGORITHM IS:-' )
SCREAMING_SNAKE_CASE = back_pointer[goal]
while x != start:
print(_UpperCamelCase , end=' ' )
SCREAMING_SNAKE_CASE = back_pointer[x]
print(_UpperCamelCase )
sys.exit()
def __lowerCAmelCase ( _UpperCamelCase : TPos ) -> Any:
'''simple docstring'''
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def __lowerCAmelCase ( _UpperCamelCase : List[Any] , _UpperCamelCase : Tuple , _UpperCamelCase : Any , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Tuple , _UpperCamelCase : List[Any] , _UpperCamelCase : Optional[Any] , ) -> List[Any]:
'''simple docstring'''
for itera in range(_UpperCamelCase ):
open_list[itera].remove_element(_UpperCamelCase )
# print("s", s)
# print("j", j)
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) = s
SCREAMING_SNAKE_CASE = (x - 1, y)
SCREAMING_SNAKE_CASE = (x + 1, y)
SCREAMING_SNAKE_CASE = (x, y + 1)
SCREAMING_SNAKE_CASE = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(_UpperCamelCase ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(_UpperCamelCase )
SCREAMING_SNAKE_CASE = -1
SCREAMING_SNAKE_CASE = float('inf' )
if valid(_UpperCamelCase ) and g_function[neighbours] > g_function[s] + 1:
SCREAMING_SNAKE_CASE = g_function[s] + 1
SCREAMING_SNAKE_CASE = s
if neighbours not in close_list_anchor:
open_list[0].put(_UpperCamelCase , key(_UpperCamelCase , 0 , _UpperCamelCase , _UpperCamelCase ) )
if neighbours not in close_list_inad:
for var in range(1 , _UpperCamelCase ):
if key(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) <= Wa * key(
_UpperCamelCase , 0 , _UpperCamelCase , _UpperCamelCase ):
open_list[j].put(
_UpperCamelCase , key(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) )
def __lowerCAmelCase ( ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(15 , 20 ):
some_list.append((x, 17) )
for x in range(10 , 19 ):
for y in range(1 , 15 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(12 , 19 ):
some_list.append((x, y) )
for x in range(3 , 13 ):
for y in range(16 , 19 ):
some_list.append((x, y) )
return some_list
a_ : str = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
a_ : List[str] = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
a_ : Union[str, Any] = make_common_ground()
a_ : Tuple = blocks_blk
# hyper parameters
a_ : Any = 1
a_ : List[str] = 1
a_ : Union[str, Any] = 20
a_ : Optional[Any] = 3 # one consistent and two other inconsistent
# start and end destination
a_ : int = (0, 0)
a_ : Optional[int] = (n - 1, n - 1)
a_ : Union[str, Any] = 1
def __lowerCAmelCase ( _UpperCamelCase : TPos , _UpperCamelCase : TPos , _UpperCamelCase : int ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {start: 0, goal: float('inf' )}
SCREAMING_SNAKE_CASE = {start: -1, goal: -1}
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = set()
for i in range(_UpperCamelCase ):
open_list.append(PriorityQueue() )
open_list[i].put(_UpperCamelCase , key(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
while open_list[0].minkey() < float('inf' ):
for i in range(1 , _UpperCamelCase ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float('inf' ):
do_something(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = open_list[i].top_show()
visited.add(_UpperCamelCase )
expand_state(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , )
close_list_inad.append(_UpperCamelCase )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float('inf' ):
do_something(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
else:
SCREAMING_SNAKE_CASE = open_list[0].top_show()
visited.add(_UpperCamelCase )
expand_state(
_UpperCamelCase , 0 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , )
close_list_anchor.append(_UpperCamelCase )
print('No path found to goal' )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(_UpperCamelCase ):
if (j, i) in blocks:
print('#' , end=' ' )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print('*' , end=' ' )
else:
print('-' , end=' ' )
else:
print('*' , end=' ' )
if (j, i) == (n - 1, n - 1):
print('<-- End position' , end=' ' )
print()
print('^' )
print('Start position' )
print()
print('# is an obstacle' )
print('- is the path taken by algorithm' )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 673 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( lowerCamelCase_ : list , lowerCamelCase_ : int ):
# Checks if the entire collection has been sorted
if len(lowerCamelCase_ ) <= 1 or n <= 1:
return
insert_next(lowerCamelCase_ , n - 1 )
rec_insertion_sort(lowerCamelCase_ , n - 1 )
def _lowerCAmelCase ( lowerCamelCase_ : list , lowerCamelCase_ : int ):
# Checks order between adjacent elements
if index >= len(lowerCamelCase_ ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
__lowercase , __lowercase = (
collection[index],
collection[index - 1],
)
insert_next(lowerCamelCase_ , index + 1 )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = input('''Enter integers separated by spaces: ''')
_SCREAMING_SNAKE_CASE = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| 502 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( lowerCAmelCase__ ):
'''simple docstring'''
a : List[Any] = ["image_processor", "tokenizer"]
a : Optional[int] = "ChineseCLIPImageProcessor"
a : Dict = ("BertTokenizer", "BertTokenizerFast")
def __init__(self ,_lowerCamelCase=None ,_lowerCamelCase=None ,**_lowerCamelCase ) -> str:
'''simple docstring'''
__lowercase = 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 ,)
__lowercase = kwargs.pop('''feature_extractor''' )
__lowercase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(_lowerCamelCase ,_lowerCamelCase )
__lowercase = self.image_processor
def __call__(self ,_lowerCamelCase=None ,_lowerCamelCase=None ,_lowerCamelCase=None ,**_lowerCamelCase ) -> List[Any]:
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
__lowercase = self.tokenizer(_lowerCamelCase ,return_tensors=_lowerCamelCase ,**_lowerCamelCase )
if images is not None:
__lowercase = self.image_processor(_lowerCamelCase ,return_tensors=_lowerCamelCase ,**_lowerCamelCase )
if text is not None and images is not None:
__lowercase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_lowerCamelCase ) ,tensor_type=_lowerCamelCase )
def _UpperCAmelCase (self ,*_lowerCamelCase ,**_lowerCamelCase ) -> str:
'''simple docstring'''
return self.tokenizer.batch_decode(*_lowerCamelCase ,**_lowerCamelCase )
def _UpperCAmelCase (self ,*_lowerCamelCase ,**_lowerCamelCase ) -> Dict:
'''simple docstring'''
return self.tokenizer.decode(*_lowerCamelCase ,**_lowerCamelCase )
@property
def _UpperCAmelCase (self ) -> Dict:
'''simple docstring'''
__lowercase = self.tokenizer.model_input_names
__lowercase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _UpperCAmelCase (self ) -> int:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,_lowerCamelCase ,)
return self.image_processor_class
| 502 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
UpperCAmelCase__ : str = TypeVar("""T""")
class a ( Generic[T] ):
def __init__( self : str , __lowerCAmelCase : list[T] , __lowerCAmelCase : Callable[[T, T], T] ):
_UpperCAmelCase = None
_UpperCAmelCase = len(__lowerCAmelCase )
_UpperCAmelCase = [any_type for _ in range(self.N )] + arr
_UpperCAmelCase = fnc
self.build()
def lowerCAmelCase_ ( self : Union[str, Any] ):
for p in range(self.N - 1 , 0 , -1 ):
_UpperCAmelCase = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowerCAmelCase_ ( self : Any , __lowerCAmelCase : int , __lowerCAmelCase : T ):
p += self.N
_UpperCAmelCase = v
while p > 1:
_UpperCAmelCase = p // 2
_UpperCAmelCase = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowerCAmelCase_ ( self : int , __lowerCAmelCase : int , __lowerCAmelCase : int ): # noqa: E741
_UpperCAmelCase , _UpperCAmelCase = l + self.N, r + self.N
_UpperCAmelCase = None
while l <= r:
if l % 2 == 1:
_UpperCAmelCase = self.st[l] if res is None else self.fn(__lowerCAmelCase , self.st[l] )
if r % 2 == 0:
_UpperCAmelCase = self.st[r] if res is None else self.fn(__lowerCAmelCase , self.st[r] )
_UpperCAmelCase , _UpperCAmelCase = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
UpperCAmelCase__ : List[str] = [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]
UpperCAmelCase__ : Optional[Any] = {
0: 7,
1: 2,
2: 6,
3: -1_4,
4: 5,
5: 4,
6: 7,
7: -1_0,
8: 9,
9: 1_0,
1_0: 1_2,
1_1: 1,
}
UpperCAmelCase__ : Tuple = SegmentTree(test_array, min)
UpperCAmelCase__ : int = SegmentTree(test_array, max)
UpperCAmelCase__ : int = SegmentTree(test_array, lambda a, b: a + b)
def __UpperCAmelCase ( ):
"""simple docstring"""
for i in range(len(lowercase ) ):
for j in range(lowercase ,len(lowercase ) ):
_UpperCAmelCase = reduce(lowercase ,test_array[i : j + 1] )
_UpperCAmelCase = reduce(lowercase ,test_array[i : j + 1] )
_UpperCAmelCase = reduce(lambda lowercase ,lowercase : a + b ,test_array[i : j + 1] )
assert min_range == min_segment_tree.query(lowercase ,lowercase )
assert max_range == max_segment_tree.query(lowercase ,lowercase )
assert sum_range == sum_segment_tree.query(lowercase ,lowercase )
test_all_segments()
for index, value in test_updates.items():
UpperCAmelCase__ : str = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 718 | """simple docstring"""
# Lint as: python3
import itertools
import os
import re
UpperCAmelCase__ = re.compile(r"""([A-Z]+)([A-Z][a-z])""")
UpperCAmelCase__ = re.compile(r"""([a-z\d])([A-Z])""")
UpperCAmelCase__ = re.compile(r"""(?<!_)_(?!_)""")
UpperCAmelCase__ = re.compile(r"""(_{2,})""")
UpperCAmelCase__ = r"""^\w+(\.\w+)*$"""
UpperCAmelCase__ = r"""<>:/\|?*"""
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = _uppercase_uppercase_re.sub(R"""\1_\2""" ,lowercase )
_UpperCAmelCase = _lowercase_uppercase_re.sub(R"""\1_\2""" ,lowercase )
return name.lower()
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = _single_underscore_re.split(lowercase )
_UpperCAmelCase = [_multiple_underscores_re.split(lowercase ) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowercase ) if n != """""" )
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
if os.path.basename(lowercase ) != name:
raise ValueError(f'''Should be a dataset name, not a path: {name}''' )
return camelcase_to_snakecase(lowercase )
def __UpperCAmelCase ( lowercase ,lowercase ):
"""simple docstring"""
if os.path.basename(lowercase ) != name:
raise ValueError(f'''Should be a dataset name, not a path: {name}''' )
if not re.match(_split_re ,lowercase ):
raise ValueError(f'''Split name should match \'{_split_re}\'\' but got \'{split}\'.''' )
return f'''{filename_prefix_for_name(lowercase )}-{split}'''
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase=None ):
"""simple docstring"""
_UpperCAmelCase = filename_prefix_for_split(lowercase ,lowercase )
if filetype_suffix:
prefix += f'''.{filetype_suffix}'''
_UpperCAmelCase = os.path.join(lowercase ,lowercase )
return f'''{filepath}*'''
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase=None ,lowercase=None ):
"""simple docstring"""
_UpperCAmelCase = filename_prefix_for_split(lowercase ,lowercase )
_UpperCAmelCase = os.path.join(lowercase ,lowercase )
if shard_lengths:
_UpperCAmelCase = len(lowercase )
_UpperCAmelCase = [f'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(lowercase )]
if filetype_suffix:
_UpperCAmelCase = [filename + f'''.{filetype_suffix}''' for filename in filenames]
return filenames
else:
_UpperCAmelCase = prefix
if filetype_suffix:
filename += f'''.{filetype_suffix}'''
return [filename]
| 275 | 0 |
"""simple docstring"""
import numpy
# List of input, output pairs
__lowercase : List[Any] = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
__lowercase : int = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
__lowercase : Dict = [2, 4, 1, 5]
__lowercase : Tuple = len(train_data)
__lowercase : List[str] = 0.0_0_9
def lowerCamelCase_ ( _lowerCamelCase : int , _lowerCamelCase : str="train" ):
return calculate_hypothesis_value(_lowerCamelCase , _lowerCamelCase ) - output(
_lowerCamelCase , _lowerCamelCase )
def lowerCamelCase_ ( _lowerCamelCase : Any ):
lowerCamelCase_ = 0
for i in range(len(_lowerCamelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def lowerCamelCase_ ( _lowerCamelCase : str , _lowerCamelCase : Optional[int] ):
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def lowerCamelCase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Tuple ):
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def lowerCamelCase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : Tuple=m ):
lowerCamelCase_ = 0
for i in range(_lowerCamelCase ):
if index == -1:
summation_value += _error(_lowerCamelCase )
else:
summation_value += _error(_lowerCamelCase ) * train_data[i][0][index]
return summation_value
def lowerCamelCase_ ( _lowerCamelCase : Optional[Any] ):
lowerCamelCase_ = summation_of_cost_derivative(_lowerCamelCase , _lowerCamelCase ) / m
return cost_derivative_value
def lowerCamelCase_ ( ):
global parameter_vector
# Tune these values to set a tolerance value for predicted output
lowerCamelCase_ = 0.00_00_02
lowerCamelCase_ = 0
lowerCamelCase_ = 0
while True:
j += 1
lowerCamelCase_ = [0, 0, 0, 0]
for i in range(0 , len(_lowerCamelCase ) ):
lowerCamelCase_ = get_cost_derivative(i - 1 )
lowerCamelCase_ = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
_lowerCamelCase , _lowerCamelCase , atol=_lowerCamelCase , rtol=_lowerCamelCase , ):
break
lowerCamelCase_ = temp_parameter_vector
print(('''Number of iterations:''', j) )
def lowerCamelCase_ ( ):
for i in range(len(_lowerCamelCase ) ):
print(('''Actual output value:''', output(_lowerCamelCase , '''test''' )) )
print(('''Hypothesis output:''', calculate_hypothesis_value(_lowerCamelCase , '''test''' )) )
if __name__ == "__main__":
run_gradient_descent()
print("""\nTesting gradient descent for a linear hypothesis function.\n""")
test_gradient_descent() | 142 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Optional[Any] = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
"""convert_funnel_original_tf_checkpoint_to_pytorch""": [],
"""tokenization_funnel""": ["""FunnelTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : int = ["""FunnelTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Optional[Any] = [
"""FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FunnelBaseModel""",
"""FunnelForMaskedLM""",
"""FunnelForMultipleChoice""",
"""FunnelForPreTraining""",
"""FunnelForQuestionAnswering""",
"""FunnelForSequenceClassification""",
"""FunnelForTokenClassification""",
"""FunnelModel""",
"""FunnelPreTrainedModel""",
"""load_tf_weights_in_funnel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Union[str, Any] = [
"""TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFFunnelBaseModel""",
"""TFFunnelForMaskedLM""",
"""TFFunnelForMultipleChoice""",
"""TFFunnelForPreTraining""",
"""TFFunnelForQuestionAnswering""",
"""TFFunnelForSequenceClassification""",
"""TFFunnelForTokenClassification""",
"""TFFunnelModel""",
"""TFFunnelPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
__lowercase : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 142 | 1 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def snake_case_ ( __snake_case : int) -> datetime:
lowerCAmelCase_ = year % 19
lowerCAmelCase_ = year % 4
lowerCAmelCase_ = year % 7
lowerCAmelCase_ = math.floor(year / 100)
lowerCAmelCase_ = math.floor((13 + 8 * leap_day_inhibits) / 25)
lowerCAmelCase_ = leap_day_inhibits / 4
lowerCAmelCase_ = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
lowerCAmelCase_ = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
lowerCAmelCase_ = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
lowerCAmelCase_ = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(__snake_case , 4 , 19)
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(__snake_case , 4 , 18)
else:
return datetime(__snake_case , 3 , 22) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday))
if __name__ == "__main__":
for year in (19_94, 20_00, 20_10, 20_21, 20_23):
A_ : Tuple ='''will be''' if year > datetime.now().year else '''was'''
print(f'''Easter in {year} {tense} {gauss_easter(year)}''')
| 606 | '''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
A_ : Tuple =[
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
['''memory_attention''', '''encoder_attn'''],
['''attention''', '''attn'''],
['''/''', '''.'''],
['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
['''.LayerNorm.beta''', '''_layer_norm.bias'''],
['''r.layer_''', '''r.layers.'''],
['''output_proj''', '''out_proj'''],
['''ffn.dense_1.''', '''fc2.'''],
['''ffn.dense.''', '''fc1.'''],
['''ffn_layer_norm''', '''final_layer_norm'''],
['''kernel''', '''weight'''],
['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
['''embeddings.weights''', '''shared.weight'''],
]
def snake_case_ ( __snake_case : Union[str, Any]) -> Optional[Any]:
for pegasus_name, hf_name in PATTERNS:
lowerCAmelCase_ = k.replace(__snake_case , __snake_case)
return k
def snake_case_ ( __snake_case : dict , __snake_case : dict) -> PegasusForConditionalGeneration:
lowerCAmelCase_ = DEFAULTS.copy()
cfg_kwargs.update(__snake_case)
lowerCAmelCase_ = PegasusConfig(**__snake_case)
lowerCAmelCase_ = PegasusForConditionalGeneration(__snake_case)
lowerCAmelCase_ = torch_model.model.state_dict()
lowerCAmelCase_ = {}
for k, v in tf_weights.items():
lowerCAmelCase_ = rename_state_dict_key(__snake_case)
if new_k not in sd:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''')
if "dense" in k or "proj" in new_k:
lowerCAmelCase_ = v.T
lowerCAmelCase_ = torch.tensor(__snake_case , dtype=sd[new_k].dtype)
assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
# make sure embedding.padding_idx is respected
lowerCAmelCase_ = torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1])
lowerCAmelCase_ = mapping['''shared.weight''']
lowerCAmelCase_ = mapping['''shared.weight''']
lowerCAmelCase_ = {k: torch.zeros_like(__snake_case) for k, v in sd.items() if k.endswith('''bias''') and k not in mapping}
mapping.update(**__snake_case)
lowerCAmelCase_ ,lowerCAmelCase_ = torch_model.model.load_state_dict(__snake_case , strict=__snake_case)
lowerCAmelCase_ = [
k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
]
assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], F'''no matches found for the following tf keys {extra}'''
return torch_model
def snake_case_ ( __snake_case : Optional[int]="./ckpt/aeslc/model.ckpt-32000") -> Dict:
lowerCAmelCase_ = tf.train.list_variables(__snake_case)
lowerCAmelCase_ = {}
lowerCAmelCase_ = ['''Adafactor''', '''global_step''']
for name, shape in tqdm(__snake_case , desc='''converting tf checkpoint to dict'''):
lowerCAmelCase_ = any(pat in name for pat in ignore_name)
if skip_key:
continue
lowerCAmelCase_ = tf.train.load_variable(__snake_case , __snake_case)
lowerCAmelCase_ = array
return tf_weights
def snake_case_ ( __snake_case : str , __snake_case : str) -> Optional[int]:
# save tokenizer first
lowerCAmelCase_ = Path(__snake_case).parent.name
lowerCAmelCase_ = task_specific_params[F'''summarization_{dataset}''']['''max_position_embeddings''']
lowerCAmelCase_ = PegasusTokenizer.from_pretrained('''sshleifer/pegasus''' , model_max_length=__snake_case)
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__snake_case)
# convert model
lowerCAmelCase_ = get_tf_weights_as_numpy(__snake_case)
lowerCAmelCase_ = task_specific_params[F'''summarization_{dataset}''']
if dataset == "large":
lowerCAmelCase_ = task_specific_params
lowerCAmelCase_ = convert_pegasus(__snake_case , __snake_case)
torch_model.save_pretrained(__snake_case)
lowerCAmelCase_ = torch_model.state_dict()
sd.pop('''model.decoder.embed_positions.weight''')
sd.pop('''model.encoder.embed_positions.weight''')
torch.save(__snake_case , Path(__snake_case) / '''pytorch_model.bin''')
if __name__ == "__main__":
A_ : str =argparse.ArgumentParser()
# Required parameters
parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
A_ : Union[str, Any] =parser.parse_args()
if args.save_dir is None:
A_ : List[Any] =Path(args.tf_ckpt_path).parent.name
A_ : Optional[int] =os.path.join('''pegasus''', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 606 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __lowerCamelCase (unittest.TestCase ):
@slow
def snake_case_ ( self: Any ):
'''simple docstring'''
__UpperCamelCase = TFCamembertModel.from_pretrained('jplu/tf-camembert-base' )
__UpperCamelCase = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]],dtype=tf.intaa,) # J'aime le camembert !"
__UpperCamelCase = model(A_ )['last_hidden_state']
__UpperCamelCase = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape,A_ )
# compare the actual values for a slice.
__UpperCamelCase = tf.convert_to_tensor(
[[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]],dtype=tf.floataa,)
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy(),expected_slice.numpy(),atol=1E-4 ) )
| 1 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy
snake_case__ : Optional[Any] = logging.get_logger(__name__)
snake_case__ : List[Any] = {
'artists_file': 'artists.json',
'lyrics_file': 'lyrics.json',
'genres_file': 'genres.json',
}
snake_case__ : int = {
'artists_file': {
'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json',
},
'genres_file': {
'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json',
},
'lyrics_file': {
'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json',
},
}
snake_case__ : Optional[int] = {
'jukebox': 5_1_2,
}
class _a ( A__ ):
"""simple docstring"""
snake_case =VOCAB_FILES_NAMES
snake_case =PRETRAINED_VOCAB_FILES_MAP
snake_case =PRETRAINED_LYRIC_TOKENS_SIZES
snake_case =["""input_ids""", """attention_mask"""]
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case=["v3", "v2", "v2"] , _snake_case=512 , _snake_case=5 , _snake_case="<|endoftext|>" , **_snake_case , ):
_UpperCAmelCase =AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else unk_token
super().__init__(
unk_token=_snake_case , n_genres=_snake_case , version=_snake_case , max_n_lyric_tokens=_snake_case , **_snake_case , )
_UpperCAmelCase =version
_UpperCAmelCase =max_n_lyric_tokens
_UpperCAmelCase =n_genres
with open(_snake_case , encoding="utf-8" ) as vocab_handle:
_UpperCAmelCase =json.load(_snake_case )
with open(_snake_case , encoding="utf-8" ) as vocab_handle:
_UpperCAmelCase =json.load(_snake_case )
with open(_snake_case , encoding="utf-8" ) as vocab_handle:
_UpperCAmelCase =json.load(_snake_case )
_UpperCAmelCase =R"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"
# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
if len(self.lyrics_encoder ) == 79:
_UpperCAmelCase =oov.replace(R"\-'" , R"\-+'" )
_UpperCAmelCase =regex.compile(_snake_case )
_UpperCAmelCase ={v: k for k, v in self.artists_encoder.items()}
_UpperCAmelCase ={v: k for k, v in self.genres_encoder.items()}
_UpperCAmelCase ={v: k for k, v in self.lyrics_encoder.items()}
@property
def SCREAMING_SNAKE_CASE ( self ):
return len(self.artists_encoder ) + len(self.genres_encoder ) + len(self.lyrics_encoder )
def SCREAMING_SNAKE_CASE ( self ):
return dict(self.artists_encoder , self.genres_encoder , self.lyrics_encoder )
def SCREAMING_SNAKE_CASE ( self , _snake_case , _snake_case , _snake_case ):
_UpperCAmelCase =[self.artists_encoder.get(_snake_case , 0 ) for artist in list_artists]
for genres in range(len(_snake_case ) ):
_UpperCAmelCase =[self.genres_encoder.get(_snake_case , 0 ) for genre in list_genres[genres]]
_UpperCAmelCase =list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres] ))
_UpperCAmelCase =[[self.lyrics_encoder.get(_snake_case , 0 ) for character in list_lyrics[0]], [], []]
return artists_id, list_genres, lyric_ids
def SCREAMING_SNAKE_CASE ( self , _snake_case ):
return list(_snake_case )
def SCREAMING_SNAKE_CASE ( self , _snake_case , _snake_case , _snake_case , **_snake_case ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase =self.prepare_for_tokenization(_snake_case , _snake_case , _snake_case )
_UpperCAmelCase =self._tokenize(_snake_case )
return artist, genre, lyrics
def SCREAMING_SNAKE_CASE ( self , _snake_case , _snake_case , _snake_case , _snake_case = False ):
for idx in range(len(self.version ) ):
if self.version[idx] == "v3":
_UpperCAmelCase =artists[idx].lower()
_UpperCAmelCase =[genres[idx].lower()]
else:
_UpperCAmelCase =self._normalize(artists[idx] ) + ".v2"
_UpperCAmelCase =[
self._normalize(_snake_case ) + ".v2" for genre in genres[idx].split("_" )
] # split is for the full dictionary with combined genres
if self.version[0] == "v2":
_UpperCAmelCase =regex.compile(R"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+" )
_UpperCAmelCase ="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"
_UpperCAmelCase ={vocab[index]: index + 1 for index in range(len(_snake_case ) )}
_UpperCAmelCase =0
_UpperCAmelCase =len(_snake_case ) + 1
_UpperCAmelCase =self.vocab
_UpperCAmelCase ={v: k for k, v in self.vocab.items()}
_UpperCAmelCase =""
else:
_UpperCAmelCase =regex.compile(R"[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+" )
_UpperCAmelCase =self._run_strip_accents(_snake_case )
_UpperCAmelCase =lyrics.replace("\\" , "\n" )
_UpperCAmelCase =self.out_of_vocab.sub("" , _snake_case ), [], []
return artists, genres, lyrics
def SCREAMING_SNAKE_CASE ( self , _snake_case ):
_UpperCAmelCase =unicodedata.normalize("NFD" , _snake_case )
_UpperCAmelCase =[]
for char in text:
_UpperCAmelCase =unicodedata.category(_snake_case )
if cat == "Mn":
continue
output.append(_snake_case )
return "".join(_snake_case )
def SCREAMING_SNAKE_CASE ( self , _snake_case ):
_UpperCAmelCase =(
[chr(_snake_case ) for i in range(ord("a" ) , ord("z" ) + 1 )]
+ [chr(_snake_case ) for i in range(ord("A" ) , ord("Z" ) + 1 )]
+ [chr(_snake_case ) for i in range(ord("0" ) , ord("9" ) + 1 )]
+ ["."]
)
_UpperCAmelCase =frozenset(_snake_case )
_UpperCAmelCase =re.compile(R"_+" )
_UpperCAmelCase ="".join([c if c in accepted else "_" for c in text.lower()] )
_UpperCAmelCase =pattern.sub("_" , _snake_case ).strip("_" )
return text
def SCREAMING_SNAKE_CASE ( self , _snake_case ):
return " ".join(_snake_case )
def SCREAMING_SNAKE_CASE ( self , _snake_case , _snake_case = None , _snake_case = False ):
# Convert to TensorType
if not isinstance(_snake_case , _snake_case ):
_UpperCAmelCase =TensorType(_snake_case )
# Get a function reference for the correct framework
if tensor_type == TensorType.TENSORFLOW:
if not is_tf_available():
raise ImportError(
"Unable to convert output to TensorFlow tensors format, TensorFlow is not installed." )
import tensorflow as tf
_UpperCAmelCase =tf.constant
_UpperCAmelCase =tf.is_tensor
elif tensor_type == TensorType.PYTORCH:
if not is_torch_available():
raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed." )
import torch
_UpperCAmelCase =torch.tensor
_UpperCAmelCase =torch.is_tensor
elif tensor_type == TensorType.JAX:
if not is_flax_available():
raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed." )
import jax.numpy as jnp # noqa: F811
_UpperCAmelCase =jnp.array
_UpperCAmelCase =_is_jax
else:
_UpperCAmelCase =np.asarray
_UpperCAmelCase =_is_numpy
# Do the tensor conversion in batch
try:
if prepend_batch_axis:
_UpperCAmelCase =[inputs]
if not is_tensor(_snake_case ):
_UpperCAmelCase =as_tensor(_snake_case )
except: # noqa E722
raise ValueError(
"Unable to create tensor, you should probably activate truncation and/or padding "
"with 'padding=True' 'truncation=True' to have batched tensors with the same length." )
return inputs
def __call__( self , _snake_case , _snake_case , _snake_case="" , _snake_case="pt" ):
_UpperCAmelCase =[0, 0, 0]
_UpperCAmelCase =[artist] * len(self.version )
_UpperCAmelCase =[genres] * len(self.version )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase =self.tokenize(_snake_case , _snake_case , _snake_case )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase =self._convert_token_to_id(_snake_case , _snake_case , _snake_case )
_UpperCAmelCase =[-INFINITY] * len(full_tokens[-1] )
_UpperCAmelCase =[
self.convert_to_tensors(
[input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]] , tensor_type=_snake_case )
for i in range(len(self.version ) )
]
return BatchEncoding({"input_ids": input_ids, "attention_masks": attention_masks} )
def SCREAMING_SNAKE_CASE ( self , _snake_case , _snake_case = None ):
if not os.path.isdir(_snake_case ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
_UpperCAmelCase =os.path.join(
_snake_case , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["artists_file"] )
with open(_snake_case , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.artists_encoder , ensure_ascii=_snake_case ) )
_UpperCAmelCase =os.path.join(
_snake_case , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["genres_file"] )
with open(_snake_case , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.genres_encoder , ensure_ascii=_snake_case ) )
_UpperCAmelCase =os.path.join(
_snake_case , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["lyrics_file"] )
with open(_snake_case , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.lyrics_encoder , ensure_ascii=_snake_case ) )
return (artists_file, genres_file, lyrics_file)
def SCREAMING_SNAKE_CASE ( self , _snake_case , _snake_case , _snake_case ):
_UpperCAmelCase =self.artists_decoder.get(_snake_case )
_UpperCAmelCase =[self.genres_decoder.get(_snake_case ) for genre in genres_index]
_UpperCAmelCase =[self.lyrics_decoder.get(_snake_case ) for character in lyric_index]
return artist, genres, lyrics
| 408 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase__ :
def __init__( self : Optional[int] , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Optional[int]=12 , lowerCamelCase__ : Union[str, Any]=7 , lowerCamelCase__ : List[Any]=True , lowerCamelCase__ : Optional[int]=True , lowerCamelCase__ : Dict=True , lowerCamelCase__ : Dict=99 , lowerCamelCase__ : str=32 , lowerCamelCase__ : List[str]=32 , lowerCamelCase__ : Dict=2 , lowerCamelCase__ : int=4 , lowerCamelCase__ : Any=37 , lowerCamelCase__ : Optional[Any]=0.1 , lowerCamelCase__ : Any=0.1 , lowerCamelCase__ : Tuple=5_12 , lowerCamelCase__ : List[Any]=0.0_2 , lowerCamelCase__ : List[Any]=0 , lowerCamelCase__ : Optional[int]=None , ) ->List[Any]:
'''simple docstring'''
_UpperCAmelCase : List[str] = parent
_UpperCAmelCase : List[Any] = batch_size
_UpperCAmelCase : str = seq_length
_UpperCAmelCase : Dict = is_training
_UpperCAmelCase : str = use_input_mask
_UpperCAmelCase : Optional[Any] = use_labels
_UpperCAmelCase : Union[str, Any] = vocab_size
_UpperCAmelCase : int = hidden_size
_UpperCAmelCase : Optional[int] = projection_dim
_UpperCAmelCase : Dict = num_hidden_layers
_UpperCAmelCase : Optional[int] = num_attention_heads
_UpperCAmelCase : Any = intermediate_size
_UpperCAmelCase : Optional[Any] = dropout
_UpperCAmelCase : Optional[Any] = attention_dropout
_UpperCAmelCase : Tuple = max_position_embeddings
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : List[Any] = scope
_UpperCAmelCase : int = bos_token_id
def lowerCAmelCase__ ( self : Optional[Any] ) ->Dict:
'''simple docstring'''
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : str = None
if self.use_input_mask:
_UpperCAmelCase : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
_UpperCAmelCase : int = input_mask.numpy()
_UpperCAmelCase : List[Any] = input_mask.shape
_UpperCAmelCase : Dict = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase__ ):
_UpperCAmelCase : List[str] = 1
_UpperCAmelCase : List[Any] = 0
_UpperCAmelCase : List[Any] = self.get_config()
return config, input_ids, tf.convert_to_tensor(lowerCamelCase__ )
def lowerCAmelCase__ ( self : str ) ->List[str]:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def lowerCAmelCase__ ( self : Any , lowerCamelCase__ : int , lowerCamelCase__ : str , lowerCamelCase__ : Tuple ) ->Dict:
'''simple docstring'''
_UpperCAmelCase : int = TFBlipTextModel(config=lowerCamelCase__ )
_UpperCAmelCase : Optional[int] = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , training=lowerCamelCase__ )
_UpperCAmelCase : Dict = model(lowerCamelCase__ , training=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 lowerCAmelCase__ ( self : Any ) ->Tuple:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
_UpperCAmelCase : Tuple = config_and_inputs
_UpperCAmelCase : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class lowerCAmelCase__ ( UpperCAmelCase__ , unittest.TestCase ):
lowerCAmelCase : Tuple = (TFBlipTextModel,) if is_tf_available() else ()
lowerCAmelCase : List[str] = False
lowerCAmelCase : List[Any] = False
lowerCAmelCase : Optional[Any] = False
def lowerCAmelCase__ ( self : str ) ->List[str]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = BlipTextModelTester(self )
_UpperCAmelCase : Any = ConfigTester(self , config_class=lowerCamelCase__ , hidden_size=37 )
def lowerCAmelCase__ ( self : Optional[Any] ) ->Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCAmelCase__ ( self : Optional[int] ) ->Tuple:
'''simple docstring'''
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def lowerCAmelCase__ ( self : Optional[Any] ) ->Optional[int]:
'''simple docstring'''
pass
def lowerCAmelCase__ ( self : Tuple ) ->Dict:
'''simple docstring'''
pass
@unittest.skip(reason="Blip does not use inputs_embeds" )
def lowerCAmelCase__ ( self : Optional[int] ) ->List[str]:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def lowerCAmelCase__ ( self : str ) ->Any:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def lowerCAmelCase__ ( self : Union[str, Any] ) ->Optional[int]:
'''simple docstring'''
pass
@slow
def lowerCAmelCase__ ( self : int ) ->Optional[Any]:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Tuple = TFBlipTextModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowerCAmelCase__ ( self : str , lowerCamelCase__ : Tuple=True ) ->Dict:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCamelCase__ )
| 708 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
' Distillation'
)
)
parser.add_argument('--model_type', default='bert', choices=['bert'])
parser.add_argument('--model_name', default='bert-base-uncased', type=str)
parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
parser.add_argument('--vocab_transform', action='store_true')
lowerCamelCase__ = parser.parse_args()
if args.model_type == "bert":
lowerCamelCase__ = BertForMaskedLM.from_pretrained(args.model_name)
lowerCamelCase__ = 'bert'
else:
raise ValueError('args.model_type should be "bert".')
lowerCamelCase__ = model.state_dict()
lowerCamelCase__ = {}
for w in ["word_embeddings", "position_embeddings"]:
lowerCamelCase__ = state_dict[F'''{prefix}.embeddings.{w}.weight''']
for w in ["weight", "bias"]:
lowerCamelCase__ = state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']
lowerCamelCase__ = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
lowerCamelCase__ = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
]
lowerCamelCase__ = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
]
lowerCamelCase__ = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
]
lowerCamelCase__ = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
]
lowerCamelCase__ = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
]
lowerCamelCase__ = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
]
lowerCamelCase__ = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
]
lowerCamelCase__ = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
]
std_idx += 1
lowerCamelCase__ = state_dict['cls.predictions.decoder.weight']
lowerCamelCase__ = state_dict['cls.predictions.bias']
if args.vocab_transform:
for w in ["weight", "bias"]:
lowerCamelCase__ = state_dict[F'''cls.predictions.transform.dense.{w}''']
lowerCamelCase__ = state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']
print(F'''N layers selected for distillation: {std_idx}''')
print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')
print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
torch.save(compressed_sd, args.dump_checkpoint)
| 40 | 0 |
import math
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Tuple = 100 ):
"""simple docstring"""
__a = sum(i * i for i in range(1 , n + 1 ) )
__a = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(F"""{solution() = }""")
| 225 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
@require_tf
class _A :
'''simple docstring'''
_snake_case : int = BlenderbotSmallConfig
_snake_case : Optional[int] = {}
_snake_case : int = """gelu"""
def __init__( self : int , lowerCamelCase : Any , lowerCamelCase : Union[str, Any]=13 , lowerCamelCase : Union[str, Any]=7 , lowerCamelCase : Dict=True , lowerCamelCase : Tuple=False , lowerCamelCase : Union[str, Any]=99 , lowerCamelCase : List[Any]=32 , lowerCamelCase : int=2 , lowerCamelCase : List[Any]=4 , lowerCamelCase : List[Any]=37 , lowerCamelCase : List[str]=0.1 , lowerCamelCase : Dict=0.1 , lowerCamelCase : Union[str, Any]=20 , lowerCamelCase : Dict=2 , lowerCamelCase : int=1 , lowerCamelCase : Tuple=0 , ):
'''simple docstring'''
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = eos_token_id
__lowercase = pad_token_id
__lowercase = bos_token_id
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__lowercase = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__lowercase = tf.concat([input_ids, eos_tensor] , axis=1 )
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
__lowercase = prepare_blenderbot_small_inputs_dict(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return config, inputs_dict
def _snake_case ( self : Union[str, Any] , lowerCamelCase : List[Any] , lowerCamelCase : List[Any] ):
'''simple docstring'''
__lowercase = TFBlenderbotSmallModel(config=lowerCamelCase ).get_decoder()
__lowercase = inputs_dict["input_ids"]
__lowercase = input_ids[:1, :]
__lowercase = inputs_dict["attention_mask"][:1, :]
__lowercase = inputs_dict["head_mask"]
__lowercase = 1
# first forward pass
__lowercase = model(lowerCamelCase , attention_mask=lowerCamelCase , head_mask=lowerCamelCase , use_cache=lowerCamelCase )
__lowercase , __lowercase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__lowercase = ids_tensor((self.batch_size, 3) , config.vocab_size )
__lowercase = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__lowercase = tf.concat([input_ids, next_tokens] , axis=-1 )
__lowercase = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__lowercase = model(lowerCamelCase , attention_mask=lowerCamelCase )[0]
__lowercase = model(lowerCamelCase , attention_mask=lowerCamelCase , past_key_values=lowerCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__lowercase = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__lowercase = output_from_no_past[:, -3:, random_slice_idx]
__lowercase = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowerCamelCase , lowerCamelCase , rtol=1e-3 )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ):
if attention_mask is None:
__lowercase = tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__lowercase = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__lowercase = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__lowercase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__lowercase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class _A ( _lowercase , _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : str = (
(TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
)
_snake_case : List[str] = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
_snake_case : int = (
{
"""conversational""": TFBlenderbotSmallForConditionalGeneration,
"""feature-extraction""": TFBlenderbotSmallModel,
"""summarization""": TFBlenderbotSmallForConditionalGeneration,
"""text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
"""translation""": TFBlenderbotSmallForConditionalGeneration,
}
if is_tf_available()
else {}
)
_snake_case : Optional[int] = True
_snake_case : List[str] = False
_snake_case : Optional[Any] = False
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = TFBlenderbotSmallModelTester(self )
__lowercase = ConfigTester(self , config_class=lowerCamelCase )
def _snake_case ( self : Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase )
@require_tokenizers
@require_tf
class _A ( unittest.TestCase ):
'''simple docstring'''
_snake_case : Optional[int] = [
"""Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
""" i'm going to throw up.\nand why is that?"""
]
_snake_case : str = """facebook/blenderbot_small-90M"""
@cached_property
def _snake_case ( self : Dict ):
'''simple docstring'''
return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )
@cached_property
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowercase = self.tokenizer(self.src_text , return_tensors="tf" )
__lowercase = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=lowerCamelCase , )
__lowercase = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=lowerCamelCase )[0]
assert generated_words in (
"i don't know. i just feel like i'm going to throw up. it's not fun.",
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
"i'm not sure. i just feel like i've been in a bad situation.",
)
| 402 | 0 |
__UpperCamelCase : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1000,
"megajoule": 1000000,
"gigajoule": 1000000000,
"wattsecond": 1.0,
"watthour": 3600,
"kilowatthour": 3600000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4186800.00,
"electronvolt": 1.6_0217_6634E-19,
"britishthermalunit_it": 1_055.05_585,
"footpound": 1.355_818,
}
def snake_case_ ( __lowercase , __lowercase , __lowercase ):
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
UpperCAmelCase_ : List[Any] = (
F'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
F'''Valid values are: {', '.join(_lowerCAmelCase )}'''
)
raise ValueError(_lowerCAmelCase )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod() | 716 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowerCAmelCase__:
'''simple docstring'''
def __init__( self : List[str] , __snake_case : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ : str = data
UpperCAmelCase_ : List[Any] = [0X67_45_23_01, 0Xef_cd_ab_89, 0X98_ba_dc_fe, 0X10_32_54_76, 0Xc3_d2_e1_f0]
@staticmethod
def _lowerCamelCase ( __snake_case : Dict , __snake_case : Dict ):
'''simple docstring'''
return ((n << b) | (n >> (32 - b))) & 0Xff_ff_ff_ff
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = B'''\x80''' + B'''\x00''' * (63 - (len(self.data ) + 8) % 64)
UpperCAmelCase_ : Union[str, Any] = self.data + padding + struct.pack('''>Q''' , 8 * len(self.data ) )
return padded_data
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
return [
self.padded_data[i : i + 64] for i in range(0 , len(self.padded_data ) , 64 )
]
def _lowerCamelCase ( self : Dict , __snake_case : Optional[int] ):
'''simple docstring'''
UpperCAmelCase_ : Any = list(struct.unpack('''>16L''' , __snake_case ) ) + [0] * 64
for i in range(16 , 80 ):
UpperCAmelCase_ : str = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) , 1 )
return w
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = self.padding()
UpperCAmelCase_ : str = self.split_blocks()
for block in self.blocks:
UpperCAmelCase_ : Any = self.expand_block(__snake_case )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : List[str] = self.h
for i in range(0 , 80 ):
if 0 <= i < 20:
UpperCAmelCase_ : Optional[Any] = (b & c) | ((~b) & d)
UpperCAmelCase_ : Optional[Any] = 0X5a_82_79_99
elif 20 <= i < 40:
UpperCAmelCase_ : List[Any] = b ^ c ^ d
UpperCAmelCase_ : str = 0X6e_d9_eb_a1
elif 40 <= i < 60:
UpperCAmelCase_ : str = (b & c) | (b & d) | (c & d)
UpperCAmelCase_ : Optional[int] = 0X8f_1b_bc_dc
elif 60 <= i < 80:
UpperCAmelCase_ : Union[str, Any] = b ^ c ^ d
UpperCAmelCase_ : Dict = 0Xca_62_c1_d6
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : int = (
self.rotate(__snake_case , 5 ) + f + e + k + expanded_block[i] & 0Xff_ff_ff_ff,
a,
self.rotate(__snake_case , 30 ),
c,
d,
)
UpperCAmelCase_ : Optional[Any] = (
self.h[0] + a & 0Xff_ff_ff_ff,
self.h[1] + b & 0Xff_ff_ff_ff,
self.h[2] + c & 0Xff_ff_ff_ff,
self.h[3] + d & 0Xff_ff_ff_ff,
self.h[4] + e & 0Xff_ff_ff_ff,
)
return ("{:08x}" * 5).format(*self.h )
def snake_case_ ( ):
UpperCAmelCase_ : Tuple = B'''Test String'''
assert SHAaHash(__lowercase ).final_hash() == hashlib.shaa(__lowercase ).hexdigest() # noqa: S324
def snake_case_ ( ):
UpperCAmelCase_ : int = argparse.ArgumentParser(description='''Process some strings or files''' )
parser.add_argument(
'''--string''' , dest='''input_string''' , default='''Hello World!! Welcome to Cryptography''' , help='''Hash the string''' , )
parser.add_argument('''--file''' , dest='''input_file''' , help='''Hash contents of a file''' )
UpperCAmelCase_ : List[Any] = parser.parse_args()
UpperCAmelCase_ : Optional[Any] = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file , '''rb''' ) as f:
UpperCAmelCase_ : List[str] = f.read()
else:
UpperCAmelCase_ : Tuple = bytes(__lowercase , '''utf-8''' )
print(SHAaHash(__lowercase ).final_hash() )
if __name__ == "__main__":
main()
import doctest
doctest.testmod() | 641 | 0 |
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
lowerCamelCase__ : Dict = logging.get_logger(__name__)
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = b.T
SCREAMING_SNAKE_CASE_ = np.sum(np.square(__UpperCAmelCase ) , axis=1 )
SCREAMING_SNAKE_CASE_ = np.sum(np.square(__UpperCAmelCase ) , axis=0 )
SCREAMING_SNAKE_CASE_ = np.matmul(__UpperCAmelCase , __UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = aa[:, None] - 2 * ab + ba[None, :]
return d
def UpperCAmelCase_ ( __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = x.reshape(-1 , 3 )
SCREAMING_SNAKE_CASE_ = squared_euclidean_distance(__UpperCAmelCase , __UpperCAmelCase )
return np.argmin(__UpperCAmelCase , axis=1 )
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["pixel_values"]
def __init__( self : List[Any] , _lowerCAmelCase : Optional[Union[List[List[int]], np.ndarray]] = None , _lowerCAmelCase : bool = True , _lowerCAmelCase : Dict[str, int] = None , _lowerCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , _lowerCAmelCase : bool = True , _lowerCAmelCase : bool = True , **_lowerCAmelCase : List[Any] , ):
super().__init__(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = size if size is not None else {'height': 256, 'width': 256}
SCREAMING_SNAKE_CASE_ = get_size_dict(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = np.array(_lowerCAmelCase ) if clusters is not None else None
SCREAMING_SNAKE_CASE_ = do_resize
SCREAMING_SNAKE_CASE_ = size
SCREAMING_SNAKE_CASE_ = resample
SCREAMING_SNAKE_CASE_ = do_normalize
SCREAMING_SNAKE_CASE_ = do_color_quantize
def lowerCAmelCase_ ( self : str , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : Dict[str, int] , _lowerCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , _lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_lowerCAmelCase : Tuple , ):
SCREAMING_SNAKE_CASE_ = get_size_dict(_lowerCAmelCase )
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(
_lowerCAmelCase , size=(size['height'], size['width']) , resample=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , ):
SCREAMING_SNAKE_CASE_ = rescale(image=_lowerCAmelCase , scale=1 / 127.5 , data_format=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = image - 1
return image
def lowerCAmelCase_ ( self : List[Any] , _lowerCAmelCase : ImageInput , _lowerCAmelCase : bool = None , _lowerCAmelCase : Dict[str, int] = None , _lowerCAmelCase : PILImageResampling = None , _lowerCAmelCase : bool = None , _lowerCAmelCase : Optional[bool] = None , _lowerCAmelCase : Optional[Union[List[List[int]], np.ndarray]] = None , _lowerCAmelCase : Optional[Union[str, TensorType]] = None , _lowerCAmelCase : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST , **_lowerCAmelCase : Optional[Any] , ):
SCREAMING_SNAKE_CASE_ = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE_ = size if size is not None else self.size
SCREAMING_SNAKE_CASE_ = get_size_dict(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE_ = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE_ = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
SCREAMING_SNAKE_CASE_ = clusters if clusters is not None else self.clusters
SCREAMING_SNAKE_CASE_ = np.array(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = make_list_of_images(_lowerCAmelCase )
if not valid_images(_lowerCAmelCase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None 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.
SCREAMING_SNAKE_CASE_ = [to_numpy_array(_lowerCAmelCase ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE_ = [self.resize(image=_lowerCAmelCase , size=_lowerCAmelCase , resample=_lowerCAmelCase ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE_ = [self.normalize(image=_lowerCAmelCase ) for image in images]
if do_color_quantize:
SCREAMING_SNAKE_CASE_ = [to_channel_dimension_format(_lowerCAmelCase , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
SCREAMING_SNAKE_CASE_ = np.array(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = color_quantize(_lowerCAmelCase , _lowerCAmelCase ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
SCREAMING_SNAKE_CASE_ = images.shape[0]
SCREAMING_SNAKE_CASE_ = images.reshape(_lowerCAmelCase , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
SCREAMING_SNAKE_CASE_ = list(_lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE_ = [to_channel_dimension_format(_lowerCAmelCase , _lowerCAmelCase ) for image in images]
SCREAMING_SNAKE_CASE_ = {'input_ids': images}
return BatchFeature(data=_lowerCAmelCase , tensor_type=_lowerCAmelCase ) | 31 |
"""simple docstring"""
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __get__( self : List[Any] ,lowercase_ : Any ,lowercase_ : List[str]=None ):
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError('''unreadable attribute''' )
lowerCAmelCase__ : Optional[Any] = '''__cached_''' + self.fget.__name__
lowerCAmelCase__ : Any = getattr(lowercase_ ,lowercase_ ,lowercase_ )
if cached is None:
lowerCAmelCase__ : str = self.fget(lowercase_ )
setattr(lowercase_ ,lowercase_ ,lowercase_ )
return cached
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : int = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'invalid truth value {val!r}' )
def __SCREAMING_SNAKE_CASE ( A_ ):
if is_torch_fx_proxy(A_ ):
return True
if is_torch_available():
import torch
if isinstance(A_ , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(A_ , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(A_ , (jnp.ndarray, Tracer) ):
return True
return isinstance(A_ , np.ndarray )
def __SCREAMING_SNAKE_CASE ( A_ ):
return isinstance(A_ , np.ndarray )
def __SCREAMING_SNAKE_CASE ( A_ ):
return _is_numpy(A_ )
def __SCREAMING_SNAKE_CASE ( A_ ):
import torch
return isinstance(A_ , torch.Tensor )
def __SCREAMING_SNAKE_CASE ( A_ ):
return False if not is_torch_available() else _is_torch(A_ )
def __SCREAMING_SNAKE_CASE ( A_ ):
import torch
return isinstance(A_ , torch.device )
def __SCREAMING_SNAKE_CASE ( A_ ):
return False if not is_torch_available() else _is_torch_device(A_ )
def __SCREAMING_SNAKE_CASE ( A_ ):
import torch
if isinstance(A_ , A_ ):
if hasattr(A_ , A_ ):
lowerCAmelCase__ : int = getattr(A_ , A_ )
else:
return False
return isinstance(A_ , torch.dtype )
def __SCREAMING_SNAKE_CASE ( A_ ):
return False if not is_torch_available() else _is_torch_dtype(A_ )
def __SCREAMING_SNAKE_CASE ( A_ ):
import tensorflow as tf
return isinstance(A_ , tf.Tensor )
def __SCREAMING_SNAKE_CASE ( A_ ):
return False if not is_tf_available() else _is_tensorflow(A_ )
def __SCREAMING_SNAKE_CASE ( A_ ):
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(A_ , '''is_symbolic_tensor''' ):
return tf.is_symbolic_tensor(A_ )
return type(A_ ) == tf.Tensor
def __SCREAMING_SNAKE_CASE ( A_ ):
return False if not is_tf_available() else _is_tf_symbolic_tensor(A_ )
def __SCREAMING_SNAKE_CASE ( A_ ):
import jax.numpy as jnp # noqa: F811
return isinstance(A_ , jnp.ndarray )
def __SCREAMING_SNAKE_CASE ( A_ ):
return False if not is_flax_available() else _is_jax(A_ )
def __SCREAMING_SNAKE_CASE ( A_ ):
if isinstance(A_ , (dict, UserDict) ):
return {k: to_py_obj(A_ ) for k, v in obj.items()}
elif isinstance(A_ , (list, tuple) ):
return [to_py_obj(A_ ) for o in obj]
elif is_tf_tensor(A_ ):
return obj.numpy().tolist()
elif is_torch_tensor(A_ ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(A_ ):
return np.asarray(A_ ).tolist()
elif isinstance(A_ , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def __SCREAMING_SNAKE_CASE ( A_ ):
if isinstance(A_ , (dict, UserDict) ):
return {k: to_numpy(A_ ) for k, v in obj.items()}
elif isinstance(A_ , (list, tuple) ):
return np.array(A_ )
elif is_tf_tensor(A_ ):
return obj.numpy()
elif is_torch_tensor(A_ ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(A_ ):
return np.asarray(A_ )
else:
return obj
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __lowerCAmelCase ( self : Any ):
lowerCAmelCase__ : Optional[int] = fields(self )
# Safety and consistency checks
if not len(lowercase_ ):
raise ValueError(F'{self.__class__.__name__} has no fields.' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F'{self.__class__.__name__} should not have more than one required field.' )
lowerCAmelCase__ : str = getattr(self ,class_fields[0].name )
lowerCAmelCase__ : List[str] = all(getattr(self ,field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(lowercase_ ):
if isinstance(lowercase_ ,lowercase_ ):
lowerCAmelCase__ : str = first_field.items()
lowerCAmelCase__ : List[str] = True
else:
try:
lowerCAmelCase__ : Union[str, Any] = iter(lowercase_ )
lowerCAmelCase__ : int = True
except TypeError:
lowerCAmelCase__ : Dict = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(lowercase_ ):
if (
not isinstance(lowercase_ ,(list, tuple) )
or not len(lowercase_ ) == 2
or not isinstance(element[0] ,lowercase_ )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
lowerCAmelCase__ : Tuple = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F'Cannot set key/value for {element}. It needs to be a tuple (key, value).' )
break
setattr(self ,element[0] ,element[1] )
if element[1] is not None:
lowerCAmelCase__ : Dict = element[1]
elif first_field is not None:
lowerCAmelCase__ : Any = first_field
else:
for field in class_fields:
lowerCAmelCase__ : Any = getattr(self ,field.name )
if v is not None:
lowerCAmelCase__ : List[str] = v
def __delitem__( self : List[str] ,*lowercase_ : List[str] ,**lowercase_ : Any ):
raise Exception(F'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.' )
def __lowerCAmelCase ( self : Optional[int] ,*lowercase_ : Union[str, Any] ,**lowercase_ : List[Any] ):
raise Exception(F'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.' )
def __lowerCAmelCase ( self : str ,*lowercase_ : Union[str, Any] ,**lowercase_ : Any ):
raise Exception(F'You cannot use ``pop`` on a {self.__class__.__name__} instance.' )
def __lowerCAmelCase ( self : int ,*lowercase_ : List[str] ,**lowercase_ : int ):
raise Exception(F'You cannot use ``update`` on a {self.__class__.__name__} instance.' )
def __getitem__( self : Any ,lowercase_ : Any ):
if isinstance(lowercase_ ,lowercase_ ):
lowerCAmelCase__ : Optional[Any] = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : Dict ,lowercase_ : Dict ,lowercase_ : int ):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(lowercase_ ,lowercase_ )
super().__setattr__(lowercase_ ,lowercase_ )
def __setitem__( self : str ,lowercase_ : Optional[int] ,lowercase_ : Optional[Any] ):
# Will raise a KeyException if needed
super().__setitem__(lowercase_ ,lowercase_ )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(lowercase_ ,lowercase_ )
def __lowerCAmelCase ( self : Optional[int] ):
return tuple(self[k] for k in self.keys() )
class SCREAMING_SNAKE_CASE ( a_ , a_ ):
"""simple docstring"""
@classmethod
def __lowerCAmelCase ( cls : Dict ,lowercase_ : Optional[Any] ):
raise ValueError(
F'{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}' )
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowercase__ = "longest"
lowercase__ = "max_length"
lowercase__ = "do_not_pad"
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowercase__ = "pt"
lowercase__ = "tf"
lowercase__ = "np"
lowercase__ = "jax"
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : List[Any] ,lowercase_ : List[ContextManager] ):
lowerCAmelCase__ : Optional[int] = context_managers
lowerCAmelCase__ : Tuple = ExitStack()
def __enter__( self : str ):
for context_manager in self.context_managers:
self.stack.enter_context(lowercase_ )
def __exit__( self : Tuple ,*lowercase_ : Tuple ,**lowercase_ : List[Any] ):
self.stack.__exit__(*lowercase_ ,**lowercase_ )
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Union[str, Any] = infer_framework(A_ )
if framework == "tf":
lowerCAmelCase__ : List[str] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
lowerCAmelCase__ : Any = inspect.signature(model_class.forward ) # PyTorch models
else:
lowerCAmelCase__ : Dict = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : List[str] = model_class.__name__
lowerCAmelCase__ : List[Any] = infer_framework(A_ )
if framework == "tf":
lowerCAmelCase__ : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
lowerCAmelCase__ : List[str] = inspect.signature(model_class.forward ) # PyTorch models
else:
lowerCAmelCase__ : Optional[int] = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def __SCREAMING_SNAKE_CASE ( A_ , A_ = "" , A_ = "." ):
def _flatten_dict(A_ , A_="" , A_="." ):
for k, v in d.items():
lowerCAmelCase__ : Any = str(A_ ) + delimiter + str(A_ ) if parent_key else k
if v and isinstance(A_ , A_ ):
yield from flatten_dict(A_ , A_ , delimiter=A_ ).items()
else:
yield key, v
return dict(_flatten_dict(A_ , A_ , A_ ) )
@contextmanager
def __SCREAMING_SNAKE_CASE ( A_ , A_ = False ):
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def __SCREAMING_SNAKE_CASE ( A_ , A_=None ):
if is_numpy_array(A_ ):
return np.transpose(A_ , axes=A_ )
elif is_torch_tensor(A_ ):
return array.T if axes is None else array.permute(*A_ )
elif is_tf_tensor(A_ ):
import tensorflow as tf
return tf.transpose(A_ , perm=A_ )
elif is_jax_tensor(A_ ):
return jnp.transpose(A_ , axes=A_ )
else:
raise ValueError(f'Type not supported for transpose: {type(A_ )}.' )
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
if is_numpy_array(A_ ):
return np.reshape(A_ , A_ )
elif is_torch_tensor(A_ ):
return array.reshape(*A_ )
elif is_tf_tensor(A_ ):
import tensorflow as tf
return tf.reshape(A_ , A_ )
elif is_jax_tensor(A_ ):
return jnp.reshape(A_ , A_ )
else:
raise ValueError(f'Type not supported for reshape: {type(A_ )}.' )
def __SCREAMING_SNAKE_CASE ( A_ , A_=None ):
if is_numpy_array(A_ ):
return np.squeeze(A_ , axis=A_ )
elif is_torch_tensor(A_ ):
return array.squeeze() if axis is None else array.squeeze(dim=A_ )
elif is_tf_tensor(A_ ):
import tensorflow as tf
return tf.squeeze(A_ , axis=A_ )
elif is_jax_tensor(A_ ):
return jnp.squeeze(A_ , axis=A_ )
else:
raise ValueError(f'Type not supported for squeeze: {type(A_ )}.' )
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
if is_numpy_array(A_ ):
return np.expand_dims(A_ , A_ )
elif is_torch_tensor(A_ ):
return array.unsqueeze(dim=A_ )
elif is_tf_tensor(A_ ):
import tensorflow as tf
return tf.expand_dims(A_ , axis=A_ )
elif is_jax_tensor(A_ ):
return jnp.expand_dims(A_ , axis=A_ )
else:
raise ValueError(f'Type not supported for expand_dims: {type(A_ )}.' )
def __SCREAMING_SNAKE_CASE ( A_ ):
if is_numpy_array(A_ ):
return np.size(A_ )
elif is_torch_tensor(A_ ):
return array.numel()
elif is_tf_tensor(A_ ):
import tensorflow as tf
return tf.size(A_ )
elif is_jax_tensor(A_ ):
return array.size
else:
raise ValueError(f'Type not supported for expand_dims: {type(A_ )}.' )
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
for key, value in auto_map.items():
if isinstance(A_ , (tuple, list) ):
lowerCAmelCase__ : Tuple = [f'{repo_id}--{v}' if (v is not None and '''--''' not in v) else v for v in value]
elif value is not None and "--" not in value:
lowerCAmelCase__ : Tuple = f'{repo_id}--{value}'
return auto_map
def __SCREAMING_SNAKE_CASE ( A_ ):
for base_class in inspect.getmro(A_ ):
lowerCAmelCase__ : List[str] = base_class.__module__
lowerCAmelCase__ : List[Any] = base_class.__name__
if module.startswith('''tensorflow''' ) or module.startswith('''keras''' ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith('''torch''' ) or name == "PreTrainedModel":
return "pt"
elif module.startswith('''flax''' ) or module.startswith('''jax''' ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'Could not infer framework from class {model_class}.' )
| 450 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : int = logging.get_logger(__name__)
_a : int = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class UpperCamelCase_ ( __UpperCamelCase ):
"""simple docstring"""
A = '''timesformer'''
def __init__( self , UpperCAmelCase=2_2_4 , UpperCAmelCase=1_6 , UpperCAmelCase=3 , UpperCAmelCase=8 , UpperCAmelCase=7_6_8 , UpperCAmelCase=1_2 , UpperCAmelCase=1_2 , UpperCAmelCase=3_0_7_2 , UpperCAmelCase="gelu" , UpperCAmelCase=0.0 , UpperCAmelCase=0.0 , UpperCAmelCase=0.02 , UpperCAmelCase=1E-6 , UpperCAmelCase=True , UpperCAmelCase="divided_space_time" , UpperCAmelCase=0 , **UpperCAmelCase , ):
super().__init__(**UpperCAmelCase )
__lowerCamelCase = image_size
__lowerCamelCase = patch_size
__lowerCamelCase = num_channels
__lowerCamelCase = num_frames
__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 = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = qkv_bias
__lowerCamelCase = attention_type
__lowerCamelCase = drop_path_rate
| 571 |
from ...processing_utils import ProcessorMixin
class UpperCamelCase_ ( __UpperCamelCase ):
"""simple docstring"""
A = ['''image_processor''', '''feature_extractor''']
A = '''TvltImageProcessor'''
A = '''TvltFeatureExtractor'''
def __init__( self , UpperCAmelCase , UpperCAmelCase ):
super().__init__(image_processor=UpperCAmelCase , feature_extractor=UpperCAmelCase )
__lowerCamelCase = image_processor
__lowerCamelCase = feature_extractor
def __call__( self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=False , UpperCAmelCase=False , *UpperCAmelCase , **UpperCAmelCase , ):
if images is None and audio is None:
raise ValueError("""You need to specify either an `images` or `audio` input to process.""" )
__lowerCamelCase = None
if images is not None:
__lowerCamelCase = self.image_processor(UpperCAmelCase , mask_pixel=UpperCAmelCase , *UpperCAmelCase , **UpperCAmelCase )
if images_mixed is not None:
__lowerCamelCase = self.image_processor(UpperCAmelCase , is_mixed=UpperCAmelCase , *UpperCAmelCase , **UpperCAmelCase )
if audio is not None:
__lowerCamelCase = self.feature_extractor(
UpperCAmelCase , *UpperCAmelCase , sampling_rate=UpperCAmelCase , mask_audio=UpperCAmelCase , **UpperCAmelCase )
__lowerCamelCase = {}
if audio is not None:
output_dict.update(UpperCAmelCase )
if images is not None:
output_dict.update(UpperCAmelCase )
if images_mixed_dict is not None:
output_dict.update(UpperCAmelCase )
return output_dict
@property
def lowerCamelCase_ ( self ):
__lowerCamelCase = self.image_processor.model_input_names
__lowerCamelCase = self.feature_extractor.model_input_names
return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names ) )
| 571 | 1 |
import os
SCREAMING_SNAKE_CASE__ = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCAmelCase__ ( lowerCamelCase_ : str ):
__a : Optional[Any] = 0
__a : Dict = 0
while index < len(lowerCamelCase_ ) - 1:
__a : Optional[Any] = SYMBOLS[numerals[index]]
__a : Union[str, Any] = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def UpperCAmelCase__ ( lowerCamelCase_ : int ):
__a : Optional[Any] = ''
__a : int = num // 1_0_0_0
numerals += m_count * "M"
num %= 1_0_0_0
__a : Tuple = num // 1_0_0
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_0_0
__a : Optional[int] = num // 1_0
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 1_0
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def UpperCAmelCase__ ( lowerCamelCase_ : str = "/p089_roman.txt" ):
__a : List[Any] = 0
with open(os.path.dirname(lowerCamelCase_ ) + roman_numerals_filename ) as filea:
__a : Tuple = filea.readlines()
for line in lines:
__a : str = line.strip()
__a : Dict = parse_roman_numerals(lowerCamelCase_ )
__a : str = generate_roman_numerals(lowerCamelCase_ )
savings += len(lowerCamelCase_ ) - len(lowerCamelCase_ )
return savings
if __name__ == "__main__":
print(F"{solution() = }")
| 47 |
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'{price_plus_tax(100, 0.25) = }')
print(F'{price_plus_tax(125.50, 0.05) = }')
| 509 | 0 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_snake_case : Union[str, Any] = get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
_snake_case : List[Any] = json.load(f)
@require_torch
class lowerCAmelCase ( unittest.TestCase ):
def lowercase ( self , UpperCamelCase ):
return FSMTTokenizer.from_pretrained(UpperCamelCase )
def lowercase ( self , UpperCamelCase ):
_SCREAMING_SNAKE_CASE = FSMTForConditionalGeneration.from_pretrained(UpperCamelCase ).to(UpperCamelCase )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 26.0],
["ru-en", 22.0],
["en-de", 22.0],
["de-en", 29.0],
] )
@slow
def lowercase ( self , UpperCamelCase , UpperCamelCase ):
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
_SCREAMING_SNAKE_CASE = F'facebook/wmt19-{pair}'
_SCREAMING_SNAKE_CASE = self.get_tokenizer(UpperCamelCase )
_SCREAMING_SNAKE_CASE = self.get_model(UpperCamelCase )
_SCREAMING_SNAKE_CASE = bleu_data[pair]["src"]
_SCREAMING_SNAKE_CASE = bleu_data[pair]["tgt"]
_SCREAMING_SNAKE_CASE = tokenizer(UpperCamelCase , return_tensors="pt" , truncation=UpperCamelCase , padding="longest" ).to(UpperCamelCase )
_SCREAMING_SNAKE_CASE = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
_SCREAMING_SNAKE_CASE = tokenizer.batch_decode(
UpperCamelCase , skip_special_tokens=UpperCamelCase , clean_up_tokenization_spaces=UpperCamelCase )
_SCREAMING_SNAKE_CASE = calculate_bleu(UpperCamelCase , UpperCamelCase )
print(UpperCamelCase )
self.assertGreaterEqual(scores["bleu"] , UpperCamelCase ) | 493 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCAmelCase ( __UpperCAmelCase , unittest.TestCase ):
a : int = LayoutLMTokenizer
a : Optional[int] = LayoutLMTokenizerFast
a : Optional[int] = True
a : Any = True
def lowercase ( self ):
super().setUp()
_SCREAMING_SNAKE_CASE = [
"[UNK]",
"[CLS]",
"[SEP]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def lowercase ( self , **UpperCamelCase ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase )
def lowercase ( self , UpperCamelCase ):
_SCREAMING_SNAKE_CASE = "UNwant\u00E9d,running"
_SCREAMING_SNAKE_CASE = "unwanted, running"
return input_text, output_text
def lowercase ( self ):
_SCREAMING_SNAKE_CASE = self.tokenizer_class(self.vocab_file )
_SCREAMING_SNAKE_CASE = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(UpperCamelCase , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase ) , [7, 4, 5, 10, 8, 9] )
def lowercase ( self ):
pass | 493 | 1 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class UpperCamelCase_ ( unittest.TestCase ):
def __init__( self : Dict , lowerCamelCase : Union[str, Any] , lowerCamelCase : Dict=13 , lowerCamelCase : Optional[Any]=7 , lowerCamelCase : int=True , lowerCamelCase : str=True , lowerCamelCase : int=True , lowerCamelCase : Optional[int]=True , lowerCamelCase : Optional[int]=99 , lowerCamelCase : List[Any]=32 , lowerCamelCase : Any=5 , lowerCamelCase : Dict=4 , lowerCamelCase : int=37 , lowerCamelCase : int="gelu" , lowerCamelCase : Tuple=0.1 , lowerCamelCase : List[str]=0.1 , lowerCamelCase : Optional[int]=5_12 , lowerCamelCase : str=16 , lowerCamelCase : List[str]=2 , lowerCamelCase : Dict=0.02 , lowerCamelCase : int=4 , ):
lowerCamelCase_ : Tuple = parent
lowerCamelCase_ : int = batch_size
lowerCamelCase_ : Any = seq_length
lowerCamelCase_ : Optional[Any] = is_training
lowerCamelCase_ : List[str] = use_attention_mask
lowerCamelCase_ : Any = use_token_type_ids
lowerCamelCase_ : Any = use_labels
lowerCamelCase_ : Tuple = vocab_size
lowerCamelCase_ : Union[str, Any] = hidden_size
lowerCamelCase_ : str = num_hidden_layers
lowerCamelCase_ : Dict = num_attention_heads
lowerCamelCase_ : int = intermediate_size
lowerCamelCase_ : str = hidden_act
lowerCamelCase_ : Dict = hidden_dropout_prob
lowerCamelCase_ : Optional[int] = attention_probs_dropout_prob
lowerCamelCase_ : str = max_position_embeddings
lowerCamelCase_ : Optional[Any] = type_vocab_size
lowerCamelCase_ : Optional[Any] = type_sequence_label_size
lowerCamelCase_ : str = initializer_range
lowerCamelCase_ : List[Any] = num_choices
def __a ( self : Union[str, Any] ):
lowerCamelCase_ : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ : Any = None
if self.use_attention_mask:
lowerCamelCase_ : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ : Tuple = None
if self.use_token_type_ids:
lowerCamelCase_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCamelCase_ : str = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=a__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def __a ( self : int ):
lowerCamelCase_ : str = self.prepare_config_and_inputs()
lowerCamelCase_ : Optional[int] = config_and_inputs
lowerCamelCase_ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def __a ( self : List[str] ):
lowerCamelCase_ : Union[str, Any] = self.prepare_config_and_inputs()
lowerCamelCase_ : int = config_and_inputs
lowerCamelCase_ : Optional[Any] = True
lowerCamelCase_ : List[Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowerCamelCase_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class UpperCamelCase_ ( lowercase_ , unittest.TestCase ):
_a : Tuple = True
_a : Optional[int] = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __a ( self : Optional[int] ):
lowerCamelCase_ : Dict = FlaxRobertaPreLayerNormModelTester(self )
@slow
def __a ( self : List[Any] ):
for model_class_name in self.all_model_classes:
lowerCamelCase_ : List[str] = model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=a__ )
lowerCamelCase_ : Union[str, Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(a__ )
@require_flax
class UpperCamelCase_ ( unittest.TestCase ):
@slow
def __a ( self : int ):
lowerCamelCase_ : Dict = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=a__ )
lowerCamelCase_ : List[Any] = np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] , dtype=jnp.intaa )
lowerCamelCase_ : int = model(a__ )[0]
lowerCamelCase_ : str = [1, 11, 5_02_65]
self.assertEqual(list(output.shape ) , a__ )
# compare the actual values for a slice.
lowerCamelCase_ : Optional[Any] = np.array(
[[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , a__ , atol=1E-4 ) )
@slow
def __a ( self : Union[str, Any] ):
lowerCamelCase_ : List[Any] = FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=a__ )
lowerCamelCase_ : Tuple = np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] , dtype=jnp.intaa )
lowerCamelCase_ : Any = model(a__ )[0]
# compare the actual values for a slice.
lowerCamelCase_ : str = np.array(
[[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , a__ , atol=1E-4 ) )
| 364 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
lowerCamelCase__ = logging.get_logger(__name__)
# General docstring
lowerCamelCase__ = """ResNetConfig"""
# Base docstring
lowerCamelCase__ = """microsoft/resnet-50"""
lowerCamelCase__ = [1, 2048, 7, 7]
# Image classification docstring
lowerCamelCase__ = """microsoft/resnet-50"""
lowerCamelCase__ = """tiger cat"""
lowerCamelCase__ = [
"""microsoft/resnet-50""",
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class snake_case__ ( nn.Module):
'''simple docstring'''
def __init__( self , a__ , a__ , a__ = 3 , a__ = 1 , a__ = "relu" ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
__snake_case :Dict = nn.Convad(
a__ , a__ , kernel_size=a__ , stride=a__ , padding=kernel_size // 2 , bias=a__ )
__snake_case :str = nn.BatchNormad(a__ )
__snake_case :List[Any] = ACTaFN[activation] if activation is not None else nn.Identity()
def __lowercase ( self , a__ ) -> Tensor:
'''simple docstring'''
__snake_case :int = self.convolution(a__ )
__snake_case :Any = self.normalization(a__ )
__snake_case :Optional[int] = self.activation(a__ )
return hidden_state
class snake_case__ ( nn.Module):
'''simple docstring'''
def __init__( self , a__ ) -> str:
'''simple docstring'''
super().__init__()
__snake_case :Dict = ResNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=7 , stride=2 , activation=config.hidden_act )
__snake_case :List[Any] = nn.MaxPoolad(kernel_size=3 , stride=2 , padding=1 )
__snake_case :Tuple = config.num_channels
def __lowercase ( self , a__ ) -> Tensor:
'''simple docstring'''
__snake_case :Optional[Any] = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"""Make sure that the channel dimension of the pixel values match with the one set in the configuration.""" )
__snake_case :Optional[int] = self.embedder(a__ )
__snake_case :int = self.pooler(a__ )
return embedding
class snake_case__ ( nn.Module):
'''simple docstring'''
def __init__( self , a__ , a__ , a__ = 2 ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
__snake_case :Optional[Any] = nn.Convad(a__ , a__ , kernel_size=1 , stride=a__ , bias=a__ )
__snake_case :Tuple = nn.BatchNormad(a__ )
def __lowercase ( self , a__ ) -> Tensor:
'''simple docstring'''
__snake_case :Any = self.convolution(a__ )
__snake_case :str = self.normalization(a__ )
return hidden_state
class snake_case__ ( nn.Module):
'''simple docstring'''
def __init__( self , a__ , a__ , a__ = 1 , a__ = "relu" ) -> List[str]:
'''simple docstring'''
super().__init__()
__snake_case :int = in_channels != out_channels or stride != 1
__snake_case :Tuple = (
ResNetShortCut(a__ , a__ , stride=a__ ) if should_apply_shortcut else nn.Identity()
)
__snake_case :Optional[int] = nn.Sequential(
ResNetConvLayer(a__ , a__ , stride=a__ ) , ResNetConvLayer(a__ , a__ , activation=a__ ) , )
__snake_case :Union[str, Any] = ACTaFN[activation]
def __lowercase ( self , a__ ) -> Union[str, Any]:
'''simple docstring'''
__snake_case :int = hidden_state
__snake_case :Dict = self.layer(a__ )
__snake_case :Any = self.shortcut(a__ )
hidden_state += residual
__snake_case :List[Any] = self.activation(a__ )
return hidden_state
class snake_case__ ( nn.Module):
'''simple docstring'''
def __init__( self , a__ , a__ , a__ = 1 , a__ = "relu" , a__ = 4 ) -> List[Any]:
'''simple docstring'''
super().__init__()
__snake_case :Optional[int] = in_channels != out_channels or stride != 1
__snake_case :List[Any] = out_channels // reduction
__snake_case :List[str] = (
ResNetShortCut(a__ , a__ , stride=a__ ) if should_apply_shortcut else nn.Identity()
)
__snake_case :int = nn.Sequential(
ResNetConvLayer(a__ , a__ , kernel_size=1 ) , ResNetConvLayer(a__ , a__ , stride=a__ ) , ResNetConvLayer(a__ , a__ , kernel_size=1 , activation=a__ ) , )
__snake_case :Dict = ACTaFN[activation]
def __lowercase ( self , a__ ) -> Any:
'''simple docstring'''
__snake_case :List[str] = hidden_state
__snake_case :List[Any] = self.layer(a__ )
__snake_case :List[Any] = self.shortcut(a__ )
hidden_state += residual
__snake_case :Optional[Any] = self.activation(a__ )
return hidden_state
class snake_case__ ( nn.Module):
'''simple docstring'''
def __init__( self , a__ , a__ , a__ , a__ = 2 , a__ = 2 , ) -> Any:
'''simple docstring'''
super().__init__()
__snake_case :Optional[int] = ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer
__snake_case :Tuple = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(a__ , a__ , stride=a__ , activation=config.hidden_act ) , *[layer(a__ , a__ , activation=config.hidden_act ) for _ in range(depth - 1 )] , )
def __lowercase ( self , a__ ) -> Tensor:
'''simple docstring'''
__snake_case :Union[str, Any] = input
for layer in self.layers:
__snake_case :str = layer(a__ )
return hidden_state
class snake_case__ ( nn.Module):
'''simple docstring'''
def __init__( self , a__ ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
__snake_case :Optional[int] = nn.ModuleList([] )
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
a__ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
__snake_case :Tuple = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(a__ , config.depths[1:] ):
self.stages.append(ResNetStage(a__ , a__ , a__ , depth=a__ ) )
def __lowercase ( self , a__ , a__ = False , a__ = True ) -> BaseModelOutputWithNoAttention:
'''simple docstring'''
__snake_case :Dict = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
__snake_case :Optional[int] = hidden_states + (hidden_state,)
__snake_case :Tuple = stage_module(a__ )
if output_hidden_states:
__snake_case :Optional[Any] = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(
last_hidden_state=a__ , hidden_states=a__ , )
class snake_case__ ( lowercase_):
'''simple docstring'''
lowerCamelCase : List[Any] = ResNetConfig
lowerCamelCase : Optional[Any] = "resnet"
lowerCamelCase : str = "pixel_values"
lowerCamelCase : Optional[int] = True
def __lowercase ( self , a__ ) -> Dict:
'''simple docstring'''
if isinstance(a__ , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="""fan_out""" , nonlinearity="""relu""" )
elif isinstance(a__ , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def __lowercase ( self , a__ , a__=False ) -> Optional[int]:
'''simple docstring'''
if isinstance(a__ , a__ ):
__snake_case :Union[str, Any] = value
lowerCamelCase__ = R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
lowerCamelCase__ = R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ResNet model outputting raw features without any specific head on top." , lowercase_ , )
class snake_case__ ( lowercase_):
'''simple docstring'''
def __init__( self , a__ ) -> Tuple:
'''simple docstring'''
super().__init__(a__ )
__snake_case :int = config
__snake_case :Any = ResNetEmbeddings(a__ )
__snake_case :Dict = ResNetEncoder(a__ )
__snake_case :Dict = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(a__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=a__ , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __lowercase ( self , a__ , a__ = None , a__ = None ) -> BaseModelOutputWithPoolingAndNoAttention:
'''simple docstring'''
__snake_case :List[str] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__snake_case :List[str] = return_dict if return_dict is not None else self.config.use_return_dict
__snake_case :int = self.embedder(a__ )
__snake_case :Any = self.encoder(
a__ , output_hidden_states=a__ , return_dict=a__ )
__snake_case :Any = encoder_outputs[0]
__snake_case :int = self.pooler(a__ )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=a__ , pooler_output=a__ , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
"\n ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , lowercase_ , )
class snake_case__ ( lowercase_):
'''simple docstring'''
def __init__( self , a__ ) -> List[Any]:
'''simple docstring'''
super().__init__(a__ )
__snake_case :Union[str, Any] = config.num_labels
__snake_case :Optional[int] = ResNetModel(a__ )
# classification head
__snake_case :List[str] = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(a__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=a__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __lowercase ( self , a__ = None , a__ = None , a__ = None , a__ = None , ) -> ImageClassifierOutputWithNoAttention:
'''simple docstring'''
__snake_case :Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
__snake_case :Tuple = self.resnet(a__ , output_hidden_states=a__ , return_dict=a__ )
__snake_case :Optional[Any] = outputs.pooler_output if return_dict else outputs[1]
__snake_case :Optional[Any] = self.classifier(a__ )
__snake_case :Any = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__snake_case :List[Any] = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__snake_case :List[Any] = """single_label_classification"""
else:
__snake_case :Union[str, Any] = """multi_label_classification"""
if self.config.problem_type == "regression":
__snake_case :Any = MSELoss()
if self.num_labels == 1:
__snake_case :Optional[Any] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
__snake_case :Any = loss_fct(a__ , a__ )
elif self.config.problem_type == "single_label_classification":
__snake_case :int = CrossEntropyLoss()
__snake_case :List[Any] = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
__snake_case :List[str] = BCEWithLogitsLoss()
__snake_case :int = loss_fct(a__ , a__ )
if not return_dict:
__snake_case :int = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=a__ , logits=a__ , hidden_states=outputs.hidden_states )
@add_start_docstrings(
"\n ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n " , lowercase_ , )
class snake_case__ ( lowercase_ , lowercase_):
'''simple docstring'''
def __init__( self , a__ ) -> int:
'''simple docstring'''
super().__init__(a__ )
super()._init_backbone(a__ )
__snake_case :Optional[int] = [config.embedding_size] + config.hidden_sizes
__snake_case :str = ResNetEmbeddings(a__ )
__snake_case :Any = ResNetEncoder(a__ )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(a__ )
@replace_return_docstrings(output_type=a__ , config_class=_CONFIG_FOR_DOC )
def __lowercase ( self , a__ , a__ = None , a__ = None ) -> BackboneOutput:
'''simple docstring'''
__snake_case :int = return_dict if return_dict is not None else self.config.use_return_dict
__snake_case :Union[str, Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__snake_case :List[str] = self.embedder(a__ )
__snake_case :List[Any] = self.encoder(a__ , output_hidden_states=a__ , return_dict=a__ )
__snake_case :Optional[int] = outputs.hidden_states
__snake_case :Tuple = ()
for idx, stage in enumerate(self.stage_names ):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
__snake_case :Union[str, Any] = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=a__ , hidden_states=outputs.hidden_states if output_hidden_states else None , attentions=a__ , )
| 455 | 0 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
lowercase_ = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def snake_case__ ( self : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[int]=None , _lowerCAmelCase : List[Any]=None ):
__snake_case : Optional[Any] = self.layer[current_layer](_lowerCAmelCase , _lowerCAmelCase , head_mask[current_layer] )
__snake_case : Dict = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top." , __UpperCamelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self : str , _lowerCAmelCase : Optional[int] ):
super().__init__(_lowerCAmelCase )
__snake_case : List[Any] = BertEncoderWithPabee(_lowerCAmelCase )
self.init_weights()
__snake_case : List[str] = 0
__snake_case : Dict = 0
__snake_case : List[Any] = 0
__snake_case : List[Any] = 0
def snake_case__ ( self : Union[str, Any] , _lowerCAmelCase : Dict ):
__snake_case : List[Any] = threshold
def snake_case__ ( self : Dict , _lowerCAmelCase : str ):
__snake_case : List[Any] = patience
def snake_case__ ( self : str ):
__snake_case : List[str] = 0
__snake_case : str = 0
def snake_case__ ( self : Optional[int] ):
__snake_case : Tuple = self.inference_layers_num / self.inference_instances_num
__snake_case : List[str] = (
f'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
f''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
)
print(_lowerCAmelCase )
@add_start_docstrings_to_model_forward(_lowerCAmelCase )
def snake_case__ ( self : List[str] , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : Any=None , _lowerCAmelCase : Any=None , _lowerCAmelCase : Tuple=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : str=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : str=None , _lowerCAmelCase : Optional[Any]=None , _lowerCAmelCase : str=None , _lowerCAmelCase : int=False , ):
if input_ids is not None and inputs_embeds is not None:
raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""" )
elif input_ids is not None:
__snake_case : Dict = input_ids.size()
elif inputs_embeds is not None:
__snake_case : Optional[int] = inputs_embeds.size()[:-1]
else:
raise ValueError("""You have to specify either input_ids or inputs_embeds""" )
__snake_case : int = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
__snake_case : Union[str, Any] = torch.ones(_lowerCAmelCase , device=_lowerCAmelCase )
if token_type_ids is None:
__snake_case : Any = torch.zeros(_lowerCAmelCase , dtype=torch.long , device=_lowerCAmelCase )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
__snake_case : torch.Tensor = self.get_extended_attention_mask(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
__snake_case , __snake_case , __snake_case : Tuple = encoder_hidden_states.size()
__snake_case : int = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
__snake_case : List[Any] = torch.ones(_lowerCAmelCase , device=_lowerCAmelCase )
__snake_case : Optional[Any] = self.invert_attention_mask(_lowerCAmelCase )
else:
__snake_case : Any = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
__snake_case : Optional[Any] = self.get_head_mask(_lowerCAmelCase , self.config.num_hidden_layers )
__snake_case : str = self.embeddings(
input_ids=_lowerCAmelCase , position_ids=_lowerCAmelCase , token_type_ids=_lowerCAmelCase , inputs_embeds=_lowerCAmelCase )
__snake_case : List[str] = embedding_output
if self.training:
__snake_case : Optional[int] = []
for i in range(self.config.num_hidden_layers ):
__snake_case : int = self.encoder.adaptive_forward(
_lowerCAmelCase , current_layer=_lowerCAmelCase , attention_mask=_lowerCAmelCase , head_mask=_lowerCAmelCase )
__snake_case : Optional[int] = self.pooler(_lowerCAmelCase )
__snake_case : int = output_layers[i](output_dropout(_lowerCAmelCase ) )
res.append(_lowerCAmelCase )
elif self.patience == 0: # Use all layers for inference
__snake_case : Union[str, Any] = self.encoder(
_lowerCAmelCase , attention_mask=_lowerCAmelCase , head_mask=_lowerCAmelCase , encoder_hidden_states=_lowerCAmelCase , encoder_attention_mask=_lowerCAmelCase , )
__snake_case : Any = self.pooler(encoder_outputs[0] )
__snake_case : Tuple = [output_layers[self.config.num_hidden_layers - 1](_lowerCAmelCase )]
else:
__snake_case : Optional[int] = 0
__snake_case : Union[str, Any] = None
__snake_case : Optional[Any] = 0
for i in range(self.config.num_hidden_layers ):
calculated_layer_num += 1
__snake_case : str = self.encoder.adaptive_forward(
_lowerCAmelCase , current_layer=_lowerCAmelCase , attention_mask=_lowerCAmelCase , head_mask=_lowerCAmelCase )
__snake_case : str = self.pooler(_lowerCAmelCase )
__snake_case : Any = output_layers[i](_lowerCAmelCase )
if regression:
__snake_case : List[str] = logits.detach()
if patient_result is not None:
__snake_case : Union[str, Any] = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
patient_counter += 1
else:
__snake_case : Union[str, Any] = 0
else:
__snake_case : Union[str, Any] = logits.detach().argmax(dim=1 )
if patient_result is not None:
__snake_case : List[str] = patient_result.detach().argmax(dim=1 )
if (patient_result is not None) and torch.all(labels.eq(_lowerCAmelCase ) ):
patient_counter += 1
else:
__snake_case : Optional[Any] = 0
__snake_case : str = logits
if patient_counter == self.patience:
break
__snake_case : Tuple = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. " , __UpperCamelCase , )
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self : Optional[int] , _lowerCAmelCase : str ):
super().__init__(_lowerCAmelCase )
__snake_case : Union[str, Any] = config.num_labels
__snake_case : List[Any] = BertModelWithPabee(_lowerCAmelCase )
__snake_case : Tuple = nn.Dropout(config.hidden_dropout_prob )
__snake_case : Any = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels ) for _ in range(config.num_hidden_layers )] )
self.init_weights()
@add_start_docstrings_to_model_forward(_lowerCAmelCase )
def snake_case__ ( self : List[Any] , _lowerCAmelCase : Tuple=None , _lowerCAmelCase : str=None , _lowerCAmelCase : str=None , _lowerCAmelCase : Optional[int]=None , _lowerCAmelCase : Dict=None , _lowerCAmelCase : Tuple=None , _lowerCAmelCase : int=None , ):
__snake_case : Optional[Any] = self.bert(
input_ids=_lowerCAmelCase , attention_mask=_lowerCAmelCase , token_type_ids=_lowerCAmelCase , position_ids=_lowerCAmelCase , head_mask=_lowerCAmelCase , inputs_embeds=_lowerCAmelCase , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
__snake_case : Union[str, Any] = (logits[-1],)
if labels is not None:
__snake_case : Optional[Any] = None
__snake_case : Optional[int] = 0
for ix, logits_item in enumerate(_lowerCAmelCase ):
if self.num_labels == 1:
# We are doing regression
__snake_case : Dict = MSELoss()
__snake_case : Optional[Any] = loss_fct(logits_item.view(-1 ) , labels.view(-1 ) )
else:
__snake_case : Any = CrossEntropyLoss()
__snake_case : str = loss_fct(logits_item.view(-1 , self.num_labels ) , labels.view(-1 ) )
if total_loss is None:
__snake_case : Tuple = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
__snake_case : List[str] = (total_loss / total_weights,) + outputs
return outputs
| 390 | import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
__snake_case : Union[str, Any] = TapasConfig.from_json_file(__SCREAMING_SNAKE_CASE )
# set absolute/relative position embeddings parameter
__snake_case : Tuple = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
__snake_case : Dict = TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE )
elif task == "WTQ":
# run_task_main.py hparams
__snake_case : int = 4
__snake_case : Dict = True
# hparam_utils.py hparams
__snake_case : Any = 0.66_46_94
__snake_case : Optional[int] = 0.20_79_51
__snake_case : str = 0.12_11_94
__snake_case : Optional[int] = True
__snake_case : int = True
__snake_case : int = False
__snake_case : List[Any] = 0.0_35_25_13
__snake_case : Any = TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
__snake_case : Any = 4
__snake_case : Union[str, Any] = False
# hparam_utils.py hparams
__snake_case : Any = 36.45_19
__snake_case : Union[str, Any] = 0.90_34_21
__snake_case : Any = 2_22.0_88
__snake_case : Tuple = True
__snake_case : List[str] = True
__snake_case : Any = True
__snake_case : str = 0.76_31_41
__snake_case : Optional[Any] = TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE )
elif task == "TABFACT":
__snake_case : int = TapasForSequenceClassification(config=__SCREAMING_SNAKE_CASE )
elif task == "MLM":
__snake_case : int = TapasForMaskedLM(config=__SCREAMING_SNAKE_CASE )
elif task == "INTERMEDIATE_PRETRAINING":
__snake_case : Optional[int] = TapasModel(config=__SCREAMING_SNAKE_CASE )
else:
raise ValueError(F'''Task {task} not supported.''' )
print(F'''Building PyTorch model from configuration: {config}''' )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Save pytorch-model (weights and configuration)
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
# Save tokenizer files
print(F'''Save tokenizer files to {pytorch_dump_path}''' )
__snake_case : Tuple = TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + """vocab.txt""" , model_max_length=5_1_2 )
tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE )
print("""Used relative position embeddings:""" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
)
parser.add_argument(
"--reset_position_index_per_cell",
default=False,
action="store_true",
help="Whether to use relative position embeddings or not. Defaults to True.",
)
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--tapas_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained TAPAS model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
lowercase_ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 390 | 1 |
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(F'Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}.' )
if tokenizer_name is None:
__lowerCamelCase : int =TOKENIZER_CLASSES
else:
__lowerCamelCase : Union[str, Any] ={tokenizer_name: getattr(SCREAMING_SNAKE_CASE , tokenizer_name + '''Fast''' )}
logger.info(F'Loading tokenizer classes: {tokenizer_names}' )
for tokenizer_name in tokenizer_names:
__lowerCamelCase : List[Any] =TOKENIZER_CLASSES[tokenizer_name]
__lowerCamelCase : Union[str, Any] =True
if checkpoint_name is None:
__lowerCamelCase : Any =list(tokenizer_class.max_model_input_sizes.keys() )
else:
__lowerCamelCase : Any =[checkpoint_name]
logger.info(F'For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}' )
for checkpoint in checkpoint_names:
logger.info(F'Loading {tokenizer_class.__class__.__name__} {checkpoint}' )
# Load tokenizer
__lowerCamelCase : Optional[Any] =tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE , force_download=SCREAMING_SNAKE_CASE )
# Save fast tokenizer
logger.info(F'Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}' )
# For organization names we create sub-directories
if "/" in checkpoint:
__lowerCamelCase , __lowerCamelCase : Optional[Any] =checkpoint.split('''/''' )
__lowerCamelCase : Dict =os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
elif add_prefix:
__lowerCamelCase : Union[str, Any] =checkpoint
__lowerCamelCase : str =dump_path
else:
__lowerCamelCase : List[Any] =None
__lowerCamelCase : Union[str, Any] =dump_path
logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}' )
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
__lowerCamelCase : int =list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
__lowerCamelCase : List[str] =file_path.split(SCREAMING_SNAKE_CASE )[-1][0]
if next_char == "/":
__lowerCamelCase : Union[str, Any] =os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCamelCase : List[Any] =None
logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}' )
__lowerCamelCase : Optional[Any] =tokenizer.save_pretrained(
SCREAMING_SNAKE_CASE , legacy_format=SCREAMING_SNAKE_CASE , filename_prefix=SCREAMING_SNAKE_CASE )
logger.info(F'=> File names {file_names}' )
for file_name in file_names:
if not file_name.endswith('''tokenizer.json''' ):
os.remove(SCREAMING_SNAKE_CASE )
logger.info(F'=> removing {file_name}' )
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--dump_path', default=None, type=str, required=True, help='Path to output generated fast tokenizer files.'
)
parser.add_argument(
'--tokenizer_name',
default=None,
type=str,
help=(
f'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
'download and convert all the checkpoints from AWS.'
),
)
parser.add_argument(
'--checkpoint_name',
default=None,
type=str,
help='Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.',
)
parser.add_argument(
'--force_download',
action='store_true',
help='Re-download checkpoints.',
)
_UpperCamelCase = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| 179 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class SCREAMING_SNAKE_CASE_ ( snake_case__ ):
"""simple docstring"""
__snake_case : int = """philschmid/bart-large-cnn-samsum"""
__snake_case : Optional[int] = (
"""This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
"""and returns a summary of the text."""
)
__snake_case : Dict = """summarizer"""
__snake_case : str = AutoTokenizer
__snake_case : List[str] = AutoModelForSeqaSeqLM
__snake_case : List[Any] = ["""text"""]
__snake_case : Dict = ["""text"""]
def __lowercase ( self :int , __lowercase :List[Any] ):
return self.pre_processor(__lowercase , return_tensors='''pt''' , truncation=__lowercase )
def __lowercase ( self :Optional[int] , __lowercase :Optional[int] ):
return self.model.generate(**__lowercase )[0]
def __lowercase ( self :List[Any] , __lowercase :str ):
return self.pre_processor.decode(__lowercase , skip_special_tokens=__lowercase , clean_up_tokenization_spaces=__lowercase )
| 179 | 1 |
import unittest
from transformers import BertGenerationConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class _lowercase :
def __init__( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int=13 , __lowerCAmelCase : int=7 , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Dict=99 , __lowerCAmelCase : Tuple=32 , __lowerCAmelCase : Optional[int]=5 , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : str=37 , __lowerCAmelCase : List[Any]="gelu" , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Tuple=50 , __lowerCAmelCase : List[Any]=0.0_2 , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : List[Any]=None , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = initializer_range
a = use_labels
a = scope
def A ( self : int ) -> str:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = self.get_config()
return config, input_ids, input_mask, token_labels
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
return BertGenerationConfig(
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 , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : Dict ) -> Tuple:
"""simple docstring"""
(
(
a
) , (
a
) , (
a
) , (
a
) ,
) = self.prepare_config_and_inputs()
a = True
a = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
a = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def A ( self : int , __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : int , **__lowerCAmelCase : Union[str, Any] , ) -> List[Any]:
"""simple docstring"""
a = BertGenerationEncoder(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] , **__lowerCAmelCase : Tuple , ) -> Dict:
"""simple docstring"""
a = True
a = BertGenerationEncoder(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase , encoder_attention_mask=__lowerCAmelCase , )
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Tuple , __lowerCAmelCase : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : List[Any] , **__lowerCAmelCase : str , ) -> Any:
"""simple docstring"""
a = True
a = True
a = BertGenerationDecoder(config=__lowerCAmelCase ).to(__lowerCAmelCase ).eval()
# first forward pass
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase , encoder_attention_mask=__lowerCAmelCase , use_cache=__lowerCAmelCase , )
a = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
a = ids_tensor((self.batch_size, 3) , config.vocab_size )
a = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
a = torch.cat([input_ids, next_tokens] , dim=-1 )
a = torch.cat([input_mask, next_mask] , dim=-1 )
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase , encoder_attention_mask=__lowerCAmelCase , output_hidden_states=__lowerCAmelCase , )["hidden_states"][0]
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase , encoder_attention_mask=__lowerCAmelCase , past_key_values=__lowerCAmelCase , output_hidden_states=__lowerCAmelCase , )["hidden_states"][0]
# select random slice
a = ids_tensor((1,) , output_from_past.shape[-1] ).item()
a = output_from_no_past[:, -3:, random_slice_idx].detach()
a = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 ) )
def A ( self : Optional[int] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , *__lowerCAmelCase : Union[str, Any] , ) -> Optional[Any]:
"""simple docstring"""
a = BertGenerationDecoder(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a , a , a , a = self.prepare_config_and_inputs()
a = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
_UpperCAmelCase = (BertGenerationDecoder,) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
if is_torch_available()
else {}
)
def A ( self : Any ) -> Dict:
"""simple docstring"""
a = BertGenerationEncoderTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = "bert"
self.model_tester.create_and_check_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*__lowerCAmelCase )
def A ( self : Any ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowerCAmelCase )
def A ( self : str ) -> int:
"""simple docstring"""
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
a = None
self.model_tester.create_and_check_model_as_decoder(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )
def A ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*__lowerCAmelCase )
@slow
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
a = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
self.assertIsNotNone(__lowerCAmelCase )
@require_torch
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
a = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
a = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size([1, 8, 1024] )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __lowerCAmelCase , atol=1E-4 ) )
@require_torch
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : List[Any] ) -> List[str]:
"""simple docstring"""
a = BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
a = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size([1, 8, 5_0358] )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __lowerCAmelCase , atol=1E-4 ) )
| 32 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class _lowercase :
def __init__( self : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : int=10 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Optional[int]=32 * 4 , __lowerCAmelCase : Dict=32 * 6 , __lowerCAmelCase : str=4 , __lowerCAmelCase : Dict=32 , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = is_training
a = use_auxiliary_loss
a = num_queries
a = num_channels
a = min_size
a = max_size
a = num_labels
a = mask_feature_size
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__lowerCAmelCase )
a = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__lowerCAmelCase )
a = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__lowerCAmelCase ) > 0.5
).float()
a = (torch.rand((self.batch_size, self.num_labels) , device=__lowerCAmelCase ) > 0.5).long()
a = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A ( self : str ) -> Any:
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def A ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
a , a , a , a , a = self.prepare_config_and_inputs()
a = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def A ( self : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
a = output.encoder_hidden_states
a = output.pixel_decoder_hidden_states
a = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , config.decoder_config.decoder_layers )
def A ( self : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str]=False ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
a = MaskFormerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
a = MaskFormerForInstanceSegmentation(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
def comm_check_on_output(__lowerCAmelCase : Tuple ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
a = model(
pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = MaskFormerModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def A ( self : Any ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : int ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
pass
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
a = MaskFormerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def A ( self : str ) -> Dict:
"""simple docstring"""
a = (self.model_tester.min_size,) * 2
a = {
"pixel_values": torch.randn((2, 3, *size) , device=__lowerCAmelCase ),
"mask_labels": torch.randn((2, 10, *size) , device=__lowerCAmelCase ),
"class_labels": torch.zeros(2 , 10 , device=__lowerCAmelCase ).long(),
}
a = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase , output_attentions=__lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = True
a = True
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
a = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
a = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
a = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
a = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A_ : int = 1E-4
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
a = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : int ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="pt" , )
a = inputs["pixel_values"].to(__lowerCAmelCase )
a = [el.to(__lowerCAmelCase ) for el in inputs["mask_labels"]]
a = [el.to(__lowerCAmelCase ) for el in inputs["class_labels"]]
with torch.no_grad():
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 32 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = 42
UpperCAmelCase = None
UpperCAmelCase = None
def _snake_case ( __snake_case ):
# Validation
def is_valid_tree(__snake_case ) -> bool:
if node is None:
return True
if not isinstance(__snake_case , __snake_case ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(__snake_case ):
raise ValueError(
'''Each node should be type of TreeNode and data should be float.''' )
def is_binary_search_tree_recursive_check(
__snake_case , __snake_case , __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 , __snake_case , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , __snake_case )
)
return is_binary_search_tree_recursive_check(__snake_case , -float('''inf''' ) , float('''inf''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10 | import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ ( __lowercase ):
def __init__( self : Union[str, Any] , _A : Optional[Any] , _A : Any=13 , _A : Union[str, Any]=7 , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[Any]=True , _A : Optional[int]=False , _A : Any=False , _A : int=False , _A : Optional[Any]=2 , _A : Any=99 , _A : str=0 , _A : Union[str, Any]=32 , _A : List[Any]=5 , _A : Tuple=4 , _A : List[str]=0.1 , _A : Union[str, Any]=0.1 , _A : int=512 , _A : Union[str, Any]=12 , _A : List[str]=2 , _A : int=0.02 , _A : Optional[Any]=3 , _A : Any=4 , _A : Optional[int]="last" , _A : Any=None , _A : Dict=None , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_lengths
_UpperCamelCase = use_token_type_ids
_UpperCamelCase = use_labels
_UpperCamelCase = gelu_activation
_UpperCamelCase = sinusoidal_embeddings
_UpperCamelCase = causal
_UpperCamelCase = asm
_UpperCamelCase = n_langs
_UpperCamelCase = vocab_size
_UpperCamelCase = n_special
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = num_labels
_UpperCamelCase = num_choices
_UpperCamelCase = summary_type
_UpperCamelCase = use_proj
_UpperCamelCase = scope
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCamelCase = None
if self.use_input_lengths:
_UpperCamelCase = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_UpperCamelCase = None
if self.use_token_type_ids:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCamelCase = ids_tensor([self.batch_size] , 2 ).float()
_UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCamelCase = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCamelCase_ ( self : str ):
return FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , )
def UpperCamelCase_ ( self : str , _A : Union[str, Any] , _A : Optional[Any] , _A : str , _A : Tuple , _A : List[str] , _A : List[Any] , _A : Any , _A : str , _A : Optional[int] , ):
_UpperCamelCase = FlaubertModel(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , lengths=_A , langs=_A )
_UpperCamelCase = model(_A , langs=_A )
_UpperCamelCase = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[Any] , _A : str , _A : Optional[int] , _A : Optional[Any] , _A : List[str] , _A : int , _A : str , _A : List[Any] , _A : Any , ):
_UpperCamelCase = FlaubertWithLMHeadModel(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[str] , _A : List[str] , _A : Optional[Any] , _A : Union[str, Any] , _A : str , _A : List[str] , _A : Tuple , _A : Optional[int] , _A : Dict , ):
_UpperCamelCase = FlaubertForQuestionAnsweringSimple(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase_ ( self : Tuple , _A : str , _A : Tuple , _A : Tuple , _A : Union[str, Any] , _A : List[str] , _A : int , _A : str , _A : Dict , _A : List[Any] , ):
_UpperCamelCase = FlaubertForQuestionAnswering(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , p_mask=_A , )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCamelCase_ ( self : List[Any] , _A : Union[str, Any] , _A : Tuple , _A : str , _A : int , _A : int , _A : Optional[int] , _A : Optional[int] , _A : int , _A : List[str] , ):
_UpperCamelCase = FlaubertForSequenceClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase_ ( self : Optional[int] , _A : List[str] , _A : Optional[Any] , _A : str , _A : Union[str, Any] , _A : List[Any] , _A : int , _A : List[Any] , _A : str , _A : List[str] , ):
_UpperCamelCase = self.num_labels
_UpperCamelCase = FlaubertForTokenClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , attention_mask=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase_ ( self : Tuple , _A : Dict , _A : str , _A : Optional[Any] , _A : List[str] , _A : Any , _A : Optional[int] , _A : Optional[Any] , _A : List[Any] , _A : List[str] , ):
_UpperCamelCase = self.num_choices
_UpperCamelCase = FlaubertForMultipleChoice(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = model(
_A , attention_mask=_A , token_type_ids=_A , labels=_A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''lengths''': input_lengths,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
UpperCAmelCase = (
{
"feature-extraction": FlaubertModel,
"fill-mask": FlaubertWithLMHeadModel,
"question-answering": FlaubertForQuestionAnsweringSimple,
"text-classification": FlaubertForSequenceClassification,
"token-classification": FlaubertForTokenClassification,
"zero-shot": FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Dict , _A : Dict , _A : Tuple , _A : int , _A : Any ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCamelCase_ ( self : str , _A : Any , _A : List[str] , _A : Optional[int]=False ):
_UpperCamelCase = super()._prepare_for_class(_A , _A , return_labels=_A )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
return inputs_dict
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = FlaubertModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , emb_dim=37 )
def UpperCamelCase_ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*_A )
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*_A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*_A )
@slow
def UpperCamelCase_ ( self : str ):
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = FlaubertModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@slow
@require_torch_gpu
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
_UpperCamelCase = True
_UpperCamelCase = model_class(config=_A )
_UpperCamelCase = self._prepare_for_class(_A , _A )
_UpperCamelCase = torch.jit.trace(
_A , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_A , os.path.join(_A , '''traced_model.pt''' ) )
_UpperCamelCase = torch.jit.load(os.path.join(_A , '''traced_model.pt''' ) , map_location=_A )
loaded(inputs_dict['''input_ids'''].to(_A ) , inputs_dict['''attention_mask'''].to(_A ) )
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
@slow
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased''' )
_UpperCamelCase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
with torch.no_grad():
_UpperCamelCase = model(_A )[0]
_UpperCamelCase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _A )
_UpperCamelCase = torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _A , atol=1e-4 ) )
| 10 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self : int ):
'''simple docstring'''
self.test()
def __magic_name__ ( self : str ):
'''simple docstring'''
snake_case__ : int = 0
snake_case__ : Union[str, Any] = False
while not completed:
if counter == 1:
self.reset()
snake_case__ : List[str] = self.advance()
if not self.does_advance(snake_case_ ):
raise Exception(
'''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.''' )
snake_case__ , snake_case__ , snake_case__ : int = self.update(snake_case_ )
counter += 1
if counter > 1_0_0_0_0:
raise Exception('''update() does not fulfill the constraint.''' )
if self.remaining() != 0:
raise Exception('''Custom Constraint is not defined correctly.''' )
@abstractmethod
def __magic_name__ ( self : Any ):
'''simple docstring'''
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def __magic_name__ ( self : int , snake_case_ : int ):
'''simple docstring'''
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def __magic_name__ ( self : List[Any] , snake_case_ : int ):
'''simple docstring'''
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def __magic_name__ ( self : Optional[Any] ):
'''simple docstring'''
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def __magic_name__ ( self : Any ):
'''simple docstring'''
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def __magic_name__ ( self : Any , snake_case_ : Any=False ):
'''simple docstring'''
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self : List[Any] , snake_case_ : List[int] ):
'''simple docstring'''
super(snake_case_ , self ).__init__()
if not isinstance(snake_case_ , snake_case_ ) or len(snake_case_ ) == 0:
raise ValueError(F"""`token_ids` has to be a non-empty list, but is {token_ids}.""" )
if any((not isinstance(snake_case_ , 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}.""" )
snake_case__ : Optional[Any] = token_ids
snake_case__ : Any = len(self.token_ids )
snake_case__ : Optional[int] = -1 # the index of the currently fulfilled step
snake_case__ : Dict = False
def __magic_name__ ( self : Optional[Any] ):
'''simple docstring'''
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def __magic_name__ ( self : Dict , snake_case_ : int ):
'''simple docstring'''
if not isinstance(snake_case_ , snake_case_ ):
raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(snake_case_ )}""" )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def __magic_name__ ( self : List[Any] , snake_case_ : int ):
'''simple docstring'''
if not isinstance(snake_case_ , snake_case_ ):
raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(snake_case_ )}""" )
snake_case__ : Optional[Any] = False
snake_case__ : Tuple = False
snake_case__ : Optional[Any] = False
if self.does_advance(snake_case_ ):
self.fulfilled_idx += 1
snake_case__ : str = True
if self.fulfilled_idx == (self.seqlen - 1):
snake_case__ : Optional[int] = True
snake_case__ : List[str] = completed
else:
# failed to make progress.
snake_case__ : Dict = True
self.reset()
return stepped, completed, reset
def __magic_name__ ( self : Union[str, Any] ):
'''simple docstring'''
snake_case__ : Tuple = False
snake_case__ : List[str] = 0
def __magic_name__ ( self : Any ):
'''simple docstring'''
return self.seqlen - (self.fulfilled_idx + 1)
def __magic_name__ ( self : str , snake_case_ : Tuple=False ):
'''simple docstring'''
snake_case__ : Any = PhrasalConstraint(self.token_ids )
if stateful:
snake_case__ : Optional[int] = self.seqlen
snake_case__ : List[str] = self.fulfilled_idx
snake_case__ : Union[str, Any] = self.completed
return new_constraint
class a :
"""simple docstring"""
def __init__( self : Any , snake_case_ : List[List[int]] , snake_case_ : int=True ):
'''simple docstring'''
snake_case__ : List[str] = max([len(snake_case_ ) for one in nested_token_ids] )
snake_case__ : Tuple = {}
for token_ids in nested_token_ids:
snake_case__ : List[str] = root
for tidx, token_id in enumerate(snake_case_ ):
if token_id not in level:
snake_case__ : int = {}
snake_case__ : Union[str, Any] = level[token_id]
if no_subsets and self.has_subsets(snake_case_ , 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}.""" )
snake_case__ : Optional[Any] = root
def __magic_name__ ( self : Tuple , snake_case_ : str ):
'''simple docstring'''
snake_case__ : Any = self.trie
for current_token in current_seq:
snake_case__ : str = start[current_token]
snake_case__ : List[str] = list(start.keys() )
return next_tokens
def __magic_name__ ( self : Optional[Any] , snake_case_ : Dict ):
'''simple docstring'''
snake_case__ : Optional[int] = self.next_tokens(snake_case_ )
return len(snake_case_ ) == 0
def __magic_name__ ( self : List[Any] , snake_case_ : Union[str, Any] ):
'''simple docstring'''
snake_case__ : str = list(root.values() )
if len(snake_case_ ) == 0:
return 1
else:
return sum([self.count_leaves(snake_case_ ) for nn in next_nodes] )
def __magic_name__ ( self : int , snake_case_ : int , snake_case_ : Optional[Any] ):
'''simple docstring'''
snake_case__ : Dict = self.count_leaves(snake_case_ )
return len(snake_case_ ) != leaf_count
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self : Optional[Any] , snake_case_ : List[List[int]] ):
'''simple docstring'''
super(snake_case_ , self ).__init__()
if not isinstance(snake_case_ , snake_case_ ) or len(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(snake_case_ , 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(snake_case_ , 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}.""" )
snake_case__ : List[str] = DisjunctiveTrie(snake_case_ )
snake_case__ : List[str] = nested_token_ids
snake_case__ : int = self.trie.max_height
snake_case__ : Dict = []
snake_case__ : Optional[int] = False
def __magic_name__ ( self : Optional[Any] ):
'''simple docstring'''
snake_case__ : int = self.trie.next_tokens(self.current_seq )
if len(snake_case_ ) == 0:
return None
else:
return token_list
def __magic_name__ ( self : Any , snake_case_ : int ):
'''simple docstring'''
if not isinstance(snake_case_ , snake_case_ ):
raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case_ )}""" )
snake_case__ : Tuple = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def __magic_name__ ( self : Union[str, Any] , snake_case_ : int ):
'''simple docstring'''
if not isinstance(snake_case_ , snake_case_ ):
raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case_ )}""" )
snake_case__ : str = False
snake_case__ : List[str] = False
snake_case__ : Optional[int] = False
if self.does_advance(snake_case_ ):
self.current_seq.append(snake_case_ )
snake_case__ : int = True
else:
snake_case__ : Optional[int] = True
self.reset()
snake_case__ : Tuple = self.trie.reached_leaf(self.current_seq )
snake_case__ : Optional[Any] = completed
return stepped, completed, reset
def __magic_name__ ( self : Optional[int] ):
'''simple docstring'''
snake_case__ : Dict = False
snake_case__ : Dict = []
def __magic_name__ ( self : Any ):
'''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 __magic_name__ ( self : Dict , snake_case_ : Any=False ):
'''simple docstring'''
snake_case__ : int = DisjunctiveConstraint(self.token_ids )
if stateful:
snake_case__ : Dict = self.seqlen
snake_case__ : Optional[int] = self.current_seq
snake_case__ : List[Any] = self.completed
return new_constraint
class a :
"""simple docstring"""
def __init__( self : List[Any] , snake_case_ : List[Constraint] ):
'''simple docstring'''
snake_case__ : List[Any] = constraints
# max # of steps required to fulfill a given constraint
snake_case__ : Union[str, Any] = max([c.seqlen for c in constraints] )
snake_case__ : Tuple = len(snake_case_ )
snake_case__ : List[str] = False
self.init_state()
def __magic_name__ ( self : Any ):
'''simple docstring'''
snake_case__ : int = []
snake_case__ : List[Any] = None
snake_case__ : str = [constraint.copy(stateful=snake_case_ ) for constraint in self.constraints]
def __magic_name__ ( self : Optional[int] ):
'''simple docstring'''
snake_case__ : Tuple = 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 __magic_name__ ( self : Dict ):
'''simple docstring'''
snake_case__ : Optional[int] = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
snake_case__ : Optional[Any] = constraint.advance()
if isinstance(snake_case_ , snake_case_ ):
token_list.append(snake_case_ )
elif isinstance(snake_case_ , snake_case_ ):
token_list.extend(snake_case_ )
else:
snake_case__ : Tuple = self.inprogress_constraint.advance()
if isinstance(snake_case_ , snake_case_ ):
token_list.append(snake_case_ )
elif isinstance(snake_case_ , snake_case_ ):
token_list.extend(snake_case_ )
if len(snake_case_ ) == 0:
return None
else:
return token_list
def __magic_name__ ( self : Optional[Any] , snake_case_ : Optional[List[int]] ):
'''simple docstring'''
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
snake_case__ , snake_case__ : Optional[int] = self.add(snake_case_ )
# the entire list of constraints are fulfilled
if self.completed:
break
def __magic_name__ ( self : Optional[int] , snake_case_ : int ):
'''simple docstring'''
if not isinstance(snake_case_ , snake_case_ ):
raise ValueError(F"""`token_id` should be an `int`, but is `{token_id}`.""" )
snake_case__ , snake_case__ : Any = False, False
if self.completed:
snake_case__ : Union[str, Any] = True
snake_case__ : Union[str, Any] = 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
snake_case__ , snake_case__ , snake_case__ : Union[str, Any] = self.inprogress_constraint.update(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=snake_case_ ) )
snake_case__ : Dict = 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 )
snake_case__ : str = None
if len(self.pending_constraints ) == 0:
# we're done!
snake_case__ : Dict = 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(snake_case_ ):
snake_case__ , snake_case__ , snake_case__ : List[Any] = pending_constraint.update(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(snake_case_ )
snake_case__ : List[Any] = None
if not complete and stepped:
snake_case__ : Any = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
snake_case__ : Optional[Any] = (
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.
snake_case__ : int = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def __magic_name__ ( self : List[str] , snake_case_ : str=True ):
'''simple docstring'''
snake_case__ : Optional[int] = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
snake_case__ : Any = [
constraint.copy(stateful=snake_case_ ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
snake_case__ : Union[str, Any] = self.inprogress_constraint.copy(stateful=snake_case_ )
snake_case__ : Optional[int] = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 502 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def _a ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
snake_case__ : Any = sorted(zip(__lowerCAmelCase , __lowerCAmelCase ) , key=lambda __lowerCAmelCase : x[0] / x[1] , reverse=__lowerCAmelCase )
snake_case__ , snake_case__ : Union[str, Any] = [i[0] for i in r], [i[1] for i in r]
snake_case__ : Dict = list(accumulate(__lowerCAmelCase ) )
snake_case__ : Optional[int] = bisect(__lowerCAmelCase , __lowerCAmelCase )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 502 | 1 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@parameterized.expand([(None,), ('foo.json',)] )
def _lowerCAmelCase ( self : Tuple , _snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
a__ = GenerationConfig(
do_sample=_snake_case , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_snake_case , config_name=_snake_case )
a__ = GenerationConfig.from_pretrained(_snake_case , config_name=_snake_case )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , _snake_case )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , _snake_case )
def _lowerCAmelCase ( self : Dict ) -> Dict:
'''simple docstring'''
a__ = AutoConfig.from_pretrained('gpt2' )
a__ = GenerationConfig.from_model_config(_snake_case )
a__ = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(_snake_case , _snake_case )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def _lowerCAmelCase ( self : Optional[int] ) -> int:
'''simple docstring'''
a__ = GenerationConfig()
a__ = {
'max_new_tokens': 1024,
'foo': 'bar',
}
a__ = copy.deepcopy(_snake_case )
a__ = generation_config.update(**_snake_case )
# update_kwargs was not modified (no side effects)
self.assertEqual(_snake_case , _snake_case )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(_snake_case , {'foo': 'bar'} )
def _lowerCAmelCase ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
a__ = GenerationConfig()
a__ = 'bar'
with tempfile.TemporaryDirectory('test-generation-config' ) as tmp_dir:
generation_config.save_pretrained(_snake_case )
a__ = GenerationConfig.from_pretrained(_snake_case )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , 'bar' )
a__ = GenerationConfig.from_model_config(_snake_case )
assert not hasattr(_snake_case , 'foo' ) # no new kwargs should be initialized if from config
def _lowerCAmelCase ( self : Optional[Any] ) -> str:
'''simple docstring'''
a__ = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , _snake_case )
self.assertEqual(default_config.num_beams , 1 )
a__ = GenerationConfig(
do_sample=_snake_case , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , _snake_case )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_snake_case )
a__ = GenerationConfig.from_pretrained(_snake_case , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , _snake_case )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def _lowerCAmelCase ( cls : Dict ) -> str:
'''simple docstring'''
a__ = TOKEN
HfFolder.save_token(_snake_case )
@classmethod
def _lowerCAmelCase ( cls : Optional[int] ) -> str:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-generation-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-generation-config-org' )
except HTTPError:
pass
def _lowerCAmelCase ( self : Tuple ) -> Dict:
'''simple docstring'''
a__ = GenerationConfig(
do_sample=_snake_case , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('test-generation-config' , use_auth_token=self._token )
a__ = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_snake_case , getattr(_snake_case , _snake_case ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-generation-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_snake_case , repo_id='test-generation-config' , push_to_hub=_snake_case , use_auth_token=self._token )
a__ = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_snake_case , getattr(_snake_case , _snake_case ) )
def _lowerCAmelCase ( self : int ) -> Dict:
'''simple docstring'''
a__ = GenerationConfig(
do_sample=_snake_case , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('valid_org/test-generation-config-org' , use_auth_token=self._token )
a__ = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_snake_case , getattr(_snake_case , _snake_case ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-generation-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_snake_case , repo_id='valid_org/test-generation-config-org' , push_to_hub=_snake_case , use_auth_token=self._token )
a__ = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_snake_case , getattr(_snake_case , _snake_case ) )
| 232 | """simple docstring"""
def _lowerCamelCase ( UpperCAmelCase__ = 60_08_51_47_51_43 ) -> int:
'''simple docstring'''
try:
a__ = int(UpperCAmelCase__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n must be greater than or equal to one.' )
a__ = 2
a__ = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
a__ = i
while n % i == 0:
a__ = n // i
i += 1
return int(UpperCAmelCase__ )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 232 | 1 |
import logging
import os
from .state import PartialState
class __A ( logging.LoggerAdapter ):
@staticmethod
def _snake_case ( UpperCAmelCase_ ):
lowerCamelCase =PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ , *UpperCAmelCase_ , **UpperCAmelCase_ ):
if PartialState._shared_state == {}:
raise RuntimeError(
"""You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.""" )
lowerCamelCase =kwargs.pop("""main_process_only""" , UpperCAmelCase_ )
lowerCamelCase =kwargs.pop("""in_order""" , UpperCAmelCase_ )
if self.isEnabledFor(UpperCAmelCase_ ):
if self._should_log(UpperCAmelCase_ ):
lowerCamelCase , lowerCamelCase =self.process(UpperCAmelCase_ , UpperCAmelCase_ )
self.logger.log(UpperCAmelCase_ , UpperCAmelCase_ , *UpperCAmelCase_ , **UpperCAmelCase_ )
elif in_order:
lowerCamelCase =PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
lowerCamelCase , lowerCamelCase =self.process(UpperCAmelCase_ , UpperCAmelCase_ )
self.logger.log(UpperCAmelCase_ , UpperCAmelCase_ , *UpperCAmelCase_ , **UpperCAmelCase_ )
state.wait_for_everyone()
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase = None ) -> Union[str, Any]:
if log_level is None:
lowerCamelCase =os.environ.get("""ACCELERATE_LOG_LEVEL""" , _UpperCAmelCase )
lowerCamelCase =logging.getLogger(_UpperCAmelCase )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(_UpperCAmelCase , {} )
| 721 |
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
lowerCamelCase =[0 for i in range(r + 1 )]
# nc0 = 1
lowerCamelCase =1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
lowerCamelCase =min(_UpperCAmelCase , _UpperCAmelCase )
while j > 0:
c[j] += c[j - 1]
j -= 1
return c[r]
print(binomial_coefficient(n=10, r=5))
| 269 | 0 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _lowerCAmelCase ( __magic_name__ :List[str] ):
monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings''' , set() )
@pytest.fixture
def _lowerCAmelCase ( __magic_name__ :Optional[int] ):
class snake_case__ :
'''simple docstring'''
def __init__( self : List[Any] , lowerCAmelCase_ : Optional[int] ) -> int:
UpperCAmelCase_ = metric_id
class snake_case__ :
'''simple docstring'''
__A = [MetricMock(__snake_case ) for metric_id in ['''accuracy''', '''mse''', '''precision''', '''codeparrot/apps_metric''']]
def UpperCamelCase ( self : Any ) -> str:
return self._metrics
monkeypatch.setattr('''datasets.inspect.huggingface_hub''' , HfhMock() )
@pytest.mark.parametrize(
'''func, args''' , [(load_metric, ('''metrics/mse''',)), (list_metrics, ()), (inspect_metric, ('''metrics/mse''', '''tmp_path'''))] )
def _lowerCAmelCase ( __magic_name__ :List[str] , __magic_name__ :int , __magic_name__ :Union[str, Any] , __magic_name__ :List[Any] , __magic_name__ :str ):
if "tmp_path" in args:
UpperCAmelCase_ = tuple(arg if arg != '''tmp_path''' else tmp_path for arg in args )
with pytest.warns(__magic_name__ , match='''https://huggingface.co/docs/evaluate''' ):
func(*__magic_name__ )
| 121 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def _lowerCAmelCase ( __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
UpperCAmelCase_ = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
UpperCAmelCase_ = _calculate(days - 1 , __magic_name__ , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
UpperCAmelCase_ = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
UpperCAmelCase_ = _calculate(days - 1 , __magic_name__ , 0 )
UpperCAmelCase_ = state_late + state_absent + state_ontime
UpperCAmelCase_ = prizestrings
return prizestrings
def _lowerCAmelCase ( __magic_name__ :int = 3_0 ):
return _calculate(__magic_name__ , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 121 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__lowerCAmelCase = 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).
__lowerCAmelCase = [0, 25, 50]
__lowerCAmelCase = [25, 50, 75]
__lowerCAmelCase = fuzz.membership.trimf(X, abca)
__lowerCAmelCase = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
__lowerCAmelCase = np.ones(75)
__lowerCAmelCase = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
__lowerCAmelCase = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
__lowerCAmelCase = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
__lowerCAmelCase = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
__lowerCAmelCase = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
__lowerCAmelCase = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
__lowerCAmelCase = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
__lowerCAmelCase = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
__lowerCAmelCase = 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()
| 335 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__lowerCAmelCase = logging.get_logger(__name__)
class __a ( __UpperCamelCase ):
__lowercase : Union[str, Any] = 'upernet'
def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=512 , lowerCAmelCase__=0.0_2 , lowerCAmelCase__=[1, 2, 3, 6] , lowerCAmelCase__=True , lowerCAmelCase__=0.4 , lowerCAmelCase__=384 , lowerCAmelCase__=256 , lowerCAmelCase__=1 , lowerCAmelCase__=False , lowerCAmelCase__=255 , **lowerCAmelCase__ , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
lowercase__: str = CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4'] )
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
lowercase__: str = backbone_config.get('model_type' )
lowercase__: Union[str, Any] = CONFIG_MAPPING[backbone_model_type]
lowercase__: Dict = config_class.from_dict(lowerCAmelCase__ )
lowercase__: List[Any] = backbone_config
lowercase__: Union[str, Any] = hidden_size
lowercase__: Tuple = initializer_range
lowercase__: Optional[int] = pool_scales
lowercase__: Union[str, Any] = use_auxiliary_head
lowercase__: Any = auxiliary_loss_weight
lowercase__: Tuple = auxiliary_in_channels
lowercase__: Optional[Any] = auxiliary_channels
lowercase__: List[Any] = auxiliary_num_convs
lowercase__: List[str] = auxiliary_concat_input
lowercase__: Any = loss_ignore_index
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
lowercase__: Tuple = copy.deepcopy(self.__dict__ )
lowercase__: List[Any] = self.backbone_config.to_dict()
lowercase__: str = self.__class__.model_type
return output
| 335 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
lowerCamelCase__ : Optional[int] = logging.get_logger(__name__)
lowerCamelCase__ : Any = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_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""",
"""adapter_layer""": """encoder.layers.*.adapter_layer""",
"""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""",
"""mask_emb""": """masked_spec_embed""",
"""pooling_layer.linear""": """projector""",
"""pooling_layer.projection""": """classifier""",
}
lowerCamelCase__ : Union[str, Any] = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""projector""",
"""classifier""",
]
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> List[str]:
snake_case__ = {}
with open(__lowerCAmelCase , '''r''' ) as file:
for line_number, line in enumerate(__lowerCAmelCase ):
snake_case__ = line.strip()
if line:
snake_case__ = line.split()
snake_case__ = line_number
snake_case__ = words[0]
snake_case__ = value
return result
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
for attribute in key.split('''.''' ):
snake_case__ = getattr(__lowerCAmelCase , __lowerCAmelCase )
snake_case__ = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCAmelCase ):
snake_case__ = PARAM_MAPPING[full_name.split('''.''' )[-1]]
snake_case__ = '''param'''
if weight_type is not None and weight_type != "param":
snake_case__ = getattr(__lowerCAmelCase , __lowerCAmelCase ).shape
elif weight_type is not None and weight_type == "param":
snake_case__ = hf_pointer
for attribute in hf_param_name.split('''.''' ):
snake_case__ = getattr(__lowerCAmelCase , __lowerCAmelCase )
snake_case__ = shape_pointer.shape
# let's reduce dimension
snake_case__ = value[0]
else:
snake_case__ = 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":
snake_case__ = value
elif weight_type == "weight_g":
snake_case__ = value
elif weight_type == "weight_v":
snake_case__ = value
elif weight_type == "bias":
snake_case__ = value
elif weight_type == "param":
for attribute in hf_param_name.split('''.''' ):
snake_case__ = getattr(__lowerCAmelCase , __lowerCAmelCase )
snake_case__ = value
else:
snake_case__ = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
snake_case__ = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCAmelCase ):
snake_case__ = PARAM_MAPPING[full_name.split('''.''' )[-1]]
snake_case__ = '''param'''
if weight_type is not None and weight_type != "param":
snake_case__ = '''.'''.join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
snake_case__ = '''.'''.join([key, hf_param_name] )
else:
snake_case__ = key
snake_case__ = value if '''lm_head''' in full_key else value[0]
lowerCamelCase__ : Optional[int] = {
"""W_a""": """linear_1.weight""",
"""W_b""": """linear_2.weight""",
"""b_a""": """linear_1.bias""",
"""b_b""": """linear_2.bias""",
"""ln_W""": """norm.weight""",
"""ln_b""": """norm.bias""",
}
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> Tuple:
snake_case__ = False
for key, mapped_key in MAPPING.items():
snake_case__ = '''wav2vec2.''' + 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]:
snake_case__ = True
if "*" in mapped_key:
snake_case__ = name.split(__lowerCAmelCase )[0].split('''.''' )[-2]
snake_case__ = mapped_key.replace('''*''' , __lowerCAmelCase )
if "weight_g" in name:
snake_case__ = '''weight_g'''
elif "weight_v" in name:
snake_case__ = '''weight_v'''
elif "bias" in name:
snake_case__ = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case__ = '''weight'''
else:
snake_case__ = None
if hf_dict is not None:
rename_dict(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else:
set_recursively(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return is_used
return is_used
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
snake_case__ = []
snake_case__ = fairseq_model.state_dict()
snake_case__ = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
snake_case__ = False
if "conv_layers" in name:
load_conv_layer(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , hf_model.config.feat_extract_norm == '''group''' , )
snake_case__ = True
else:
snake_case__ = load_wavaveca_layer(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if not is_used:
unused_weights.append(__lowerCAmelCase )
logger.warning(F"""Unused weights: {unused_weights}""" )
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
snake_case__ = full_name.split('''conv_layers.''' )[-1]
snake_case__ = name.split('''.''' )
snake_case__ = int(items[0] )
snake_case__ = 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.""" )
snake_case__ = 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.""" )
snake_case__ = 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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
snake_case__ = 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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
snake_case__ = 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 SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=True , __lowerCAmelCase=False ) -> List[str]:
if config_path is not None:
snake_case__ = WavaVecaConfig.from_pretrained(__lowerCAmelCase )
else:
snake_case__ = WavaVecaConfig()
if is_seq_class:
snake_case__ = read_txt_into_dict(__lowerCAmelCase )
snake_case__ = idalabel
snake_case__ = WavaVecaForSequenceClassification(__lowerCAmelCase )
snake_case__ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , )
feature_extractor.save_pretrained(__lowerCAmelCase )
elif is_finetuned:
if dict_path:
snake_case__ = Dictionary.load(__lowerCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case__ = target_dict.pad_index
snake_case__ = target_dict.bos_index
snake_case__ = target_dict.eos_index
snake_case__ = len(target_dict.symbols )
snake_case__ = os.path.join(__lowerCAmelCase , '''vocab.json''' )
if not os.path.isdir(__lowerCAmelCase ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__lowerCAmelCase ) )
return
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
snake_case__ = target_dict.indices
# fairseq has the <pad> and <s> switched
snake_case__ = 0
snake_case__ = 1
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(__lowerCAmelCase , __lowerCAmelCase )
snake_case__ = WavaVecaCTCTokenizer(
__lowerCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=__lowerCAmelCase , )
snake_case__ = True if config.feat_extract_norm == '''layer''' else False
snake_case__ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , )
snake_case__ = WavaVecaProcessor(feature_extractor=__lowerCAmelCase , tokenizer=__lowerCAmelCase )
processor.save_pretrained(__lowerCAmelCase )
snake_case__ = WavaVecaForCTC(__lowerCAmelCase )
else:
snake_case__ = WavaVecaForPreTraining(__lowerCAmelCase )
if is_finetuned or is_seq_class:
snake_case__ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
snake_case__ = argparse.Namespace(task='''audio_pretraining''' )
snake_case__ = fairseq.tasks.setup_task(__lowerCAmelCase )
snake_case__ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__lowerCAmelCase )
snake_case__ = model[0].eval()
recursively_load_weights(__lowerCAmelCase , __lowerCAmelCase , not is_finetuned )
hf_wavavec.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
lowerCamelCase__ : str = 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"""
)
parser.add_argument(
"""--is_seq_class""",
action="""store_true""",
help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
)
lowerCamelCase__ : Tuple = parser.parse_args()
lowerCamelCase__ : str = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 33 |
'''simple docstring'''
from math import factorial
def _a ( __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : float ):
"""simple docstring"""
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(__lowerCAmelCase , __lowerCAmelCase ) or not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
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__ : Dict = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
snake_case__ : str = float(factorial(__lowerCAmelCase ) )
coefficient /= factorial(__lowerCAmelCase ) * 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))
| 347 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class A__ :
'''simple docstring'''
def __init__( self: Optional[Any]) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = {}
def _SCREAMING_SNAKE_CASE ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: str) -> None:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = {}
def _SCREAMING_SNAKE_CASE ( self: List[Any] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: float) -> None:
"""simple docstring"""
if nodea not in self.connections:
self.add_node(_SCREAMING_SNAKE_CASE)
if nodea not in self.connections:
self.add_node(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = probability
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> list[str]:
"""simple docstring"""
return list(self.connections)
def _SCREAMING_SNAKE_CASE ( self: List[str] , _SCREAMING_SNAKE_CASE: str) -> str:
"""simple docstring"""
__lowerCAmelCase : List[str] = 0
__lowerCAmelCase : str = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def _lowercase ( __snake_case ,__snake_case ,__snake_case ) -> dict[str, int]:
__lowerCAmelCase : List[Any] = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(__snake_case ,__snake_case ,__snake_case )
__lowerCAmelCase : List[Any] = Counter(graph.get_nodes() )
__lowerCAmelCase : Tuple = start
for _ in range(__snake_case ):
__lowerCAmelCase : Union[str, Any] = graph.transition(__snake_case )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod() | 615 |
"""simple docstring"""
def _lowercase ( __snake_case ,__snake_case ) -> list:
__lowerCAmelCase : List[str] = len(__snake_case )
__lowerCAmelCase : Dict = []
for i in range(len(__snake_case ) - pat_len + 1 ):
__lowerCAmelCase : List[Any] = True
for j in range(__snake_case ):
if s[i + j] != pattern[j]:
__lowerCAmelCase : Union[str, Any] = False
break
if match_found:
position.append(__snake_case )
return position
if __name__ == "__main__":
assert naive_pattern_search('ABCDEFG', 'DE') == [3]
print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC')) | 615 | 1 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def __lowerCAmelCase ( UpperCamelCase ) -> Dict:
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
lowerCAmelCase_ = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class _lowerCAmelCase ( _lowercase ):
@staticmethod
def __magic_name__( __UpperCAmelCase ):
lowerCAmelCase__ : int = parser.add_parser(
'''convert''' , help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.''' , )
train_parser.add_argument('''--model_type''' , type=__UpperCAmelCase , required=__UpperCAmelCase , help='''Model\'s type.''' )
train_parser.add_argument(
'''--tf_checkpoint''' , type=__UpperCAmelCase , required=__UpperCAmelCase , help='''TensorFlow checkpoint path or folder.''' )
train_parser.add_argument(
'''--pytorch_dump_output''' , type=__UpperCAmelCase , required=__UpperCAmelCase , help='''Path to the PyTorch saved model output.''' )
train_parser.add_argument('''--config''' , type=__UpperCAmelCase , default='''''' , help='''Configuration file path or folder.''' )
train_parser.add_argument(
'''--finetuning_task_name''' , type=__UpperCAmelCase , default=__UpperCAmelCase , help='''Optional fine-tuning task name if the TF model was a finetuned model.''' , )
train_parser.set_defaults(func=__UpperCAmelCase )
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , *__UpperCAmelCase , ):
lowerCAmelCase__ : str = logging.get_logger('''transformers-cli/converting''' )
self._logger.info(f"""Loading model {model_type}""" )
lowerCAmelCase__ : Any = model_type
lowerCAmelCase__ : Dict = tf_checkpoint
lowerCAmelCase__ : Optional[Any] = pytorch_dump_output
lowerCAmelCase__ : Tuple = config
lowerCAmelCase__ : List[str] = finetuning_task_name
def __magic_name__( self ):
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCAmelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCAmelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCAmelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(__UpperCAmelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCAmelCase )
if "ckpt" in self._tf_checkpoint.lower():
lowerCAmelCase__ : Dict = self._tf_checkpoint
lowerCAmelCase__ : Any = ''''''
else:
lowerCAmelCase__ : List[Any] = self._tf_checkpoint
lowerCAmelCase__ : Union[str, Any] = ''''''
convert_transfo_xl_checkpoint_to_pytorch(
__UpperCAmelCase , self._config , self._pytorch_dump_output , __UpperCAmelCase )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCAmelCase )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCAmelCase )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
'''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]''' )
| 678 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class _lowerCAmelCase ( unittest.TestCase ):
A__ = MODEL_FOR_CAUSAL_LM_MAPPING
A__ = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def __magic_name__( self ):
lowerCAmelCase__ : Tuple = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
lowerCAmelCase__ : Optional[int] = text_generator('''This is a test''' , do_sample=__UpperCAmelCase )
self.assertEqual(
__UpperCAmelCase , [
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
] , )
lowerCAmelCase__ : List[str] = text_generator(['''This is a test''', '''This is a second test'''] )
self.assertEqual(
__UpperCAmelCase , [
[
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
''' oscope. oscope. FiliFili@@'''
)
}
],
] , )
lowerCAmelCase__ : str = text_generator('''This is a test''' , do_sample=__UpperCAmelCase , num_return_sequences=2 , return_tensors=__UpperCAmelCase )
self.assertEqual(
__UpperCAmelCase , [
{'''generated_token_ids''': ANY(__UpperCAmelCase )},
{'''generated_token_ids''': ANY(__UpperCAmelCase )},
] , )
lowerCAmelCase__ : List[Any] = text_generator.model.config.eos_token_id
lowerCAmelCase__ : List[Any] = '''<pad>'''
lowerCAmelCase__ : List[Any] = text_generator(
['''This is a test''', '''This is a second test'''] , do_sample=__UpperCAmelCase , num_return_sequences=2 , batch_size=2 , return_tensors=__UpperCAmelCase , )
self.assertEqual(
__UpperCAmelCase , [
[
{'''generated_token_ids''': ANY(__UpperCAmelCase )},
{'''generated_token_ids''': ANY(__UpperCAmelCase )},
],
[
{'''generated_token_ids''': ANY(__UpperCAmelCase )},
{'''generated_token_ids''': ANY(__UpperCAmelCase )},
],
] , )
@require_tf
def __magic_name__( self ):
lowerCAmelCase__ : int = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
lowerCAmelCase__ : List[Any] = text_generator('''This is a test''' , do_sample=__UpperCAmelCase )
self.assertEqual(
__UpperCAmelCase , [
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
] , )
lowerCAmelCase__ : List[str] = text_generator(['''This is a test''', '''This is a second test'''] , do_sample=__UpperCAmelCase )
self.assertEqual(
__UpperCAmelCase , [
[
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
)
}
],
] , )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : Dict = TextGenerationPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase )
return text_generator, ["This is a test", "Another test"]
def __magic_name__( self ):
lowerCAmelCase__ : Any = '''Hello I believe in'''
lowerCAmelCase__ : List[Any] = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
lowerCAmelCase__ : Optional[int] = text_generator(__UpperCAmelCase )
self.assertEqual(
__UpperCAmelCase , [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] , )
lowerCAmelCase__ : List[str] = text_generator(__UpperCAmelCase , stop_sequence=''' fe''' )
self.assertEqual(__UpperCAmelCase , [{'''generated_text''': '''Hello I believe in fe'''}] )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : str = text_generator.model
lowerCAmelCase__ : Optional[int] = text_generator.tokenizer
lowerCAmelCase__ : Tuple = text_generator('''This is a test''' )
self.assertEqual(__UpperCAmelCase , [{'''generated_text''': ANY(__UpperCAmelCase )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
lowerCAmelCase__ : Optional[int] = text_generator('''This is a test''' , return_full_text=__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , [{'''generated_text''': ANY(__UpperCAmelCase )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
lowerCAmelCase__ : Dict = pipeline(task='''text-generation''' , model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , return_full_text=__UpperCAmelCase )
lowerCAmelCase__ : Dict = text_generator('''This is a test''' )
self.assertEqual(__UpperCAmelCase , [{'''generated_text''': ANY(__UpperCAmelCase )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
lowerCAmelCase__ : List[str] = text_generator('''This is a test''' , return_full_text=__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , [{'''generated_text''': ANY(__UpperCAmelCase )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
lowerCAmelCase__ : Optional[int] = text_generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=__UpperCAmelCase )
self.assertEqual(
__UpperCAmelCase , [
[{'''generated_text''': ANY(__UpperCAmelCase )}, {'''generated_text''': ANY(__UpperCAmelCase )}],
[{'''generated_text''': ANY(__UpperCAmelCase )}, {'''generated_text''': ANY(__UpperCAmelCase )}],
] , )
if text_generator.tokenizer.pad_token is not None:
lowerCAmelCase__ : List[str] = text_generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=__UpperCAmelCase )
self.assertEqual(
__UpperCAmelCase , [
[{'''generated_text''': ANY(__UpperCAmelCase )}, {'''generated_text''': ANY(__UpperCAmelCase )}],
[{'''generated_text''': ANY(__UpperCAmelCase )}, {'''generated_text''': ANY(__UpperCAmelCase )}],
] , )
with self.assertRaises(__UpperCAmelCase ):
lowerCAmelCase__ : Any = text_generator('''test''' , return_full_text=__UpperCAmelCase , return_text=__UpperCAmelCase )
with self.assertRaises(__UpperCAmelCase ):
lowerCAmelCase__ : Optional[int] = text_generator('''test''' , return_full_text=__UpperCAmelCase , return_tensors=__UpperCAmelCase )
with self.assertRaises(__UpperCAmelCase ):
lowerCAmelCase__ : str = text_generator('''test''' , return_text=__UpperCAmelCase , return_tensors=__UpperCAmelCase )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
lowerCAmelCase__ : str = text_generator('''''' )
self.assertEqual(__UpperCAmelCase , [{'''generated_text''': ANY(__UpperCAmelCase )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
lowerCAmelCase__ : List[str] = text_generator('''''' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
lowerCAmelCase__ : Optional[Any] = ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
if (
tokenizer.model_max_length < 1_0000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('''This is a test''' * 500 , max_new_tokens=20 )
lowerCAmelCase__ : Optional[Any] = text_generator('''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(__UpperCAmelCase ):
text_generator(
'''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def __magic_name__( self ):
import torch
# Classic `model_kwargs`
lowerCAmelCase__ : List[str] = pipeline(
model='''hf-internal-testing/tiny-random-bloom''' , model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
lowerCAmelCase__ : Any = pipe('''This is a test''' )
self.assertEqual(
__UpperCAmelCase , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
lowerCAmelCase__ : Dict = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
lowerCAmelCase__ : Union[str, Any] = pipe('''This is a test''' )
self.assertEqual(
__UpperCAmelCase , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
lowerCAmelCase__ : str = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
lowerCAmelCase__ : Any = pipe('''This is a test''' )
self.assertEqual(
__UpperCAmelCase , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
@require_torch
@require_torch_gpu
def __magic_name__( self ):
import torch
lowerCAmelCase__ : List[str] = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device=0 , torch_dtype=torch.floataa )
pipe('''This is a test''' )
@require_torch
@require_accelerate
@require_torch_gpu
def __magic_name__( self ):
import torch
lowerCAmelCase__ : Any = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.floataa )
pipe('''This is a test''' , do_sample=__UpperCAmelCase , top_p=0.5 )
def __magic_name__( self ):
lowerCAmelCase__ : int = '''Hello world'''
lowerCAmelCase__ : Union[str, Any] = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
lowerCAmelCase__ : List[Any] = logging.get_logger('''transformers.generation.tf_utils''' )
else:
lowerCAmelCase__ : Dict = logging.get_logger('''transformers.generation.utils''' )
lowerCAmelCase__ : Optional[Any] = '''Both `max_new_tokens`''' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(__UpperCAmelCase ) as cl:
lowerCAmelCase__ : List[str] = text_generator(__UpperCAmelCase , max_length=10 , max_new_tokens=1 )
self.assertIn(__UpperCAmelCase , cl.out )
# The user only sets one -> no warning
with CaptureLogger(__UpperCAmelCase ) as cl:
lowerCAmelCase__ : Any = text_generator(__UpperCAmelCase , max_new_tokens=1 )
self.assertNotIn(__UpperCAmelCase , cl.out )
with CaptureLogger(__UpperCAmelCase ) as cl:
lowerCAmelCase__ : Union[str, Any] = text_generator(__UpperCAmelCase , max_length=10 )
self.assertNotIn(__UpperCAmelCase , cl.out )
| 678 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
snake_case_ = logging.get_logger(__name__)
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = WavaVecaForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_ , config=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ : Any = downstream_dict["projector.weight"]
SCREAMING_SNAKE_CASE_ : List[str] = downstream_dict["projector.bias"]
SCREAMING_SNAKE_CASE_ : List[str] = downstream_dict["model.post_net.linear.weight"]
SCREAMING_SNAKE_CASE_ : List[Any] = downstream_dict["model.post_net.linear.bias"]
return model
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = WavaVecaForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE_ , config=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ : str = downstream_dict["model.linear.weight"]
SCREAMING_SNAKE_CASE_ : List[Any] = downstream_dict["model.linear.bias"]
return model
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = WavaVecaForXVector.from_pretrained(SCREAMING_SNAKE_CASE_ , config=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ : Dict = downstream_dict["connector.weight"]
SCREAMING_SNAKE_CASE_ : int = downstream_dict["connector.bias"]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
SCREAMING_SNAKE_CASE_ : List[Any] = downstream_dict[
F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
]
SCREAMING_SNAKE_CASE_ : int = downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]
SCREAMING_SNAKE_CASE_ : List[Any] = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
SCREAMING_SNAKE_CASE_ : List[str] = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
SCREAMING_SNAKE_CASE_ : Optional[Any] = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
SCREAMING_SNAKE_CASE_ : str = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
SCREAMING_SNAKE_CASE_ : Dict = downstream_dict["objective.W"]
return model
@torch.no_grad()
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Dict ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = torch.load(SCREAMING_SNAKE_CASE_ , map_location="cpu" )
SCREAMING_SNAKE_CASE_ : Optional[Any] = checkpoint["Downstream"]
SCREAMING_SNAKE_CASE_ : Optional[Any] = WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ : List[str] = WavaVecaFeatureExtractor.from_pretrained(
SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , do_normalize=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ : Optional[int] = hf_config.architectures[0]
if arch.endswith("ForSequenceClassification" ):
SCREAMING_SNAKE_CASE_ : int = convert_classification(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif arch.endswith("ForAudioFrameClassification" ):
SCREAMING_SNAKE_CASE_ : List[Any] = convert_diarization(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif arch.endswith("ForXVector" ):
SCREAMING_SNAKE_CASE_ : Any = convert_xvector(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}" )
if hf_config.use_weighted_layer_sum:
SCREAMING_SNAKE_CASE_ : Tuple = checkpoint["Featurizer"]["weights"]
hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_ )
hf_model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
snake_case_ = argparse.ArgumentParser()
parser.add_argument(
'--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
)
parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
snake_case_ = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 68 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
_A = "xmod"
def __init__( self , lowercase__=3_0522 , lowercase__=768 , lowercase__=12 , lowercase__=12 , lowercase__=3072 , lowercase__="gelu" , lowercase__=0.1 , lowercase__=0.1 , lowercase__=512 , lowercase__=2 , lowercase__=0.02 , lowercase__=1e-12 , lowercase__=1 , lowercase__=0 , lowercase__=2 , lowercase__="absolute" , lowercase__=True , lowercase__=None , lowercase__=False , lowercase__=2 , lowercase__=False , lowercase__=True , lowercase__=True , lowercase__=("en_XX",) , lowercase__=None , **lowercase__ , ):
"""simple docstring"""
super().__init__(pad_token_id=lowercase__ , bos_token_id=lowercase__ , eos_token_id=lowercase__ , **lowercase__ )
SCREAMING_SNAKE_CASE_ : Tuple = vocab_size
SCREAMING_SNAKE_CASE_ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE_ : Tuple = num_hidden_layers
SCREAMING_SNAKE_CASE_ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE_ : List[str] = hidden_act
SCREAMING_SNAKE_CASE_ : Optional[int] = intermediate_size
SCREAMING_SNAKE_CASE_ : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : List[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE_ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE_ : str = initializer_range
SCREAMING_SNAKE_CASE_ : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE_ : Tuple = position_embedding_type
SCREAMING_SNAKE_CASE_ : str = use_cache
SCREAMING_SNAKE_CASE_ : Optional[int] = classifier_dropout
SCREAMING_SNAKE_CASE_ : int = pre_norm
SCREAMING_SNAKE_CASE_ : Optional[int] = adapter_reduction_factor
SCREAMING_SNAKE_CASE_ : List[str] = adapter_layer_norm
SCREAMING_SNAKE_CASE_ : List[str] = adapter_reuse_layer_norm
SCREAMING_SNAKE_CASE_ : int = ln_before_adapter
SCREAMING_SNAKE_CASE_ : List[Any] = list(lowercase__ )
SCREAMING_SNAKE_CASE_ : Any = default_language
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
@property
def __lowerCamelCase ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE_ : Tuple = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE_ : Optional[int] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 68 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( UpperCamelCase__ , unittest.TestCase ):
UpperCamelCase = CanineTokenizer
UpperCamelCase = False
def A__ ( self :Tuple ):
'''simple docstring'''
super().setUp()
__magic_name__ : Optional[int] =CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def A__ ( self :Optional[Any] ):
'''simple docstring'''
return CanineTokenizer.from_pretrained("""google/canine-s""" )
def A__ ( self :Optional[int] , **__snake_case :Any ):
'''simple docstring'''
__magic_name__ : Any =self.tokenizer_class.from_pretrained(self.tmpdirname , **__snake_case )
__magic_name__ : Optional[int] =10_24
return tokenizer
@require_torch
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : str =self.canine_tokenizer
__magic_name__ : Any =["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
# fmt: off
__magic_name__ : Optional[int] =[5_73_44, 76, 1_05, 1_02, 1_01, 32, 1_05, 1_15, 32, 1_08, 1_05, 1_07, 1_01, 32, 97, 32, 98, 1_11, 1_20, 32, 1_11, 1_02, 32, 99, 1_04, 1_11, 99, 1_11, 1_08, 97, 1_16, 1_01, 1_15, 46, 5_73_45, 0, 0, 0, 0]
# fmt: on
__magic_name__ : Dict =tokenizer(__snake_case , padding=__snake_case , return_tensors="""pt""" )
self.assertIsInstance(__snake_case , __snake_case )
__magic_name__ : Optional[int] =list(batch.input_ids.numpy()[0] )
self.assertListEqual(__snake_case , __snake_case )
self.assertEqual((2, 39) , batch.input_ids.shape )
self.assertEqual((2, 39) , batch.attention_mask.shape )
@require_torch
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =self.canine_tokenizer
__magic_name__ : Optional[Any] =["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
__magic_name__ : int =tokenizer(__snake_case , padding=__snake_case , return_tensors="""pt""" )
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn("""input_ids""" , __snake_case )
self.assertIn("""attention_mask""" , __snake_case )
self.assertIn("""token_type_ids""" , __snake_case )
@require_torch
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : Dict =self.canine_tokenizer
__magic_name__ : List[Any] =[
"""What's the weater?""",
"""It's about 25 degrees.""",
]
__magic_name__ : Any =tokenizer(
text_target=__snake_case , max_length=32 , padding="""max_length""" , truncation=__snake_case , return_tensors="""pt""" )
self.assertEqual(32 , targets["""input_ids"""].shape[1] )
def A__ ( self :Any ):
'''simple docstring'''
__magic_name__ : Optional[Any] =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
__magic_name__ : Tuple =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
__magic_name__ : Any =tempfile.mkdtemp()
__magic_name__ : Union[str, Any] =""" He is very happy, UNwant\u00E9d,running"""
__magic_name__ : List[str] =tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
tokenizer.save_pretrained(__snake_case )
__magic_name__ : Optional[Any] =tokenizer.__class__.from_pretrained(__snake_case )
__magic_name__ : str =after_tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
self.assertListEqual(__snake_case , __snake_case )
shutil.rmtree(__snake_case )
__magic_name__ : int =self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
__magic_name__ : str =tempfile.mkdtemp()
__magic_name__ : Optional[int] =""" He is very happy, UNwant\u00E9d,running"""
__magic_name__ : Optional[Any] =tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
__magic_name__ : Optional[int] =chr(0xE_0_0_7 )
additional_special_tokens.append(__snake_case )
tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
__magic_name__ : List[Any] =tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
tokenizer.save_pretrained(__snake_case )
__magic_name__ : Optional[Any] =tokenizer.__class__.from_pretrained(__snake_case )
__magic_name__ : List[Any] =after_tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
self.assertListEqual(__snake_case , __snake_case )
self.assertIn(__snake_case , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
__magic_name__ : Optional[int] =tokenizer.__class__.from_pretrained(__snake_case , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(__snake_case )
def A__ ( self :Optional[Any] ):
'''simple docstring'''
__magic_name__ : Optional[int] =self.get_tokenizers(do_lower_case=__snake_case )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
__magic_name__ , __magic_name__ : List[str] =self.get_clean_sequence(__snake_case )
# a special token for Canine can be defined as follows:
__magic_name__ : Tuple =0xE_0_0_5
__magic_name__ : Tuple =chr(__snake_case )
tokenizer.add_special_tokens({"""cls_token""": special_token} )
__magic_name__ : Optional[int] =tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
self.assertEqual(len(__snake_case ) , 1 )
__magic_name__ : Any =tokenizer.decode(ids + encoded_special_token , clean_up_tokenization_spaces=__snake_case )
__magic_name__ : Union[str, Any] =tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
__magic_name__ : Optional[int] =tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
__magic_name__ : Union[str, Any] =tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
self.assertEqual(__snake_case , input_encoded + special_token_id )
__magic_name__ : List[str] =tokenizer.decode(__snake_case , skip_special_tokens=__snake_case )
self.assertTrue(special_token not in decoded )
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : Dict =self.get_tokenizers(do_lower_case=__snake_case )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
__magic_name__ : Tuple =chr(0xE_0_0_5 )
__magic_name__ : Union[str, Any] =chr(0xE_0_0_6 )
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1] , special_tokens=__snake_case )
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({"""additional_special_tokens""": [SPECIAL_TOKEN_2]} )
__magic_name__ : List[Any] =tokenizer.tokenize(__snake_case )
__magic_name__ : Union[str, Any] =tokenizer.tokenize(__snake_case )
self.assertEqual(len(__snake_case ) , 1 )
self.assertEqual(len(__snake_case ) , 1 )
self.assertEqual(token_a[0] , __snake_case )
self.assertEqual(token_a[0] , __snake_case )
@require_tokenizers
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : Dict =self.get_tokenizers(do_lower_case=__snake_case )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# a special token for Canine can be defined as follows:
__magic_name__ : Dict =0xE_0_0_6
__magic_name__ : Tuple =chr(__snake_case )
__magic_name__ : str =AddedToken(__snake_case , lstrip=__snake_case )
tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]} )
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(__snake_case )
tokenizer.from_pretrained(__snake_case )
def A__ ( self :int ):
'''simple docstring'''
__magic_name__ : str =[]
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(__snake_case )
with open(os.path.join(__snake_case , """special_tokens_map.json""" ) , encoding="""utf-8""" ) as json_file:
__magic_name__ : List[Any] =json.load(__snake_case )
with open(os.path.join(__snake_case , """tokenizer_config.json""" ) , encoding="""utf-8""" ) as json_file:
__magic_name__ : str =json.load(__snake_case )
# a special token for Canine can be defined as follows:
__magic_name__ : int =0xE_0_0_6
__magic_name__ : List[str] =chr(__snake_case )
__magic_name__ : Union[str, Any] =[new_token_a]
__magic_name__ : List[Any] =[new_token_a]
with open(os.path.join(__snake_case , """special_tokens_map.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(__snake_case , __snake_case )
with open(os.path.join(__snake_case , """tokenizer_config.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(__snake_case , __snake_case )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
__magic_name__ : Union[str, Any] =tokenizer_class.from_pretrained(__snake_case , extra_ids=0 )
self.assertIn(__snake_case , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) ) , )
__magic_name__ : str =0xE_0_0_7
__magic_name__ : Optional[int] =chr(__snake_case )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
__magic_name__ : List[Any] =[AddedToken(__snake_case , lstrip=__snake_case )]
__magic_name__ : str =tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , extra_ids=0 )
self.assertIn(__snake_case , tokenizer.additional_special_tokens )
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) )
@require_tokenizers
def A__ ( self :str ):
'''simple docstring'''
__magic_name__ : List[str] =self.get_tokenizers(do_lower_case=__snake_case )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
__magic_name__ : Dict ="""hello world"""
if self.space_between_special_tokens:
__magic_name__ : Dict ="""[CLS] hello world [SEP]"""
else:
__magic_name__ : int =input
__magic_name__ : Any =tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
__magic_name__ : List[Any] =tokenizer.decode(__snake_case , spaces_between_special_tokens=self.space_between_special_tokens )
self.assertIn(__snake_case , [output, output.lower()] )
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : str =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
__magic_name__ : str =[
"""bos_token""",
"""eos_token""",
"""unk_token""",
"""sep_token""",
"""pad_token""",
"""cls_token""",
"""mask_token""",
]
__magic_name__ : Union[str, Any] ="""a"""
__magic_name__ : int =ord(__snake_case )
for attr in attributes_list:
setattr(__snake_case , attr + """_id""" , __snake_case )
self.assertEqual(getattr(__snake_case , __snake_case ) , __snake_case )
self.assertEqual(getattr(__snake_case , attr + """_id""" ) , __snake_case )
setattr(__snake_case , attr + """_id""" , __snake_case )
self.assertEqual(getattr(__snake_case , __snake_case ) , __snake_case )
self.assertEqual(getattr(__snake_case , attr + """_id""" ) , __snake_case )
setattr(__snake_case , """additional_special_tokens_ids""" , [] )
self.assertListEqual(getattr(__snake_case , """additional_special_tokens""" ) , [] )
self.assertListEqual(getattr(__snake_case , """additional_special_tokens_ids""" ) , [] )
__magic_name__ : Optional[int] =0xE_0_0_6
__magic_name__ : Any =chr(__snake_case )
setattr(__snake_case , """additional_special_tokens_ids""" , [additional_special_token_id] )
self.assertListEqual(getattr(__snake_case , """additional_special_tokens""" ) , [additional_special_token] )
self.assertListEqual(getattr(__snake_case , """additional_special_tokens_ids""" ) , [additional_special_token_id] )
def A__ ( self :int ):
'''simple docstring'''
pass
def A__ ( self :str ):
'''simple docstring'''
pass
def A__ ( self :str ):
'''simple docstring'''
pass
def A__ ( self :Tuple ):
'''simple docstring'''
pass
def A__ ( self :Tuple ):
'''simple docstring'''
pass
def A__ ( self :Any ):
'''simple docstring'''
pass
def A__ ( self :Optional[int] ):
'''simple docstring'''
pass
def A__ ( self :int ):
'''simple docstring'''
pass
| 21 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_snake_case = collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_snake_case = """https://storage.googleapis.com/cvdf-datasets/mnist/"""
def _A ( __magic_name__ ):
lowercase__ = numpy.dtype(numpy.uintaa ).newbyteorder(">" )
return numpy.frombuffer(bytestream.read(4 ) , dtype=__magic_name__ )[0]
@deprecated(__magic_name__ , "Please use tf.data to implement this functionality." )
def _A ( __magic_name__ ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__magic_name__ ) as bytestream:
lowercase__ = _readaa(__magic_name__ )
if magic != 2051:
raise ValueError(
"Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = bytestream.read(rows * cols * num_images )
lowercase__ = numpy.frombuffer(__magic_name__ , dtype=numpy.uinta )
lowercase__ = data.reshape(__magic_name__ , __magic_name__ , __magic_name__ , 1 )
return data
@deprecated(__magic_name__ , "Please use tf.one_hot on tensors." )
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = labels_dense.shape[0]
lowercase__ = numpy.arange(__magic_name__ ) * num_classes
lowercase__ = numpy.zeros((num_labels, num_classes) )
lowercase__ = 1
return labels_one_hot
@deprecated(__magic_name__ , "Please use tf.data to implement this functionality." )
def _A ( __magic_name__ , __magic_name__=False , __magic_name__=10 ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__magic_name__ ) as bytestream:
lowercase__ = _readaa(__magic_name__ )
if magic != 2049:
raise ValueError(
"Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = bytestream.read(__magic_name__ )
lowercase__ = numpy.frombuffer(__magic_name__ , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(__magic_name__ , __magic_name__ )
return labels
class lowerCAmelCase :
@deprecated(
_lowercase , "Please use alternatives such as official/mnist/_DataSet.py"
" from tensorflow/models." , )
def __init__( self :List[str] , _lowercase :Optional[Any] , _lowercase :Union[str, Any] , _lowercase :Tuple=False , _lowercase :str=False , _lowercase :Dict=dtypes.floataa , _lowercase :Optional[Any]=True , _lowercase :Any=None , ):
'''simple docstring'''
lowercase__ , lowercase__ = random_seed.get_seed(_lowercase )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
lowercase__ = dtypes.as_dtype(_lowercase ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype )
if fake_data:
lowercase__ = 1_00_00
lowercase__ = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
lowercase__ = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
lowercase__ = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
lowercase__ = images.astype(numpy.floataa )
lowercase__ = numpy.multiply(_lowercase , 1.0 / 255.0 )
lowercase__ = images
lowercase__ = labels
lowercase__ = 0
lowercase__ = 0
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return self._images
@property
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
return self._labels
@property
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
return self._num_examples
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return self._epochs_completed
def UpperCAmelCase ( self :str , _lowercase :Union[str, Any] , _lowercase :Any=False , _lowercase :Union[str, Any]=True ):
'''simple docstring'''
if fake_data:
lowercase__ = [1] * 7_84
lowercase__ = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(_lowercase )],
[fake_label for _ in range(_lowercase )],
)
lowercase__ = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
lowercase__ = numpy.arange(self._num_examples )
numpy.random.shuffle(_lowercase )
lowercase__ = self.images[perma]
lowercase__ = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
lowercase__ = self._num_examples - start
lowercase__ = self._images[start : self._num_examples]
lowercase__ = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
lowercase__ = numpy.arange(self._num_examples )
numpy.random.shuffle(_lowercase )
lowercase__ = self.images[perm]
lowercase__ = self.labels[perm]
# Start next epoch
lowercase__ = 0
lowercase__ = batch_size - rest_num_examples
lowercase__ = self._index_in_epoch
lowercase__ = self._images[start:end]
lowercase__ = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
lowercase__ = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(__magic_name__ , "Please write your own downloading logic." )
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
if not gfile.Exists(__magic_name__ ):
gfile.MakeDirs(__magic_name__ )
lowercase__ = os.path.join(__magic_name__ , __magic_name__ )
if not gfile.Exists(__magic_name__ ):
urllib.request.urlretrieve(__magic_name__ , __magic_name__ ) # noqa: S310
with gfile.GFile(__magic_name__ ) as f:
lowercase__ = f.size()
print("Successfully downloaded" , __magic_name__ , __magic_name__ , "bytes." )
return filepath
@deprecated(
__magic_name__ , "Please use alternatives such as:" " tensorflow_datasets.load('mnist')" )
def _A ( __magic_name__ , __magic_name__=False , __magic_name__=False , __magic_name__=dtypes.floataa , __magic_name__=True , __magic_name__=5000 , __magic_name__=None , __magic_name__=DEFAULT_SOURCE_URL , ):
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=__magic_name__ , one_hot=__magic_name__ , dtype=__magic_name__ , seed=__magic_name__ )
lowercase__ = fake()
lowercase__ = fake()
lowercase__ = fake()
return _Datasets(train=__magic_name__ , validation=__magic_name__ , test=__magic_name__ )
if not source_url: # empty string check
lowercase__ = DEFAULT_SOURCE_URL
lowercase__ = "train-images-idx3-ubyte.gz"
lowercase__ = "train-labels-idx1-ubyte.gz"
lowercase__ = "t10k-images-idx3-ubyte.gz"
lowercase__ = "t10k-labels-idx1-ubyte.gz"
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + train_images_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_images(__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + train_labels_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_labels(__magic_name__ , one_hot=__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + test_images_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_images(__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + test_labels_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_labels(__magic_name__ , one_hot=__magic_name__ )
if not 0 <= validation_size <= len(__magic_name__ ):
lowercase__ = (
"Validation size should be between 0 and "
f'''{len(__magic_name__ )}. Received: {validation_size}.'''
)
raise ValueError(__magic_name__ )
lowercase__ = train_images[:validation_size]
lowercase__ = train_labels[:validation_size]
lowercase__ = train_images[validation_size:]
lowercase__ = train_labels[validation_size:]
lowercase__ = {"dtype": dtype, "reshape": reshape, "seed": seed}
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
return _Datasets(train=__magic_name__ , validation=__magic_name__ , test=__magic_name__ )
| 655 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowercase_ = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
'''processing_trocr''': ['''TrOCRProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TrOCRForCausalLM''',
'''TrOCRPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 336 |
def lowerCAmelCase ( UpperCAmelCase ) ->list[int]:
"""simple docstring"""
__magic_name__ : Optional[int] = len(UpperCAmelCase )
for i in range(UpperCAmelCase ):
for j in range(i + 1, UpperCAmelCase ):
if numbers[j] < numbers[i]:
__magic_name__ , __magic_name__ : Dict = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
lowercase_ = input('''Enter numbers separated by a comma:\n''').strip()
lowercase_ = [int(item) for item in user_input.split(''',''')]
print(exchange_sort(unsorted))
| 336 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
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''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->str:
"""simple docstring"""
for attribute in key.split('''.''' ):
lowercase : Union[str, Any] = getattr(_UpperCamelCase, _UpperCamelCase )
if weight_type is not None:
lowercase : Union[str, Any] = getattr(_UpperCamelCase, _UpperCamelCase ).shape
else:
lowercase : List[Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
lowercase : str = value
elif weight_type == "weight_g":
lowercase : Optional[Any] = value
elif weight_type == "weight_v":
lowercase : Any = value
elif weight_type == "bias":
lowercase : Union[str, Any] = value
else:
lowercase : List[str] = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Optional[int]:
"""simple docstring"""
lowercase : int = []
lowercase : str = fairseq_model.state_dict()
lowercase : Any = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
lowercase : Optional[int] = False
if "conv_layers" in name:
load_conv_layer(
_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, hf_model.config.feat_extract_norm == '''group''', )
lowercase : Dict = True
else:
for key, mapped_key in MAPPING.items():
lowercase : Tuple = '''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
if key in name or (key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0] and not is_finetuned):
lowercase : List[str] = True
if "*" in mapped_key:
lowercase : str = name.split(_UpperCamelCase )[0].split('''.''' )[-2]
lowercase : Optional[int] = mapped_key.replace('''*''', _UpperCamelCase )
if "weight_g" in name:
lowercase : Union[str, Any] = '''weight_g'''
elif "weight_v" in name:
lowercase : Optional[Any] = '''weight_v'''
elif "weight" in name:
lowercase : Optional[int] = '''weight'''
elif "bias" in name:
lowercase : List[Any] = '''bias'''
else:
lowercase : List[Any] = None
set_recursively(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
continue
if not is_used:
unused_weights.append(_UpperCamelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Dict:
"""simple docstring"""
lowercase : str = full_name.split('''conv_layers.''' )[-1]
lowercase : str = name.split('''.''' )
lowercase : Dict = int(items[0] )
lowercase : Any = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
lowercase : int = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
lowercase : Optional[int] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
lowercase : Dict = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
lowercase : Dict = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_UpperCamelCase )
@torch.no_grad()
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase=None, _UpperCamelCase=None, _UpperCamelCase=True ) ->int:
"""simple docstring"""
if config_path is not None:
lowercase : Optional[Any] = HubertConfig.from_pretrained(_UpperCamelCase )
else:
lowercase : int = HubertConfig()
if is_finetuned:
if dict_path:
lowercase : Any = Dictionary.load(_UpperCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowercase : List[str] = target_dict.pad_index
lowercase : Dict = target_dict.bos_index
lowercase : List[str] = target_dict.eos_index
lowercase : int = len(target_dict.symbols )
lowercase : Any = os.path.join(_UpperCamelCase, '''vocab.json''' )
if not os.path.isdir(_UpperCamelCase ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_UpperCamelCase ) )
return
os.makedirs(_UpperCamelCase, exist_ok=_UpperCamelCase )
with open(_UpperCamelCase, '''w''', encoding='''utf-8''' ) as vocab_handle:
json.dump(target_dict.indices, _UpperCamelCase )
lowercase : Dict = WavaVecaCTCTokenizer(
_UpperCamelCase, unk_token=target_dict.unk_word, pad_token=target_dict.pad_word, bos_token=target_dict.bos_word, eos_token=target_dict.eos_word, word_delimiter_token='''|''', do_lower_case=_UpperCamelCase, )
lowercase : Tuple = True if config.feat_extract_norm == '''layer''' else False
lowercase : Optional[Any] = WavaVecaFeatureExtractor(
feature_size=1, sampling_rate=16000, padding_value=0, do_normalize=_UpperCamelCase, return_attention_mask=_UpperCamelCase, )
lowercase : Any = WavaVecaProcessor(feature_extractor=_UpperCamelCase, tokenizer=_UpperCamelCase )
processor.save_pretrained(_UpperCamelCase )
lowercase : Optional[Any] = HubertForCTC(_UpperCamelCase )
else:
lowercase : Tuple = HubertModel(_UpperCamelCase )
if is_finetuned:
lowercase , lowercase , lowercase : Optional[int] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path], arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
lowercase , lowercase , lowercase : int = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
lowercase : str = model[0].eval()
recursively_load_weights(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
hf_wavavec.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--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_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 319 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {'''vocab_file''': '''vocab.txt'''}
__a = {
'''vocab_file''': {
'''facebook/esm2_t6_8M_UR50D''': '''https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt''',
'''facebook/esm2_t12_35M_UR50D''': '''https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt''',
},
}
__a = {
'''facebook/esm2_t6_8M_UR50D''': 10_24,
'''facebook/esm2_t12_35M_UR50D''': 10_24,
}
def __lowercase ( _UpperCamelCase ) ->Tuple:
"""simple docstring"""
with open(_UpperCamelCase, '''r''' ) as f:
lowercase : List[Any] = f.read().splitlines()
return [l.strip() for l in lines]
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Dict = VOCAB_FILES_NAMES
A : List[str] = PRETRAINED_VOCAB_FILES_MAP
A : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A : List[str] = ['input_ids', 'attention_mask']
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="<unk>" , SCREAMING_SNAKE_CASE__="<cls>" , SCREAMING_SNAKE_CASE__="<pad>" , SCREAMING_SNAKE_CASE__="<mask>" , SCREAMING_SNAKE_CASE__="<eos>" , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
lowercase : str = load_vocab_file(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = dict(enumerate(self.all_tokens ) )
lowercase : Tuple = {tok: ind for ind, tok in enumerate(self.all_tokens )}
lowercase : Tuple = unk_token
lowercase : Optional[Any] = cls_token
lowercase : Union[str, Any] = pad_token
lowercase : Dict = mask_token
lowercase : Dict = eos_token
lowercase : Any = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return self._id_to_token.get(SCREAMING_SNAKE_CASE__ , self.unk_token )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return self._token_to_id.get(SCREAMING_SNAKE_CASE__ , self._token_to_id.get(self.unk_token ) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
return text.split()
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__=False ):
return len(self._id_to_token )
def __lowerCamelCase ( self ):
return {token: i for i, token in enumerate(self.all_tokens )}
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return self._token_to_id.get(SCREAMING_SNAKE_CASE__ , self._token_to_id.get(self.unk_token ) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return self._id_to_token.get(SCREAMING_SNAKE_CASE__ , self.unk_token )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : List[str] = [self.cls_token_id]
lowercase : Dict = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!''' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
lowercase : Tuple = [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]
if token_ids_a is not None:
mask += [0] * len(SCREAMING_SNAKE_CASE__ ) + [1]
return mask
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : List[str] = os.path.join(SCREAMING_SNAKE_CASE__ , (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''' )
with open(SCREAMING_SNAKE_CASE__ , '''w''' ) as f:
f.write('''\n'''.join(self.all_tokens ) )
return (vocab_file,)
@property
def __lowerCamelCase ( self ):
return self.get_vocab_size(with_added_tokens=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = False ):
return super()._add_tokens(SCREAMING_SNAKE_CASE__ , special_tokens=SCREAMING_SNAKE_CASE__ )
| 319 | 1 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
SCREAMING_SNAKE_CASE_ = True
except (ImportError, AttributeError):
SCREAMING_SNAKE_CASE_ = object
def __lowercase ( *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
pass
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = logging.get_logger("""transformers-cli/serving""")
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
return ServeCommand(_SCREAMING_SNAKE_CASE , args.host , args.port , args.workers )
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : dict
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : List[str]
__snake_case : Optional[List[int]]
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : str
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Any
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : ArgumentParser ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parser.add_parser(
"""serve""" ,help="""CLI tool to run inference requests through REST and GraphQL endpoints.""" )
serve_parser.add_argument(
"""--task""" ,type=lowerCamelCase__ ,choices=get_supported_tasks() ,help="""The task to run the pipeline on""" ,)
serve_parser.add_argument("""--host""" ,type=lowerCamelCase__ ,default="""localhost""" ,help="""Interface the server will listen on.""" )
serve_parser.add_argument("""--port""" ,type=lowerCamelCase__ ,default=8888 ,help="""Port the serving will listen to.""" )
serve_parser.add_argument("""--workers""" ,type=lowerCamelCase__ ,default=1 ,help="""Number of http workers""" )
serve_parser.add_argument("""--model""" ,type=lowerCamelCase__ ,help="""Model's name or path to stored model.""" )
serve_parser.add_argument("""--config""" ,type=lowerCamelCase__ ,help="""Model's config name or path to stored model.""" )
serve_parser.add_argument("""--tokenizer""" ,type=lowerCamelCase__ ,help="""Tokenizer name to use.""" )
serve_parser.add_argument(
"""--device""" ,type=lowerCamelCase__ ,default=-1 ,help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)""" ,)
serve_parser.set_defaults(func=lowerCamelCase__ )
def __init__( self : List[str] ,lowerCamelCase__ : Pipeline ,lowerCamelCase__ : str ,lowerCamelCase__ : int ,lowerCamelCase__ : int ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = pipeline
SCREAMING_SNAKE_CASE = host
SCREAMING_SNAKE_CASE = port
SCREAMING_SNAKE_CASE = workers
if not _serve_dependencies_installed:
raise RuntimeError(
"""Using serve command requires FastAPI and uvicorn. """
"""Please install transformers with [serving]: pip install \"transformers[serving]\"."""
"""Or install FastAPI and uvicorn separately.""" )
else:
logger.info(F"""Serving model over {host}:{port}""" )
SCREAMING_SNAKE_CASE = FastAPI(
routes=[
APIRoute(
"""/""" ,self.model_info ,response_model=lowerCamelCase__ ,response_class=lowerCamelCase__ ,methods=["""GET"""] ,),
APIRoute(
"""/tokenize""" ,self.tokenize ,response_model=lowerCamelCase__ ,response_class=lowerCamelCase__ ,methods=["""POST"""] ,),
APIRoute(
"""/detokenize""" ,self.detokenize ,response_model=lowerCamelCase__ ,response_class=lowerCamelCase__ ,methods=["""POST"""] ,),
APIRoute(
"""/forward""" ,self.forward ,response_model=lowerCamelCase__ ,response_class=lowerCamelCase__ ,methods=["""POST"""] ,),
] ,timeout=600 ,)
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> str:
'''simple docstring'''
run(self._app ,host=self.host ,port=self.port ,workers=self.workers )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : str = Body(lowerCamelCase__ ,embed=lowerCamelCase__ ) ,lowerCamelCase__ : bool = Body(lowerCamelCase__ ,embed=lowerCamelCase__ ) ) -> Any:
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE = self._pipeline.tokenizer.tokenize(lowerCamelCase__ )
if return_ids:
SCREAMING_SNAKE_CASE = self._pipeline.tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
return ServeTokenizeResult(tokens=lowerCamelCase__ ,tokens_ids=lowerCamelCase__ )
else:
return ServeTokenizeResult(tokens=lowerCamelCase__ )
except Exception as e:
raise HTTPException(status_code=500 ,detail={"""model""": """""", """error""": str(lowerCamelCase__ )} )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ,lowerCamelCase__ : List[int] = Body(lowerCamelCase__ ,embed=lowerCamelCase__ ) ,lowerCamelCase__ : bool = Body(lowerCamelCase__ ,embed=lowerCamelCase__ ) ,lowerCamelCase__ : bool = Body(lowerCamelCase__ ,embed=lowerCamelCase__ ) ,) -> int:
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE = self._pipeline.tokenizer.decode(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
return ServeDeTokenizeResult(model="""""" ,text=lowerCamelCase__ )
except Exception as e:
raise HTTPException(status_code=500 ,detail={"""model""": """""", """error""": str(lowerCamelCase__ )} )
async def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : str=Body(lowerCamelCase__ ,embed=lowerCamelCase__ ) ) -> Optional[Any]:
'''simple docstring'''
if len(lowerCamelCase__ ) == 0:
return ServeForwardResult(output=[] ,attention=[] )
try:
# Forward through the model
SCREAMING_SNAKE_CASE = self._pipeline(lowerCamelCase__ )
return ServeForwardResult(output=lowerCamelCase__ )
except Exception as e:
raise HTTPException(500 ,{"""error""": str(lowerCamelCase__ )} )
| 116 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : int ,*lowerCamelCase__ : int ,**lowerCamelCase__ : List[Any] ) -> None:
'''simple docstring'''
warnings.warn(
"""The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use DeformableDetrImageProcessor instead.""" ,lowerCamelCase__ ,)
super().__init__(*lowerCamelCase__ ,**lowerCamelCase__ )
| 116 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Union[str, Any] ,lowerCAmelCase_ : str ,lowerCAmelCase_ : List[str] = "x" ,lowerCAmelCase_ : Tuple = 10**-10 ,lowerCAmelCase_ : Optional[int] = 1 ,) -> complex:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] =symbols(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =lambdify(lowerCAmelCase_ ,lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] =lambdify(lowerCAmelCase_ ,diff(lowerCAmelCase_ ,lowerCAmelCase_ ) )
SCREAMING_SNAKE_CASE_ : int =starting_point
while True:
if diff_function(lowerCAmelCase_ ) != 0:
SCREAMING_SNAKE_CASE_ : Dict =prev_guess - multiplicity * func(lowerCAmelCase_ ) / diff_function(
lowerCAmelCase_ )
else:
raise ZeroDivisionError('Could not find root' ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
SCREAMING_SNAKE_CASE_ : Union[str, Any] =next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")
# Find root of polynomial
# Find fourth Root of 5
print(f"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}""")
# Find value of e
print(
'The root of log(y) - 1 = 0 is ',
f"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
)
# Exponential Roots
print(
'The root of exp(x) - 1 = 0 is',
f"""{newton_raphson('exp(x) - 1', 10, precision=0.005)}""",
)
# Find root of cos(x)
print(f"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")
| 220 |
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
lowerCamelCase_ : int = 16
lowerCamelCase_ : str = 32
def A__ ( lowerCamelCase ) -> int:
return int(x / 2**20 )
class _UpperCamelCase :
'''simple docstring'''
def __enter__( self : int ):
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
UpperCamelCase_: str = torch.cuda.memory_allocated()
return self
def __exit__( self : List[Any] , *snake_case_ : Union[str, Any] ):
gc.collect()
torch.cuda.empty_cache()
UpperCamelCase_: List[str] = torch.cuda.memory_allocated()
UpperCamelCase_: int = torch.cuda.max_memory_allocated()
UpperCamelCase_: Optional[int] = bamb(self.end - self.begin )
UpperCamelCase_: Tuple = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def A__ ( lowerCamelCase , lowerCamelCase = 16 , lowerCamelCase = "bert-base-cased" , lowerCamelCase = 3_20 , lowerCamelCase = 1_60 , ) -> Dict:
UpperCamelCase_: str = AutoTokenizer.from_pretrained(lowerCamelCase )
UpperCamelCase_: List[str] = load_dataset(
"""glue""" , """mrpc""" , split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''} )
def tokenize_function(lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase_: Optional[Any] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase , max_length=lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCamelCase_: Dict = datasets.map(
lowerCamelCase , batched=lowerCamelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=lowerCamelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase_: int = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase , padding="""max_length""" , max_length=1_28 , return_tensors="""pt""" )
return tokenizer.pad(lowerCamelCase , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
UpperCamelCase_: List[str] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase )
UpperCamelCase_: Optional[Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase )
return train_dataloader, eval_dataloader
def A__ ( lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
# Initialize accelerator
UpperCamelCase_: Any = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase_: str = config["""lr"""]
UpperCamelCase_: Any = int(config["""num_epochs"""] )
UpperCamelCase_: Tuple = int(config["""seed"""] )
UpperCamelCase_: Dict = int(config["""batch_size"""] )
UpperCamelCase_: Dict = args.model_name_or_path
set_seed(lowerCamelCase )
UpperCamelCase_, UpperCamelCase_: int = get_dataloaders(lowerCamelCase , lowerCamelCase , lowerCamelCase , args.n_train , args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase_: Any = AutoModelForSequenceClassification.from_pretrained(lowerCamelCase , return_dict=lowerCamelCase )
# Instantiate optimizer
UpperCamelCase_: Tuple = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
UpperCamelCase_: List[Any] = optimizer_cls(params=model.parameters() , lr=lowerCamelCase )
if accelerator.state.deepspeed_plugin is not None:
UpperCamelCase_: Any = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
UpperCamelCase_: List[Any] = 1
UpperCamelCase_: Optional[int] = (len(lowerCamelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
UpperCamelCase_: List[Any] = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase , num_warmup_steps=0 , num_training_steps=lowerCamelCase , )
else:
UpperCamelCase_: Dict = DummyScheduler(lowerCamelCase , total_num_steps=lowerCamelCase , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_: str = accelerator.prepare(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# We need to keep track of how many total steps we have iterated over
UpperCamelCase_: str = 0
# We also need to keep track of the stating epoch so files are named properly
UpperCamelCase_: Dict = 0
# Now we train the model
UpperCamelCase_: Dict = {}
for epoch in range(lowerCamelCase , lowerCamelCase ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(lowerCamelCase ):
UpperCamelCase_: Union[str, Any] = model(**lowerCamelCase )
UpperCamelCase_: str = outputs.loss
UpperCamelCase_: List[str] = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin ) ) )
accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used ) )
accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked ) )
accelerator.print(
"""Total Peak Memory consumed during the train (max): {}""".format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
UpperCamelCase_: Optional[Any] = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , """peak_memory_utilization.json""" ) , """w""" ) as f:
json.dump(lowerCamelCase , lowerCamelCase )
def A__ ( ) -> Optional[Any]:
UpperCamelCase_: Any = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=lowerCamelCase , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=lowerCamelCase , )
parser.add_argument(
"""--output_dir""" , type=lowerCamelCase , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--peak_memory_upper_bound""" , type=lowerCamelCase , default=lowerCamelCase , help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.""" , )
parser.add_argument(
"""--n_train""" , type=lowerCamelCase , default=3_20 , help="""Number of training examples to use.""" , )
parser.add_argument(
"""--n_val""" , type=lowerCamelCase , default=1_60 , help="""Number of validation examples to use.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowerCamelCase , default=1 , help="""Number of train epochs.""" , )
UpperCamelCase_: Optional[int] = parser.parse_args()
UpperCamelCase_: Union[str, Any] = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(lowerCamelCase , lowerCamelCase )
if __name__ == "__main__":
main()
| 548 | 0 |
def __lowerCamelCase ( __a :str ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
A__ = sorted(string.lower() )
return len(__a ) == len(set(__a ) )
if __name__ == "__main__":
A : Union[str, Any] = input('''Enter a string ''').strip()
A : str = is_isogram(input_str)
print(F'''{input_str} is {"an" if isogram else "not an"} isogram.''')
| 706 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def __lowerCamelCase ( __a :Dict ) -> List[Any]:
"""simple docstring"""
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def __lowerCamelCase ( __a :str ) -> int:
"""simple docstring"""
A__ = create_tensor(__a )
A__ = gather(__a )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def __lowerCamelCase ( __a :Optional[Any] ) -> Optional[int]:
"""simple docstring"""
A__ = [state.process_index]
A__ = gather_object(__a )
assert len(__a ) == state.num_processes, F'{gathered_obj}, {len(__a )} != {state.num_processes}'
assert gathered_obj == list(range(state.num_processes ) ), F'{gathered_obj} != {list(range(state.num_processes ) )}'
def __lowerCamelCase ( __a :Optional[int] ) -> Dict:
"""simple docstring"""
A__ = create_tensor(__a )
A__ = broadcast(__a )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def __lowerCamelCase ( __a :List[str] ) -> Tuple:
"""simple docstring"""
if state.is_main_process:
A__ = torch.arange(state.num_processes + 1 ).to(state.device )
else:
A__ = torch.arange(state.num_processes ).to(state.device )
A__ = pad_across_processes(__a )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def __lowerCamelCase ( __a :Optional[int] ) -> Tuple:
"""simple docstring"""
if state.num_processes != 2:
return
A__ = create_tensor(__a )
A__ = reduce(__a , """sum""" )
A__ = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(__a , __a ), F'{reduced_tensor} != {truth_tensor}'
def __lowerCamelCase ( __a :str ) -> List[str]:
"""simple docstring"""
if state.num_processes != 2:
return
A__ = create_tensor(__a )
A__ = reduce(__a , """mean""" )
A__ = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(__a , __a ), F'{reduced_tensor} != {truth_tensor}'
def __lowerCamelCase ( __a :List[Any] ) -> Union[str, Any]:
"""simple docstring"""
main()
def __lowerCamelCase ( ) -> List[str]:
"""simple docstring"""
A__ = PartialState()
state.print(F'State: {state}' )
state.print("""testing gather""" )
test_gather(__a )
state.print("""testing gather_object""" )
test_gather_object(__a )
state.print("""testing broadcast""" )
test_broadcast(__a )
state.print("""testing pad_across_processes""" )
test_pad_across_processes(__a )
state.print("""testing reduce_sum""" )
test_reduce_sum(__a )
state.print("""testing reduce_mean""" )
test_reduce_mean(__a )
if __name__ == "__main__":
main()
| 247 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCamelCase_ = 42
UpperCamelCase_ = None
UpperCamelCase_ = None
def lowercase_ ( ) -> Node | None:
"""simple docstring"""
lowercase : Tuple =Node(1 )
lowercase : List[Any] =Node(2 )
lowercase : str =Node(3 )
lowercase : Union[str, Any] =Node(4 )
lowercase : List[Any] =Node(5 )
return tree
def lowercase_ ( __A : Node | None ) -> list[int]:
"""simple docstring"""
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def lowercase_ ( __A : Node | None ) -> list[int]:
"""simple docstring"""
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def lowercase_ ( __A : Node | None ) -> list[int]:
"""simple docstring"""
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def lowercase_ ( __A : Node | None ) -> int:
"""simple docstring"""
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def lowercase_ ( __A : Node | None ) -> Sequence[Node | None]:
"""simple docstring"""
lowercase : list[Any] =[]
if root is None:
return output
lowercase : Dict =deque([root] )
while process_queue:
lowercase : int =process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def lowercase_ ( __A : Node | None , __A : int ) -> Sequence[Node | None]:
"""simple docstring"""
lowercase : list[Any] =[]
def populate_output(__A : Node | None , __A : int ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(__A , __A )
return output
def lowercase_ ( __A : Node | None , __A : int ) -> Sequence[Node | None]:
"""simple docstring"""
lowercase : list[Any] =[]
def populate_output(__A : Node | None , __A : int ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(__A , __A )
return output
def lowercase_ ( __A : Node | None ) -> Sequence[Node | None] | list[Any]:
"""simple docstring"""
if root is None:
return []
lowercase : list[Sequence[Node | None]] =[]
lowercase : str =0
lowercase : int =height(__A )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(__A , __A ) )
lowercase : Optional[Any] =1
else:
output.append(get_nodes_from_right_to_left(__A , __A ) )
lowercase : Dict =0
return output
def lowercase_ ( ) -> None: # Main function for testing.
"""simple docstring"""
lowercase : int =make_tree()
print(F'In-order Traversal: {inorder(__A )}' )
print(F'Pre-order Traversal: {preorder(__A )}' )
print(F'Post-order Traversal: {postorder(__A )}' , '''\n''' )
print(F'Height of Tree: {height(__A )}' , '''\n''' )
print('''Complete Level Order Traversal: ''' )
print(level_order(__A ) , '''\n''' )
print('''Level-wise order Traversal: ''' )
for level in range(1 , height(__A ) + 1 ):
print(F'Level {level}:' , get_nodes_from_left_to_right(__A , level=__A ) )
print('''\nZigZag order Traversal: ''' )
print(zigzag(__A ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 94 |
from importlib import import_module
from .logging import get_logger
SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_logger(__name__)
class UpperCAmelCase_ :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=None ):
UpperCAmelCase__ : List[str] = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("""__""" ):
setattr(self , _lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) )
UpperCAmelCase__ : Tuple = module._original_module if isinstance(_lowerCAmelCase , _PatchedModuleObj ) else module
class UpperCAmelCase_ :
__lowerCamelCase = []
def __init__( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=None ):
UpperCAmelCase__ : str = obj
UpperCAmelCase__ : List[str] = target
UpperCAmelCase__ : List[str] = new
UpperCAmelCase__ : Any = target.split(""".""" )[0]
UpperCAmelCase__ : Union[str, Any] = {}
UpperCAmelCase__ : str = attrs or []
def __enter__( self ):
*UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = self.target.split(""".""" )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(_lowerCAmelCase ) ):
try:
UpperCAmelCase__ : Optional[int] = import_module(""".""".join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
UpperCAmelCase__ : Any = getattr(self.obj , _lowerCAmelCase )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(_lowerCAmelCase , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
UpperCAmelCase__ : List[Any] = obj_attr
# patch at top level
setattr(self.obj , _lowerCAmelCase , _PatchedModuleObj(_lowerCAmelCase , attrs=self.attrs ) )
UpperCAmelCase__ : Optional[Any] = getattr(self.obj , _lowerCAmelCase )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(_lowerCAmelCase , _lowerCAmelCase , _PatchedModuleObj(getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , attrs=self.attrs ) )
UpperCAmelCase__ : Union[str, Any] = getattr(_lowerCAmelCase , _lowerCAmelCase )
# finally set the target attribute
setattr(_lowerCAmelCase , _lowerCAmelCase , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
UpperCAmelCase__ : Union[str, Any] = getattr(import_module(""".""".join(_lowerCAmelCase ) ) , _lowerCAmelCase )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , _lowerCAmelCase ) is attr_value:
UpperCAmelCase__ : Optional[int] = getattr(self.obj , _lowerCAmelCase )
setattr(self.obj , _lowerCAmelCase , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
UpperCAmelCase__ : Dict = globals()["""__builtins__"""][target_attr]
setattr(self.obj , _lowerCAmelCase , self.new )
else:
raise RuntimeError(f"Tried to patch attribute {target_attr} instead of a submodule." )
def __exit__( self , *_lowerCAmelCase ):
for attr in list(self.original ):
setattr(self.obj , _lowerCAmelCase , self.original.pop(_lowerCAmelCase ) )
def __UpperCAmelCase ( self ):
self.__enter__()
self._active_patches.append(self )
def __UpperCAmelCase ( self ):
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 79 | 0 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
while a != 0:
UpperCamelCase_, UpperCamelCase_: Dict = b % a, a
return b
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
if gcd(lowerCamelCase , lowerCamelCase ) != 1:
UpperCamelCase_: str = F'''mod inverse of {a!r} and {m!r} does not exist'''
raise ValueError(lowerCamelCase )
UpperCamelCase_, UpperCamelCase_, UpperCamelCase_: List[str] = 1, 0, a
UpperCamelCase_, UpperCamelCase_, UpperCamelCase_: Dict = 0, 1, m
while va != 0:
UpperCamelCase_: List[str] = ua // va
UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_: List[str] = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 670 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : str ):
UpperCamelCase_: Optional[int] = logging.get_logger()
# the current default level is logging.WARNING
UpperCamelCase_: Dict = logging.get_verbosity()
logging.set_verbosity_error()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_warning()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_info()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_debug()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
# restore to the original level
logging.set_verbosity(snake_case_ )
def lowerCAmelCase__ ( self : Optional[int] ):
UpperCamelCase_: Union[str, Any] = logging.get_verbosity()
UpperCamelCase_: int = logging.get_logger("""transformers.models.bart.tokenization_bart""" )
UpperCamelCase_: Union[str, Any] = """Testing 1, 2, 3"""
# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
if level_origin <= logging.WARNING:
with CaptureLogger(snake_case_ ) as cl:
logger.warning(snake_case_ )
self.assertEqual(cl.out , msg + """\n""" )
# this is setting the level for all of `transformers.*` loggers
logging.set_verbosity_error()
# should not be able to log warnings
with CaptureLogger(snake_case_ ) as cl:
logger.warning(snake_case_ )
self.assertEqual(cl.out , """""" )
# should be able to log warnings again
logging.set_verbosity_warning()
with CaptureLogger(snake_case_ ) as cl:
logger.warning(snake_case_ )
self.assertEqual(cl.out , msg + """\n""" )
# restore to the original level
logging.set_verbosity(snake_case_ )
@mockenv(TRANSFORMERS_VERBOSITY="""error""" )
def lowerCAmelCase__ ( self : Optional[int] ):
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
# this action activates the env var
UpperCamelCase_: List[str] = logging.get_logger("""transformers.models.bart.tokenization_bart""" )
UpperCamelCase_: str = os.getenv("""TRANSFORMERS_VERBOSITY""" , snake_case_ )
UpperCamelCase_: Any = logging.log_levels[env_level_str]
UpperCamelCase_: Dict = logging.get_verbosity()
self.assertEqual(
snake_case_ , snake_case_ , f'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}''' , )
# restore to the original level
UpperCamelCase_: str = """"""
transformers.utils.logging._reset_library_root_logger()
@mockenv(TRANSFORMERS_VERBOSITY="""super-error""" )
def lowerCAmelCase__ ( self : List[Any] ):
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
UpperCamelCase_: str = logging.logging.getLogger()
with CaptureLogger(snake_case_ ) as cl:
# this action activates the env var
logging.get_logger("""transformers.models.bart.tokenization_bart""" )
self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error""" , cl.out )
# no need to restore as nothing was changed
def lowerCAmelCase__ ( self : List[Any] ):
# testing `logger.warning_advice()`
transformers.utils.logging._reset_library_root_logger()
UpperCamelCase_: List[str] = logging.get_logger("""transformers.models.bart.tokenization_bart""" )
UpperCamelCase_: Any = """Testing 1, 2, 3"""
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1""" ):
# nothing should be logged as env var disables this method
with CaptureLogger(snake_case_ ) as cl:
logger.warning_advice(snake_case_ )
self.assertEqual(cl.out , """""" )
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""""" ):
# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
with CaptureLogger(snake_case_ ) as cl:
logger.warning_advice(snake_case_ )
self.assertEqual(cl.out , msg + """\n""" )
def A__ ( ) -> Union[str, Any]:
disable_progress_bar()
assert are_progress_bars_disabled()
enable_progress_bar()
assert not are_progress_bars_disabled()
| 670 | 1 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = 3
lowerCamelCase__ = 2_50
lowerCamelCase__ = ids_tensor((batch_size, length) ,_lowerCAmelCase )
lowerCamelCase__ = torch.ones((batch_size, length) ,device=_lowerCAmelCase ,dtype=torch.float ) / length
return input_ids, scores
def UpperCamelCase_ ( self ):
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(5 )
lowerCamelCase__ = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=10 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(9 )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(10 )
self.assertTrue(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = MaxLengthCriteria(max_length=10 )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(5 )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(9 )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(10 )
self.assertTrue(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = MaxNewTokensCriteria(start_length=5 ,max_new_tokens=5 )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(5 )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(9 )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(10 )
self.assertTrue(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length ,10 )
def UpperCamelCase_ ( self ):
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(5 )
lowerCamelCase__ = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ = MaxTimeCriteria(max_time=0.1 ,initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) ,10 )
with self.assertWarns(_lowerCAmelCase ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) ,11 )
lowerCamelCase__ = validate_stopping_criteria(StoppingCriteriaList() ,11 )
self.assertEqual(len(_lowerCAmelCase ) ,1 )
| 50 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from .np_formatter import NumpyFormatter
lowerCAmelCase__ : Tuple =logging.get_logger(__name__)
lowerCAmelCase__ : Dict[Optional[str], Type[Formatter]] ={}
lowerCAmelCase__ : Dict[Optional[str], str] ={}
lowerCAmelCase__ : Dict[Optional[str], Exception] ={}
def __lowercase ( a__ , a__ , a__ = None , ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = aliases if aliases is not None else []
if format_type in _FORMAT_TYPES:
logger.warning(
f"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})""" )
__SCREAMING_SNAKE_CASE = formatter_cls
for alias in set(aliases + [format_type] ):
if alias in _FORMAT_TYPES_ALIASES:
logger.warning(
f"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})""" )
__SCREAMING_SNAKE_CASE = format_type
def __lowercase ( a__ , a__ , a__ = None ) -> List[str]:
__SCREAMING_SNAKE_CASE = aliases if aliases is not None else []
for alias in set(aliases + [format_type] ):
__SCREAMING_SNAKE_CASE = unavailable_error
# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')
if config.TORCH_AVAILABLE:
from .torch_formatter import TorchFormatter
_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
lowerCAmelCase__ : List[Any] =ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])
if config.TF_AVAILABLE:
from .tf_formatter import TFFormatter
_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
lowerCAmelCase__ : Optional[Any] =ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])
if config.JAX_AVAILABLE:
from .jax_formatter import JaxFormatter
_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
lowerCAmelCase__ : Optional[int] =ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])
def __lowercase ( a__ ) -> Optional[str]:
if format_type in _FORMAT_TYPES_ALIASES:
return _FORMAT_TYPES_ALIASES[format_type]
else:
return format_type
def __lowercase ( a__ , **a__ ) -> Formatter:
__SCREAMING_SNAKE_CASE = get_format_type_from_alias(a__ )
if format_type in _FORMAT_TYPES:
return _FORMAT_TYPES[format_type](**a__ )
if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
else:
raise ValueError(
f"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None )}, but got '{format_type}'""" )
| 148 | 0 |
'''simple docstring'''
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 5_0 ) -> int:
'''simple docstring'''
A__ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 709 |
import collections
import importlib.util
import os
import re
from pathlib import Path
lowerCAmelCase__ = """src/transformers"""
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase__ = re.compile(R"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCAmelCase__ = re.compile(R"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
lowerCAmelCase__ = re.compile(R"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
lowerCAmelCase__ = re.compile(R"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCAmelCase__ = re.compile(R"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
lowerCAmelCase__ = re.compile("""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
lowerCAmelCase__ = re.compile("""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
lowerCAmelCase__ = re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
lowerCAmelCase__ = re.compile(R"""^\s*try:""")
# Catches a line with else:
lowerCAmelCase__ = re.compile(R"""^\s*else:""")
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any ) -> int:
'''simple docstring'''
if _re_test_backend.search(SCREAMING_SNAKE_CASE_ ) is None:
return None
A__ = [b[0] for b in _re_backend.findall(SCREAMING_SNAKE_CASE_ )]
backends.sort()
return "_and_".join(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" , newline="\n" ) as f:
A__ = f.readlines()
A__ = 0
while line_index < len(SCREAMING_SNAKE_CASE_ ) and not lines[line_index].startswith("_import_structure = {" ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(SCREAMING_SNAKE_CASE_ ):
return None
# First grab the objects without a specific backend in _import_structure
A__ = []
while not lines[line_index].startswith("if TYPE_CHECKING" ) and find_backend(lines[line_index] ) is None:
A__ = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(SCREAMING_SNAKE_CASE_ ):
A__ = _re_one_line_import_struct.search(SCREAMING_SNAKE_CASE_ ).groups()[0]
A__ = re.findall("\[([^\]]+)\]" , SCREAMING_SNAKE_CASE_ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(", " )] )
line_index += 1
continue
A__ = _re_import_struct_key_value.search(SCREAMING_SNAKE_CASE_ )
if single_line_import_search is not None:
A__ = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", " ) if len(SCREAMING_SNAKE_CASE_ ) > 0]
objects.extend(SCREAMING_SNAKE_CASE_ )
elif line.startswith(" " * 8 + "\"" ):
objects.append(line[9:-3] )
line_index += 1
A__ = {"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.
A__ = 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:
A__ = 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
A__ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(" " * 4 ):
A__ = lines[line_index]
if _re_import_struct_add_one.search(SCREAMING_SNAKE_CASE_ ) is not None:
objects.append(_re_import_struct_add_one.search(SCREAMING_SNAKE_CASE_ ).groups()[0] )
elif _re_import_struct_add_many.search(SCREAMING_SNAKE_CASE_ ) is not None:
A__ = _re_import_struct_add_many.search(SCREAMING_SNAKE_CASE_ ).groups()[0].split(", " )
A__ = [obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE_ ) > 0]
objects.extend(SCREAMING_SNAKE_CASE_ )
elif _re_between_brackets.search(SCREAMING_SNAKE_CASE_ ) is not None:
A__ = _re_between_brackets.search(SCREAMING_SNAKE_CASE_ ).groups()[0].split(", " )
A__ = [obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE_ ) > 0]
objects.extend(SCREAMING_SNAKE_CASE_ )
elif _re_quote_object.search(SCREAMING_SNAKE_CASE_ ) is not None:
objects.append(_re_quote_object.search(SCREAMING_SNAKE_CASE_ ).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
A__ = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
A__ = []
while (
line_index < len(SCREAMING_SNAKE_CASE_ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith("else" )
):
A__ = lines[line_index]
A__ = _re_import.search(SCREAMING_SNAKE_CASE_ )
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
A__ = {"none": objects}
# Let's continue with backend-specific objects
while line_index < len(SCREAMING_SNAKE_CASE_ ):
# If the line is an if is_backend_available, we grab all objects associated.
A__ = 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:
A__ = 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
A__ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(" " * 8 ):
A__ = lines[line_index]
A__ = _re_import.search(SCREAMING_SNAKE_CASE_ )
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
A__ = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict , SCREAMING_SNAKE_CASE_: List[Any] ) -> Optional[int]:
'''simple docstring'''
def find_duplicates(SCREAMING_SNAKE_CASE_: str ):
return [k for k, v in collections.Counter(SCREAMING_SNAKE_CASE_ ).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!"]
A__ = []
for key in import_dict_objects.keys():
A__ = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}' )
A__ = 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] ) ):
A__ = "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__ ( ) -> Dict:
'''simple docstring'''
A__ = []
for root, _, files in os.walk(SCREAMING_SNAKE_CASE_ ):
if "__init__.py" in files:
A__ = os.path.join(SCREAMING_SNAKE_CASE_ , "__init__.py" )
A__ = parse_init(SCREAMING_SNAKE_CASE_ )
if objects is not None:
A__ = analyze_results(*SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
A__ = F'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
failures.append("\n".join(SCREAMING_SNAKE_CASE_ ) )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
raise ValueError("\n\n".join(SCREAMING_SNAKE_CASE_ ) )
def lowerCAmelCase__ ( ) -> Optional[Any]:
'''simple docstring'''
A__ = []
for path, directories, files in os.walk(SCREAMING_SNAKE_CASE_ ):
for folder in directories:
# Ignore private modules
if folder.startswith("_" ):
directories.remove(SCREAMING_SNAKE_CASE_ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(SCREAMING_SNAKE_CASE_ ) / folder).glob("*.py" ) ) ) == 0:
continue
A__ = str((Path(SCREAMING_SNAKE_CASE_ ) / folder).relative_to(SCREAMING_SNAKE_CASE_ ) )
A__ = short_path.replace(os.path.sep , "." )
submodules.append(SCREAMING_SNAKE_CASE_ )
for fname in files:
if fname == "__init__.py":
continue
A__ = str((Path(SCREAMING_SNAKE_CASE_ ) / fname).relative_to(SCREAMING_SNAKE_CASE_ ) )
A__ = short_path.replace(".py" , "" ).replace(os.path.sep , "." )
if len(submodule.split("." ) ) == 1:
submodules.append(SCREAMING_SNAKE_CASE_ )
return submodules
lowerCAmelCase__ = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
]
def lowerCAmelCase__ ( ) -> Optional[int]:
'''simple docstring'''
A__ = importlib.util.spec_from_file_location(
"transformers" , os.path.join(SCREAMING_SNAKE_CASE_ , "__init__.py" ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
A__ = spec.loader.load_module()
A__ = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(SCREAMING_SNAKE_CASE_ ) > 0:
A__ = "\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()
| 626 | 0 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A__ ( __lowerCAmelCase : int = 5000 ):
lowerCamelCase__ = [(i * (3 * i - 1)) // 2 for i in range(1 , __lowerCAmelCase )]
for i, pentagonal_i in enumerate(__lowerCAmelCase ):
for j in range(__lowerCAmelCase , len(__lowerCAmelCase ) ):
lowerCamelCase__ = pentagonal_nums[j]
lowerCamelCase__ = pentagonal_i + pentagonal_j
lowerCamelCase__ = pentagonal_j - pentagonal_i
if is_pentagonal(__lowerCAmelCase ) and is_pentagonal(__lowerCAmelCase ):
return b
return -1
if __name__ == "__main__":
print(F'{solution() = }')
| 50 |
"""simple docstring"""
def UpperCamelCase ( _A ) -> int:
lowercase : Dict = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def UpperCamelCase ( _A = 100 ) -> int:
lowercase : Union[str, Any] = 1
lowercase : Tuple = 2
for i in range(2 , max_n + 1 ):
lowercase : Any = pre_numerator
lowercase : Dict = 2 * i // 3 if i % 3 == 0 else 1
lowercase : Optional[Any] = cur_numerator
lowercase : str = e_cont * pre_numerator + temp
return sum_digits(_A )
if __name__ == "__main__":
print(F'{solution() = }')
| 264 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase = """
Examples:
```py
>>> import torch
>>> import numpy as np
>>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
>>> from transformers import pipeline
>>> from diffusers.utils import load_image
>>> def make_hint(image, depth_estimator):
... image = depth_estimator(image)[\"depth\"]
... image = np.array(image)
... image = image[:, :, None]
... image = np.concatenate([image, image, image], axis=2)
... detected_map = torch.from_numpy(image).float() / 255.0
... hint = detected_map.permute(2, 0, 1)
... return hint
>>> depth_estimator = pipeline(\"depth-estimation\")
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
... )
>>> pipe_prior = pipe_prior.to(\"cuda\")
>>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
... )
>>> pipe = pipe.to(\"cuda\")
>>> img = load_image(
... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
... \"/kandinsky/cat.png\"
... ).resize((768, 768))
>>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")
>>> prompt = \"A robot, 4k photo\"
>>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"
>>> generator = torch.Generator(device=\"cuda\").manual_seed(43)
>>> image_emb, zero_image_emb = pipe_prior(
... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
... ).to_tuple()
>>> images = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... hint=hint,
... num_inference_steps=50,
... generator=generator,
... height=768,
... width=768,
... ).images
>>> images[0].save(\"robot_cat.png\")
```
"""
def A_ ( __a : str , __a : int , __a : Dict=8 ):
"""simple docstring"""
a__ = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
a__ = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class __snake_case ( SCREAMING_SNAKE_CASE):
'''simple docstring'''
def __init__( self , a_ , a_ , a_ , ):
super().__init__()
self.register_modules(
unet=__UpperCamelCase , scheduler=__UpperCamelCase , movq=__UpperCamelCase , )
a__ = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def _a ( self , a_ , a_ , a_ , a_ , a_ , a_ ):
if latents is None:
a__ = randn_tensor(__UpperCamelCase , generator=__UpperCamelCase , device=__UpperCamelCase , dtype=__UpperCamelCase )
else:
if latents.shape != shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
a__ = latents.to(__UpperCamelCase )
a__ = latents * scheduler.init_noise_sigma
return latents
def _a ( self , a_=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
a__ = torch.device(F'''cuda:{gpu_id}''' )
a__ = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__UpperCamelCase , __UpperCamelCase )
def _a ( self , a_=0 ):
if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
a__ = torch.device(F'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("""cpu""" , silence_dtype_warnings=__UpperCamelCase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
a__ = None
for cpu_offloaded_model in [self.unet, self.movq]:
a__ , a__ = cpu_offload_with_hook(__UpperCamelCase , __UpperCamelCase , prev_module_hook=__UpperCamelCase )
# We'll offload the last model manually.
a__ = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _a ( self ):
if not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(__UpperCamelCase , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__UpperCamelCase )
def __call__( self , a_ , a_ , a_ , a_ = 512 , a_ = 512 , a_ = 100 , a_ = 4.0 , a_ = 1 , a_ = None , a_ = None , a_ = "pil" , a_ = True , ):
a__ = self._execution_device
a__ = guidance_scale > 1.0
if isinstance(__UpperCamelCase , __UpperCamelCase ):
a__ = torch.cat(__UpperCamelCase , dim=0 )
if isinstance(__UpperCamelCase , __UpperCamelCase ):
a__ = torch.cat(__UpperCamelCase , dim=0 )
if isinstance(__UpperCamelCase , __UpperCamelCase ):
a__ = torch.cat(__UpperCamelCase , dim=0 )
a__ = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
a__ = image_embeds.repeat_interleave(__UpperCamelCase , dim=0 )
a__ = negative_image_embeds.repeat_interleave(__UpperCamelCase , dim=0 )
a__ = hint.repeat_interleave(__UpperCamelCase , dim=0 )
a__ = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=__UpperCamelCase )
a__ = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=__UpperCamelCase )
self.scheduler.set_timesteps(__UpperCamelCase , device=__UpperCamelCase )
a__ = self.scheduler.timesteps
a__ = self.movq.config.latent_channels
a__ , a__ = downscale_height_and_width(__UpperCamelCase , __UpperCamelCase , self.movq_scale_factor )
# create initial latent
a__ = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , self.scheduler , )
for i, t in enumerate(self.progress_bar(__UpperCamelCase ) ):
# expand the latents if we are doing classifier free guidance
a__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
a__ = {"""image_embeds""": image_embeds, """hint""": hint}
a__ = self.unet(
sample=__UpperCamelCase , timestep=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , added_cond_kwargs=__UpperCamelCase , return_dict=__UpperCamelCase , )[0]
if do_classifier_free_guidance:
a__ , a__ = noise_pred.split(latents.shape[1] , dim=1 )
a__ , a__ = noise_pred.chunk(2 )
a__ , a__ = variance_pred.chunk(2 )
a__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
a__ = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , """variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
a__ , a__ = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
a__ = self.scheduler.step(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , generator=__UpperCamelCase , )[0]
# post-processing
a__ = self.movq.decode(__UpperCamelCase , force_not_quantize=__UpperCamelCase )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
a__ = image * 0.5 + 0.5
a__ = image.clamp(0 , 1 )
a__ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
a__ = self.numpy_to_pil(__UpperCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__UpperCamelCase )
| 715 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
UpperCAmelCase = 16
UpperCAmelCase = 32
def A_ ( __a : Accelerator , __a : int = 16 ):
"""simple docstring"""
a__ = AutoTokenizer.from_pretrained("""bert-base-cased""" )
a__ = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(__a : int ):
# max_length=None => use the model max length (it's actually the default)
a__ = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=__a , max_length=__a )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
a__ = datasets.map(
__a , batched=__a , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
a__ = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(__a : Optional[int] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
a__ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
a__ = 16
elif accelerator.mixed_precision != "no":
a__ = 8
else:
a__ = None
return tokenizer.pad(
__a , padding="""longest""" , max_length=__a , pad_to_multiple_of=__a , return_tensors="""pt""" , )
# Instantiate dataloaders.
a__ = DataLoader(
tokenized_datasets["""train"""] , shuffle=__a , collate_fn=__a , batch_size=__a )
a__ = DataLoader(
tokenized_datasets["""validation"""] , shuffle=__a , collate_fn=__a , batch_size=__a )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
UpperCAmelCase = mocked_dataloaders # noqa: F811
def A_ ( __a : List[Any] , __a : Tuple ):
"""simple docstring"""
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , __a ) == "1":
a__ = 2
# New Code #
a__ = int(args.gradient_accumulation_steps )
a__ = int(args.local_sgd_steps )
# Initialize accelerator
a__ = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=__a )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
a__ = config["""lr"""]
a__ = int(config["""num_epochs"""] )
a__ = int(config["""seed"""] )
a__ = int(config["""batch_size"""] )
a__ = evaluate.load("""glue""" , """mrpc""" )
set_seed(__a )
a__ , a__ = get_dataloaders(__a , __a )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
a__ = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=__a )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
a__ = model.to(accelerator.device )
# Instantiate optimizer
a__ = AdamW(params=model.parameters() , lr=__a )
# Instantiate scheduler
a__ = get_linear_schedule_with_warmup(
optimizer=__a , num_warmup_steps=100 , num_training_steps=(len(__a ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
a__ , a__ , a__ , a__ , a__ = accelerator.prepare(
__a , __a , __a , __a , __a )
# Now we train the model
for epoch in range(__a ):
model.train()
with LocalSGD(
accelerator=__a , model=__a , local_sgd_steps=__a , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(__a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(__a ):
a__ = model(**__a )
a__ = output.loss
accelerator.backward(__a )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(__a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
a__ = model(**__a )
a__ = outputs.logits.argmax(dim=-1 )
a__ , a__ = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=__a , references=__a , )
a__ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , __a )
def A_ ( ):
"""simple docstring"""
a__ = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=__a , default=__a , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" , type=__a , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
parser.add_argument(
"""--local_sgd_steps""" , type=__a , default=8 , help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
a__ = parser.parse_args()
a__ = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(__a , __a )
if __name__ == "__main__":
main()
| 351 | 0 |
"""simple docstring"""
import math
class __A :
'''simple docstring'''
def __init__( self : List[Any] ,_snake_case : Optional[int]=0 ) -> List[str]: # a graph with Node 0,1,...,N-1
"""simple docstring"""
lowercase__ : Optional[Any] = n
lowercase__ : Tuple = [
[math.inf for j in range(0 ,_snake_case )] for i in range(0 ,_snake_case )
] # adjacency matrix for weight
lowercase__ : List[Any] = [
[math.inf for j in range(0 ,_snake_case )] for i in range(0 ,_snake_case )
] # dp[i][j] stores minimum distance from i to j
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Any ,_snake_case : Union[str, Any] ,_snake_case : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = w
def UpperCAmelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
for k in range(0 ,self.n ):
for i in range(0 ,self.n ):
for j in range(0 ,self.n ):
lowercase__ : int = min(self.dp[i][j] ,self.dp[i][k] + self.dp[k][j] )
def UpperCAmelCase ( self : Dict ,_snake_case : Optional[int] ,_snake_case : Tuple ) -> Tuple:
"""simple docstring"""
return self.dp[u][v]
if __name__ == "__main__":
lowerCAmelCase_ = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 560 |
"""simple docstring"""
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCAmelCase_ = data_utils.TransfoXLTokenizer
lowerCAmelCase_ = data_utils.TransfoXLCorpus
lowerCAmelCase_ = data_utils
lowerCAmelCase_ = data_utils
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(__lowerCamelCase , '''rb''' ) as fp:
lowercase__ : Dict = pickle.load(__lowerCamelCase , encoding='''latin1''' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
lowercase__ : int = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
print(f"""Save vocabulary to {pytorch_vocab_dump_path}""" )
lowercase__ : List[Any] = corpus.vocab.__dict__
torch.save(__lowerCamelCase , __lowerCamelCase )
lowercase__ : int = corpus.__dict__
corpus_dict_no_vocab.pop('''vocab''' , __lowerCamelCase )
lowercase__ : List[str] = pytorch_dump_folder_path + '''/''' + CORPUS_NAME
print(f"""Save dataset to {pytorch_dataset_dump_path}""" )
torch.save(__lowerCamelCase , __lowerCamelCase )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
lowercase__ : Tuple = os.path.abspath(__lowerCamelCase )
lowercase__ : List[Any] = os.path.abspath(__lowerCamelCase )
print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.""" )
# Initialise PyTorch model
if transfo_xl_config_file == "":
lowercase__ : Tuple = TransfoXLConfig()
else:
lowercase__ : List[str] = TransfoXLConfig.from_json_file(__lowerCamelCase )
print(f"""Building PyTorch model from configuration: {config}""" )
lowercase__ : Union[str, Any] = TransfoXLLMHeadModel(__lowerCamelCase )
lowercase__ : List[Any] = load_tf_weights_in_transfo_xl(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# Save pytorch-model
lowercase__ : Optional[int] = os.path.join(__lowerCamelCase , __lowerCamelCase )
lowercase__ : Dict = os.path.join(__lowerCamelCase , __lowerCamelCase )
print(f"""Save PyTorch model to {os.path.abspath(__lowerCamelCase )}""" )
torch.save(model.state_dict() , __lowerCamelCase )
print(f"""Save configuration file to {os.path.abspath(__lowerCamelCase )}""" )
with open(__lowerCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=True,
help='Path to the folder to store the PyTorch model or dataset/vocab.',
)
parser.add_argument(
'--tf_checkpoint_path',
default='',
type=str,
help='An optional path to a TensorFlow checkpoint path to be converted.',
)
parser.add_argument(
'--transfo_xl_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained BERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--transfo_xl_dataset_file',
default='',
type=str,
help='An optional dataset file to be converted in a vocabulary.',
)
lowerCAmelCase_ = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 560 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Any = logging.get_logger(__name__)
__lowercase : List[Any] = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class _A ( _UpperCAmelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = '''wav2vec2'''
def __init__( self : Any , A_ : str=32 , A_ : Dict=768 , A_ : int=12 , A_ : Tuple=12 , A_ : Optional[Any]=3_072 , A_ : List[Any]="gelu" , A_ : int=0.1 , A_ : Any=0.1 , A_ : Optional[int]=0.1 , A_ : Optional[Any]=0.0 , A_ : Union[str, Any]=0.0 , A_ : List[Any]=0.1 , A_ : List[Any]=0.1 , A_ : Union[str, Any]=0.02 , A_ : List[str]=1E-5 , A_ : Optional[int]="group" , A_ : str="gelu" , A_ : Union[str, Any]=(512, 512, 512, 512, 512, 512, 512) , A_ : Union[str, Any]=(5, 2, 2, 2, 2, 2, 2) , A_ : int=(10, 3, 3, 3, 3, 2, 2) , A_ : str=False , A_ : Union[str, Any]=128 , A_ : str=16 , A_ : str=False , A_ : str=True , A_ : List[Any]=0.05 , A_ : List[Any]=10 , A_ : Any=2 , A_ : Optional[int]=0.0 , A_ : Dict=10 , A_ : Optional[int]=0 , A_ : int=320 , A_ : Optional[int]=2 , A_ : Tuple=0.1 , A_ : Optional[Any]=100 , A_ : Tuple=256 , A_ : Dict=256 , A_ : Union[str, Any]=0.1 , A_ : Tuple="sum" , A_ : Any=False , A_ : List[str]=False , A_ : Union[str, Any]=256 , A_ : Optional[Any]=(512, 512, 512, 512, 1_500) , A_ : List[Any]=(5, 3, 3, 1, 1) , A_ : Dict=(1, 2, 3, 1, 1) , A_ : Tuple=512 , A_ : List[str]=0 , A_ : Any=1 , A_ : Dict=2 , A_ : Optional[Any]=False , A_ : List[Any]=3 , A_ : str=2 , A_ : Tuple=3 , A_ : Dict=None , A_ : Tuple=None , **A_ : Any , ) -> Union[str, Any]:
super().__init__(**A_ , pad_token_id=A_ , bos_token_id=A_ , eos_token_id=A_ )
__snake_case = hidden_size
__snake_case = feat_extract_norm
__snake_case = feat_extract_activation
__snake_case = list(A_ )
__snake_case = list(A_ )
__snake_case = list(A_ )
__snake_case = conv_bias
__snake_case = num_conv_pos_embeddings
__snake_case = num_conv_pos_embedding_groups
__snake_case = len(self.conv_dim )
__snake_case = num_hidden_layers
__snake_case = intermediate_size
__snake_case = hidden_act
__snake_case = num_attention_heads
__snake_case = hidden_dropout
__snake_case = attention_dropout
__snake_case = activation_dropout
__snake_case = feat_proj_dropout
__snake_case = final_dropout
__snake_case = layerdrop
__snake_case = layer_norm_eps
__snake_case = initializer_range
__snake_case = vocab_size
__snake_case = do_stable_layer_norm
__snake_case = use_weighted_layer_sum
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
__snake_case = apply_spec_augment
__snake_case = mask_time_prob
__snake_case = mask_time_length
__snake_case = mask_time_min_masks
__snake_case = mask_feature_prob
__snake_case = mask_feature_length
__snake_case = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
__snake_case = num_codevectors_per_group
__snake_case = num_codevector_groups
__snake_case = contrastive_logits_temperature
__snake_case = feat_quantizer_dropout
__snake_case = num_negatives
__snake_case = codevector_dim
__snake_case = proj_codevector_dim
__snake_case = diversity_loss_weight
# ctc loss
__snake_case = ctc_loss_reduction
__snake_case = ctc_zero_infinity
# adapter
__snake_case = add_adapter
__snake_case = adapter_kernel_size
__snake_case = adapter_stride
__snake_case = num_adapter_layers
__snake_case = output_hidden_size or hidden_size
__snake_case = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__snake_case = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__snake_case = list(A_ )
__snake_case = list(A_ )
__snake_case = list(A_ )
__snake_case = xvector_output_dim
@property
def lowercase ( self : Optional[Any] ) -> str:
return functools.reduce(operator.mul , self.conv_stride , 1 ) | 93 | """simple docstring"""
import qiskit
def SCREAMING_SNAKE_CASE ( snake_case, snake_case):
__snake_case = qiskit.Aer.get_backend('''aer_simulator''')
# Create a Quantum Circuit acting on the q register
__snake_case = qiskit.QuantumCircuit(snake_case, snake_case)
# Map the quantum measurement to the classical bits
circuit.measure([0], [0])
# Execute the circuit on the simulator
__snake_case = qiskit.execute(snake_case, snake_case, shots=10_00)
# Return the histogram data of the results of the experiment.
return job.result().get_counts(snake_case)
if __name__ == "__main__":
print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""") | 93 | 1 |
"""simple docstring"""
import re
def lowerCamelCase ( _snake_case ):
if len(re.findall('[ATCG]' ,_snake_case ) ) != len(_snake_case ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' ,'TAGC' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 110 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __UpperCAmelCase ( __UpperCamelCase ):
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
__lowercase : Any = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
__lowercase : Dict = key.replace('''heads.cmd.mim_head.cls.predictions''' , '''mmm_image_head''' )
__lowercase : Dict = key.replace('''heads.cmd.mlm_head.cls.predictions''' , '''mmm_text_head''' )
__lowercase : Dict = key.replace('''heads.cmd.itm_head.cls''' , '''itm_head''' )
__lowercase : Tuple = key.replace('''heads.cmd.itm_head.pooler''' , '''itm_head.pooler''' )
__lowercase : Dict = key.replace('''heads.cmd.clip_head.logit_scale''' , '''flava.logit_scale''' )
__lowercase : Optional[int] = key.replace('''heads.fairseq_mlm.cls.predictions''' , '''mlm_head''' )
__lowercase : Optional[int] = key.replace('''heads.imagenet.mim_head.cls.predictions''' , '''mim_head''' )
__lowercase : Union[str, Any] = key.replace('''mm_text_projection''' , '''flava.text_to_mm_projection''' )
__lowercase : str = key.replace('''mm_image_projection''' , '''flava.image_to_mm_projection''' )
__lowercase : Dict = key.replace('''image_encoder.module''' , '''flava.image_model''' )
__lowercase : str = key.replace('''text_encoder.module''' , '''flava.text_model''' )
__lowercase : Dict = key.replace('''mm_encoder.module.encoder.cls_token''' , '''flava.multimodal_model.cls_token''' )
__lowercase : Union[str, Any] = key.replace('''mm_encoder.module''' , '''flava.multimodal_model''' )
__lowercase : List[str] = key.replace('''text_projection''' , '''flava.text_projection''' )
__lowercase : Any = key.replace('''image_projection''' , '''flava.image_projection''' )
__lowercase : Tuple = value.float()
for key, value in codebook_state_dict.items():
__lowercase : int = value
return upgrade
@torch.no_grad()
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None ):
if config_path is not None:
__lowercase : Union[str, Any] = FlavaConfig.from_pretrained(__UpperCamelCase )
else:
__lowercase : Union[str, Any] = FlavaConfig()
__lowercase : Any = FlavaForPreTraining(__UpperCamelCase ).eval()
__lowercase : Any = convert_dalle_checkpoint(__UpperCamelCase , __UpperCamelCase , save_checkpoint=__UpperCamelCase )
if os.path.exists(__UpperCamelCase ):
__lowercase : Optional[Any] = torch.load(__UpperCamelCase , map_location='''cpu''' )
else:
__lowercase : List[Any] = torch.hub.load_state_dict_from_url(__UpperCamelCase , map_location='''cpu''' )
__lowercase : Optional[int] = upgrade_state_dict(__UpperCamelCase , __UpperCamelCase )
hf_model.load_state_dict(__UpperCamelCase )
__lowercase : Union[str, Any] = hf_model.state_dict()
__lowercase : Optional[Any] = count_parameters(__UpperCamelCase )
__lowercase : List[Any] = count_parameters(__UpperCamelCase ) + count_parameters(__UpperCamelCase )
assert torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 )
hf_model.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
a_ = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 76 | 0 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 100 * 2**20, 900 * 2**20] )
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> List[str]:
"""simple docstring"""
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , '''IN_MEMORY_MAX_SIZE''' , UpperCAmelCase )
lowerCamelCase__ : Dict = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
lowerCamelCase__ : int = dataset_size < in_memory_max_size
else:
lowerCamelCase__ : List[str] = False
lowerCamelCase__ : str = is_small_dataset(UpperCAmelCase )
assert result == expected
| 700 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
def __init__( self : Tuple , A : str = "▁" , A : bool = True , A : Union[str, AddedToken] = "<unk>" , A : Union[str, AddedToken] = "</s>" , A : Union[str, AddedToken] = "<pad>" , ) ->Optional[int]:
lowerCamelCase__ : str = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
lowerCamelCase__ : Optional[int] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
lowerCamelCase__ : Optional[Any] = token_dict['''token''']
lowerCamelCase__ : int = Tokenizer(Unigram() )
lowerCamelCase__ : List[Any] = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ),
normalizers.Lowercase(),
] )
lowerCamelCase__ : Dict = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=A , add_prefix_space=A ),
pre_tokenizers.Digits(individual_digits=A ),
pre_tokenizers.Punctuation(),
] )
lowerCamelCase__ : Optional[int] = decoders.Metaspace(replacement=A , add_prefix_space=A )
lowerCamelCase__ : Any = TemplateProcessing(
single=F"$A {self.special_tokens['eos']['token']}" , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , )
lowerCamelCase__ : List[str] = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(A , A )
def __lowerCamelCase ( self : List[str] , A : Union[str, List[str]] , A : int = 8_0_0_0 , A : bool = True , ) ->Optional[int]:
lowerCamelCase__ : Optional[int] = trainers.UnigramTrainer(
vocab_size=A , special_tokens=self.special_tokens_list , show_progress=A , )
if isinstance(A , A ):
lowerCamelCase__ : Union[str, Any] = [files]
self._tokenizer.train(A , trainer=A )
self.add_unk_id()
def __lowerCamelCase ( self : Union[str, Any] , A : Union[Iterator[str], Iterator[Iterator[str]]] , A : int = 8_0_0_0 , A : bool = True , ) ->List[Any]:
lowerCamelCase__ : str = trainers.UnigramTrainer(
vocab_size=A , special_tokens=self.special_tokens_list , show_progress=A , )
self._tokenizer.train_from_iterator(A , trainer=A )
self.add_unk_id()
def __lowerCamelCase ( self : int ) ->Union[str, Any]:
lowerCamelCase__ : Union[str, Any] = json.loads(self._tokenizer.to_str() )
lowerCamelCase__ : str = self.special_tokens['''unk''']['''id''']
lowerCamelCase__ : List[Any] = Tokenizer.from_str(json.dumps(A ) )
| 130 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=snake_case )
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : str = field(default='''image-classification''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
__lowerCamelCase : ClassVar[Features] = Features({'''image''': Image()} )
__lowerCamelCase : ClassVar[Features] = Features({'''labels''': ClassLabel} )
__lowerCamelCase : str = "image"
__lowerCamelCase : str = "labels"
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if self.label_column not in features:
raise ValueError(F"""Column {self.label_column} is not present in features.""" )
if not isinstance(features[self.label_column] ,SCREAMING_SNAKE_CASE_ ):
raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""" )
snake_case : Any = copy.deepcopy(self )
snake_case : List[str] = self.label_schema.copy()
snake_case : Union[str, Any] = features[self.label_column]
snake_case : List[str] = label_schema
return task_template
@property
def snake_case_ ( self ):
'''simple docstring'''
return {
self.image_column: "image",
self.label_column: "labels",
}
| 36 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class A__ :
"""simple docstring"""
def __init__( self: Union[str, Any] )-> List[str]:
lowerCamelCase : Optional[int] = {}
def a__ ( self: Any , __a: int , __a: List[Any] , __a: Optional[int]=1 )-> Optional[Any]:
if self.graph.get(__a ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
lowerCamelCase : Tuple = [[w, v]]
if not self.graph.get(__a ):
lowerCamelCase : Optional[Any] = []
def a__ ( self: str )-> str:
return list(self.graph )
def a__ ( self: Any , __a: int , __a: Any )-> int:
if self.graph.get(__a ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(__a )
def a__ ( self: Optional[int] , __a: str=-2 , __a: Optional[Any]=-1 )-> int:
if s == d:
return []
lowerCamelCase : Union[str, Any] = []
lowerCamelCase : str = []
if s == -2:
lowerCamelCase : List[Any] = list(self.graph )[0]
stack.append(__a )
visited.append(__a )
lowerCamelCase : int = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase : Optional[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(__a )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase : Any = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(__a ) != 0:
lowerCamelCase : int = stack[len(__a ) - 1]
else:
lowerCamelCase : Dict = ss
# check if se have reached the starting point
if len(__a ) == 0:
return visited
def a__ ( self: str , __a: str=-1 )-> Optional[Any]:
if c == -1:
lowerCamelCase : List[str] = floor(random() * 10_000 ) + 10
for i in range(__a ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
lowerCamelCase : Optional[int] = floor(random() * c ) + 1
if n != i:
self.add_pair(__a , __a , 1 )
def a__ ( self: Any , __a: int=-2 )-> List[str]:
lowerCamelCase : List[Any] = deque()
lowerCamelCase : List[str] = []
if s == -2:
lowerCamelCase : str = list(self.graph )[0]
d.append(__a )
visited.append(__a )
while d:
lowerCamelCase : Dict = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def a__ ( self: Tuple , __a: Union[str, Any] )-> Union[str, Any]:
lowerCamelCase : Optional[int] = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def a__ ( self: Optional[Any] , __a: Any )-> Optional[int]:
return len(self.graph[u] )
def a__ ( self: Optional[int] , __a: Tuple=-2 )-> List[Any]:
lowerCamelCase : Any = []
lowerCamelCase : Optional[Any] = []
if s == -2:
lowerCamelCase : List[Any] = list(self.graph )[0]
stack.append(__a )
visited.append(__a )
lowerCamelCase : Tuple = s
lowerCamelCase : List[Any] = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase : List[str] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase : List[str] = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(__a ) != 0:
lowerCamelCase : Dict = stack[len(__a ) - 1]
else:
lowerCamelCase : Tuple = ss
# check if se have reached the starting point
if len(__a ) == 0:
return sorted_nodes
def a__ ( self: Dict )-> Tuple:
lowerCamelCase : Any = []
lowerCamelCase : Union[str, Any] = []
lowerCamelCase : List[str] = list(self.graph )[0]
stack.append(__a )
visited.append(__a )
lowerCamelCase : Union[str, Any] = -2
lowerCamelCase : Optional[int] = []
lowerCamelCase : Optional[int] = s
lowerCamelCase : str = False
lowerCamelCase : Optional[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase : Any = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCamelCase : List[Any] = len(__a ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase : Union[str, Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCamelCase : Optional[int] = True
if len(__a ) != 0:
lowerCamelCase : Tuple = stack[len(__a ) - 1]
else:
lowerCamelCase : List[str] = False
indirect_parents.append(__a )
lowerCamelCase : Any = s
lowerCamelCase : int = ss
# check if se have reached the starting point
if len(__a ) == 0:
return list(__a )
def a__ ( self: Any )-> int:
lowerCamelCase : str = []
lowerCamelCase : Any = []
lowerCamelCase : List[Any] = list(self.graph )[0]
stack.append(__a )
visited.append(__a )
lowerCamelCase : Any = -2
lowerCamelCase : Optional[int] = []
lowerCamelCase : Tuple = s
lowerCamelCase : Tuple = False
lowerCamelCase : Tuple = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase : Union[str, Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCamelCase : str = len(__a ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase : Optional[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCamelCase : List[Any] = True
if len(__a ) != 0:
lowerCamelCase : List[str] = stack[len(__a ) - 1]
else:
lowerCamelCase : str = False
indirect_parents.append(__a )
lowerCamelCase : Any = s
lowerCamelCase : List[str] = ss
# check if se have reached the starting point
if len(__a ) == 0:
return False
def a__ ( self: Optional[int] , __a: Tuple=-2 , __a: List[Any]=-1 )-> Optional[Any]:
lowerCamelCase : Union[str, Any] = time()
self.dfs(__a , __a )
lowerCamelCase : Tuple = time()
return end - begin
def a__ ( self: List[str] , __a: Optional[Any]=-2 )-> List[Any]:
lowerCamelCase : str = time()
self.bfs(__a )
lowerCamelCase : Tuple = time()
return end - begin
class A__ :
"""simple docstring"""
def __init__( self: Any )-> Tuple:
lowerCamelCase : List[Any] = {}
def a__ ( self: Tuple , __a: Any , __a: int , __a: List[Any]=1 )-> Union[str, Any]:
# check if the u exists
if self.graph.get(__a ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
lowerCamelCase : Any = [[w, v]]
# add the other way
if self.graph.get(__a ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
lowerCamelCase : Dict = [[w, u]]
def a__ ( self: Tuple , __a: List[str] , __a: List[str] )-> Any:
if self.graph.get(__a ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(__a )
# the other way round
if self.graph.get(__a ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(__a )
def a__ ( self: Any , __a: str=-2 , __a: str=-1 )-> Tuple:
if s == d:
return []
lowerCamelCase : Dict = []
lowerCamelCase : List[Any] = []
if s == -2:
lowerCamelCase : Tuple = list(self.graph )[0]
stack.append(__a )
visited.append(__a )
lowerCamelCase : Any = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase : Any = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(__a )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase : List[str] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(__a ) != 0:
lowerCamelCase : List[str] = stack[len(__a ) - 1]
else:
lowerCamelCase : Union[str, Any] = ss
# check if se have reached the starting point
if len(__a ) == 0:
return visited
def a__ ( self: Any , __a: Tuple=-1 )-> List[Any]:
if c == -1:
lowerCamelCase : Any = floor(random() * 10_000 ) + 10
for i in range(__a ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
lowerCamelCase : Union[str, Any] = floor(random() * c ) + 1
if n != i:
self.add_pair(__a , __a , 1 )
def a__ ( self: Tuple , __a: int=-2 )-> str:
lowerCamelCase : Dict = deque()
lowerCamelCase : int = []
if s == -2:
lowerCamelCase : str = list(self.graph )[0]
d.append(__a )
visited.append(__a )
while d:
lowerCamelCase : Optional[Any] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def a__ ( self: str , __a: str )-> Any:
return len(self.graph[u] )
def a__ ( self: Any )-> List[str]:
lowerCamelCase : int = []
lowerCamelCase : Tuple = []
lowerCamelCase : str = list(self.graph )[0]
stack.append(__a )
visited.append(__a )
lowerCamelCase : List[str] = -2
lowerCamelCase : Optional[Any] = []
lowerCamelCase : Optional[Any] = s
lowerCamelCase : List[Any] = False
lowerCamelCase : List[str] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase : Optional[Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCamelCase : Any = len(__a ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase : List[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCamelCase : Any = True
if len(__a ) != 0:
lowerCamelCase : Tuple = stack[len(__a ) - 1]
else:
lowerCamelCase : Dict = False
indirect_parents.append(__a )
lowerCamelCase : str = s
lowerCamelCase : Any = ss
# check if se have reached the starting point
if len(__a ) == 0:
return list(__a )
def a__ ( self: Any )-> Union[str, Any]:
lowerCamelCase : str = []
lowerCamelCase : str = []
lowerCamelCase : str = list(self.graph )[0]
stack.append(__a )
visited.append(__a )
lowerCamelCase : List[str] = -2
lowerCamelCase : List[str] = []
lowerCamelCase : Optional[int] = s
lowerCamelCase : Tuple = False
lowerCamelCase : List[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase : Optional[Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCamelCase : Tuple = len(__a ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase : Optional[int] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCamelCase : str = True
if len(__a ) != 0:
lowerCamelCase : Optional[int] = stack[len(__a ) - 1]
else:
lowerCamelCase : Tuple = False
indirect_parents.append(__a )
lowerCamelCase : List[str] = s
lowerCamelCase : List[Any] = ss
# check if se have reached the starting point
if len(__a ) == 0:
return False
def a__ ( self: Tuple )-> Optional[int]:
return list(self.graph )
def a__ ( self: Optional[Any] , __a: Dict=-2 , __a: Optional[Any]=-1 )-> Optional[int]:
lowerCamelCase : List[str] = time()
self.dfs(__a , __a )
lowerCamelCase : Optional[int] = time()
return end - begin
def a__ ( self: Union[str, Any] , __a: Optional[Any]=-2 )-> Any:
lowerCamelCase : Tuple = time()
self.bfs(__a )
lowerCamelCase : Optional[int] = time()
return end - begin
| 222 | 0 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class __magic_name__ ( nn.Module):
A: int
A: jnp.dtype = jnp.floataa
def UpperCAmelCase__ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ : Dict = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : int , lowerCamelCase__ : List[Any] ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = hidden_states.shape
UpperCamelCase__ : List[Any] = jax.image.resize(
lowerCamelCase__ , shape=(batch, height * 2, width * 2, channels) , method='''nearest''' , )
UpperCamelCase__ : Any = self.conv(lowerCamelCase__ )
return hidden_states
class __magic_name__ ( nn.Module):
A: int
A: jnp.dtype = jnp.floataa
def UpperCAmelCase__ ( self : Optional[int] ) -> str:
'''simple docstring'''
UpperCamelCase__ : List[str] = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Union[str, Any] , lowerCamelCase__ : Union[str, Any] ) -> int:
'''simple docstring'''
UpperCamelCase__ : List[str] = self.conv(lowerCamelCase__ )
return hidden_states
class __magic_name__ ( nn.Module):
A: int
A: int = None
A: float = 0.0
A: bool = None
A: jnp.dtype = jnp.floataa
def UpperCAmelCase__ ( self : List[str] ) -> Dict:
'''simple docstring'''
UpperCamelCase__ : str = self.in_channels if self.out_channels is None else self.out_channels
UpperCamelCase__ : Any = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
UpperCamelCase__ : Tuple = nn.Conv(
lowerCamelCase__ , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
UpperCamelCase__ : Union[str, Any] = nn.Dense(lowerCamelCase__ , dtype=self.dtype )
UpperCamelCase__ : Optional[Any] = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
UpperCamelCase__ : Tuple = nn.Dropout(self.dropout_prob )
UpperCamelCase__ : List[str] = nn.Conv(
lowerCamelCase__ , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
UpperCamelCase__ : Optional[int] = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
UpperCamelCase__ : Optional[Any] = None
if use_nin_shortcut:
UpperCamelCase__ : List[str] = nn.Conv(
lowerCamelCase__ , kernel_size=(1, 1) , strides=(1, 1) , padding='''VALID''' , dtype=self.dtype , )
def __call__( self : Any , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[str]=True ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = hidden_states
UpperCamelCase__ : int = self.norma(lowerCamelCase__ )
UpperCamelCase__ : List[str] = nn.swish(lowerCamelCase__ )
UpperCamelCase__ : Tuple = self.conva(lowerCamelCase__ )
UpperCamelCase__ : List[str] = self.time_emb_proj(nn.swish(lowerCamelCase__ ) )
UpperCamelCase__ : str = jnp.expand_dims(jnp.expand_dims(lowerCamelCase__ , 1 ) , 1 )
UpperCamelCase__ : Tuple = hidden_states + temb
UpperCamelCase__ : Dict = self.norma(lowerCamelCase__ )
UpperCamelCase__ : int = nn.swish(lowerCamelCase__ )
UpperCamelCase__ : str = self.dropout(lowerCamelCase__ , lowerCamelCase__ )
UpperCamelCase__ : int = self.conva(lowerCamelCase__ )
if self.conv_shortcut is not None:
UpperCamelCase__ : str = self.conv_shortcut(lowerCamelCase__ )
return hidden_states + residual
| 706 |
__UpperCamelCase : List[Any] = 256
# Modulus to hash a string
__UpperCamelCase : Union[str, Any] = 100_0003
def _a ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
UpperCamelCase__ : Optional[int] = len(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = len(SCREAMING_SNAKE_CASE )
if p_len > t_len:
return False
UpperCamelCase__ : Any = 0
UpperCamelCase__ : str = 0
UpperCamelCase__ : List[Any] = 1
# Calculating the hash of pattern and substring of text
for i in range(SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : Any = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
UpperCamelCase__ : List[str] = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
UpperCamelCase__ : Dict = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
UpperCamelCase__ : Optional[int] = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def _a ( ):
"""simple docstring"""
UpperCamelCase__ : Tuple = '''abc1abc12'''
UpperCamelCase__ : Dict = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
UpperCamelCase__ : List[str] = '''alskfjaldsk23adsfabcabc'''
assert rabin_karp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and not rabin_karp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Test 2)
UpperCamelCase__ : Optional[int] = '''ABABX'''
UpperCamelCase__ : int = '''ABABZABABYABABX'''
assert rabin_karp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Test 3)
UpperCamelCase__ : int = '''AAAB'''
UpperCamelCase__ : str = '''ABAAAAAB'''
assert rabin_karp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Test 4)
UpperCamelCase__ : Union[str, Any] = '''abcdabcy'''
UpperCamelCase__ : List[str] = '''abcxabcdabxabcdabcdabcy'''
assert rabin_karp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Test 5)
UpperCamelCase__ : Tuple = '''Lü'''
UpperCamelCase__ : Any = '''Lüsai'''
assert rabin_karp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = '''Lue'''
assert not rabin_karp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
print('''Success.''' )
if __name__ == "__main__":
test_rabin_karp()
| 106 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
__A : str = logging.getLogger(__name__)
@dataclass
class A_ :
UpperCAmelCase__ = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCAmelCase__ = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCAmelCase__ = field(
default=a_ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
@dataclass
class A_ :
UpperCAmelCase__ = field(default=a_ , metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
UpperCAmelCase__ = field(
default=a_ , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. If passed, sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCAmelCase__ = field(
default=a_ , metadata={
'''help''': (
'''Whether to pad all samples to the maximum sentence length. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch. More '''
'''efficient on GPU but very bad for TPU.'''
)
} , )
UpperCAmelCase__ = field(
default=a_ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCAmelCase__ = field(
default=a_ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def _lowercase ( self ):
'''simple docstring'''
if self.train_file is not None:
UpperCAmelCase = self.train_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
UpperCAmelCase = self.validation_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class A_ :
UpperCAmelCase__ = 42
UpperCAmelCase__ = True
UpperCAmelCase__ = None
UpperCAmelCase__ = None
def __call__( self , _A ):
'''simple docstring'''
UpperCAmelCase = '''label''' if '''label''' in features[0].keys() else '''labels'''
UpperCAmelCase = [feature.pop(_A ) for feature in features]
UpperCAmelCase = len(_A )
UpperCAmelCase = len(features[0]['''input_ids'''] )
UpperCAmelCase = [
[{k: v[i] for k, v in feature.items()} for i in range(_A )] for feature in features
]
UpperCAmelCase = list(chain(*_A ) )
UpperCAmelCase = self.tokenizer.pad(
_A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
# Un-flatten
UpperCAmelCase = {k: v.view(_A , _A , -1 ) for k, v in batch.items()}
# Add back labels
UpperCAmelCase = torch.tensor(_A , dtype=torch.intaa )
return batch
def __SCREAMING_SNAKE_CASE ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_swag''' , UpperCamelCase__ , UpperCamelCase__ )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(UpperCamelCase__ )
datasets.utils.logging.set_verbosity(UpperCamelCase__ )
transformers.utils.logging.set_verbosity(UpperCamelCase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
UpperCAmelCase = {}
if data_args.train_file is not None:
UpperCAmelCase = data_args.train_file
if data_args.validation_file is not None:
UpperCAmelCase = data_args.validation_file
UpperCAmelCase = data_args.train_file.split('''.''' )[-1]
UpperCAmelCase = load_dataset(
UpperCamelCase__ , data_files=UpperCamelCase__ , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
UpperCAmelCase = load_dataset(
'''swag''' , '''regular''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
UpperCAmelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
UpperCAmelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=UpperCamelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
UpperCAmelCase = [F"""ending{i}""" for i in range(4 )]
UpperCAmelCase = '''sent1'''
UpperCAmelCase = '''sent2'''
if data_args.max_seq_length is None:
UpperCAmelCase = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
'''The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'''
''' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'''
''' override this default with `--block_size xxx`.''' )
UpperCAmelCase = 1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
F"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
UpperCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(UpperCamelCase__ ):
UpperCAmelCase = [[context] * 4 for context in examples[context_name]]
UpperCAmelCase = examples[question_header_name]
UpperCAmelCase = [
[F"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(UpperCamelCase__ )
]
# Flatten out
UpperCAmelCase = list(chain(*UpperCamelCase__ ) )
UpperCAmelCase = list(chain(*UpperCamelCase__ ) )
# Tokenize
UpperCAmelCase = tokenizer(
UpperCamelCase__ , UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , padding='''max_length''' if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(UpperCamelCase__ ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('''--do_train requires a train dataset''' )
UpperCAmelCase = raw_datasets['''train''']
if data_args.max_train_samples is not None:
UpperCAmelCase = min(len(UpperCamelCase__ ) , data_args.max_train_samples )
UpperCAmelCase = train_dataset.select(range(UpperCamelCase__ ) )
with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
UpperCAmelCase = train_dataset.map(
UpperCamelCase__ , batched=UpperCamelCase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError('''--do_eval requires a validation dataset''' )
UpperCAmelCase = raw_datasets['''validation''']
if data_args.max_eval_samples is not None:
UpperCAmelCase = min(len(UpperCamelCase__ ) , data_args.max_eval_samples )
UpperCAmelCase = eval_dataset.select(range(UpperCamelCase__ ) )
with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
UpperCAmelCase = eval_dataset.map(
UpperCamelCase__ , batched=UpperCamelCase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
UpperCAmelCase = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=UpperCamelCase__ , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(UpperCamelCase__ ):
UpperCAmelCase , UpperCAmelCase = eval_predictions
UpperCAmelCase = np.argmax(UpperCamelCase__ , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
UpperCAmelCase = Trainer(
model=UpperCamelCase__ , args=UpperCamelCase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=UpperCamelCase__ , data_collator=UpperCamelCase__ , compute_metrics=UpperCamelCase__ , )
# Training
if training_args.do_train:
UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCAmelCase = last_checkpoint
UpperCAmelCase = trainer.train(resume_from_checkpoint=UpperCamelCase__ )
trainer.save_model() # Saves the tokenizer too for easy upload
UpperCAmelCase = train_result.metrics
UpperCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCamelCase__ )
)
UpperCAmelCase = min(UpperCamelCase__ , len(UpperCamelCase__ ) )
trainer.log_metrics('''train''' , UpperCamelCase__ )
trainer.save_metrics('''train''' , UpperCamelCase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
UpperCAmelCase = trainer.evaluate()
UpperCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCamelCase__ )
UpperCAmelCase = min(UpperCamelCase__ , len(UpperCamelCase__ ) )
trainer.log_metrics('''eval''' , UpperCamelCase__ )
trainer.save_metrics('''eval''' , UpperCamelCase__ )
UpperCAmelCase = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''multiple-choice''',
'''dataset_tags''': '''swag''',
'''dataset_args''': '''regular''',
'''dataset''': '''SWAG''',
'''language''': '''en''',
}
if training_args.push_to_hub:
trainer.push_to_hub(**UpperCamelCase__ )
else:
trainer.create_model_card(**UpperCamelCase__ )
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 130 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A : Optional[Any] = logging.get_logger(__name__)
__A : Dict = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
__A : Tuple = {
"tokenizer_file": {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
},
}
__A : Dict = {
"gpt-neox-20b": 2_048,
}
class A_ (a_ ):
UpperCAmelCase__ = VOCAB_FILES_NAMES
UpperCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self , _A=None , _A=None , _A=None , _A="<|endoftext|>" , _A="<|endoftext|>" , _A="<|endoftext|>" , _A=False , **_A , ):
'''simple docstring'''
super().__init__(
_A , _A , tokenizer_file=_A , unk_token=_A , bos_token=_A , eos_token=_A , add_prefix_space=_A , **_A , )
UpperCAmelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , _A ) != add_prefix_space:
UpperCAmelCase = getattr(_A , pre_tok_state.pop('''type''' ) )
UpperCAmelCase = add_prefix_space
UpperCAmelCase = pre_tok_class(**_A )
UpperCAmelCase = add_prefix_space
def _lowercase ( self , _A , _A = None ):
'''simple docstring'''
UpperCAmelCase = self._tokenizer.model.save(_A , name=_A )
return tuple(_A )
def _lowercase ( self , _A ):
'''simple docstring'''
UpperCAmelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(_A , add_special_tokens=_A ) + [self.eos_token_id] )
if len(_A ) > self.model_max_length:
UpperCAmelCase = input_ids[-self.model_max_length :]
return input_ids
| 130 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowercase__ : Union[str, Any] = 1_6
lowercase__ : Tuple = 3_2
def A_ ( snake_case : Accelerator , snake_case : int = 16 ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
__UpperCamelCase = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(snake_case : Tuple ):
# max_length=None => use the model max length (it's actually the default)
__UpperCamelCase = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=snake_case , max_length=snake_case )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__UpperCamelCase = datasets.map(
snake_case , batched=snake_case , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__UpperCamelCase = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(snake_case : Union[str, Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__UpperCamelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__UpperCamelCase = 16
elif accelerator.mixed_precision != "no":
__UpperCamelCase = 8
else:
__UpperCamelCase = None
return tokenizer.pad(
snake_case , padding='''longest''' , max_length=snake_case , pad_to_multiple_of=snake_case , return_tensors='''pt''' , )
# Instantiate dataloaders.
__UpperCamelCase = DataLoader(
tokenized_datasets['''train'''] , shuffle=snake_case , collate_fn=snake_case , batch_size=snake_case )
__UpperCamelCase = DataLoader(
tokenized_datasets['''validation'''] , shuffle=snake_case , collate_fn=snake_case , batch_size=snake_case )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowercase__ : List[str] = mocked_dataloaders # noqa: F811
def A_ ( snake_case : str , snake_case : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , snake_case ) == "1":
__UpperCamelCase = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
__UpperCamelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='''all''' , project_dir=args.project_dir )
else:
__UpperCamelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__UpperCamelCase = config['''lr''']
__UpperCamelCase = int(config['''num_epochs'''] )
__UpperCamelCase = int(config['''seed'''] )
__UpperCamelCase = int(config['''batch_size'''] )
set_seed(snake_case )
__UpperCamelCase , __UpperCamelCase = get_dataloaders(snake_case , snake_case )
__UpperCamelCase = evaluate.load('''glue''' , '''mrpc''' )
# If the batch size is too big we use gradient accumulation
__UpperCamelCase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
__UpperCamelCase = batch_size // MAX_GPU_BATCH_SIZE
__UpperCamelCase = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__UpperCamelCase = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=snake_case )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__UpperCamelCase = model.to(accelerator.device )
# Instantiate optimizer
__UpperCamelCase = AdamW(params=model.parameters() , lr=snake_case )
# Instantiate scheduler
__UpperCamelCase = get_linear_schedule_with_warmup(
optimizer=snake_case , num_warmup_steps=100 , num_training_steps=(len(snake_case ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = accelerator.prepare(
snake_case , snake_case , snake_case , snake_case , snake_case )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
__UpperCamelCase = os.path.split(snake_case )[-1].split('''.''' )[0]
accelerator.init_trackers(snake_case , snake_case )
# Now we train the model
for epoch in range(snake_case ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
__UpperCamelCase = 0
for step, batch in enumerate(snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__UpperCamelCase = model(**snake_case )
__UpperCamelCase = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
__UpperCamelCase = loss / gradient_accumulation_steps
accelerator.backward(snake_case )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
__UpperCamelCase = model(**snake_case )
__UpperCamelCase = outputs.logits.argmax(dim=-1 )
__UpperCamelCase , __UpperCamelCase = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=snake_case , references=snake_case , )
__UpperCamelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"epoch {epoch}:" , snake_case )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
'''accuracy''': eval_metric['''accuracy'''],
'''f1''': eval_metric['''f1'''],
'''train_loss''': total_loss.item() / len(snake_case ),
'''epoch''': epoch,
} , step=snake_case , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def A_ ( ) -> int:
'''simple docstring'''
__UpperCamelCase = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=snake_case , default=snake_case , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
parser.add_argument(
'''--with_tracking''' , action='''store_true''' , help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' , )
parser.add_argument(
'''--project_dir''' , type=snake_case , default='''logs''' , help='''Location on where to store experiment tracking logs` and relevent project information''' , )
__UpperCamelCase = parser.parse_args()
__UpperCamelCase = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(snake_case , snake_case )
if __name__ == "__main__":
main()
| 451 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : int = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = [
"GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"GitForCausalLM",
"GitModel",
"GitPreTrainedModel",
"GitVisionModel",
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
lowercase__ : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 451 | 1 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _SCREAMING_SNAKE_CASE( unittest.TestCase ):
def __init__( self : Optional[int] , UpperCamelCase_ : Dict ) -> Any:
SCREAMING_SNAKE_CASE__ :Union[str, Any] = parent
def __lowerCamelCase ( self : Union[str, Any] ) -> str:
return {}
def lowerCamelCase ( ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :Dict = '<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR=\"FFFFFF\">\n <HR>\n <a href=\"http://google.com\">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style=\"color:#0000FF\">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>'
SCREAMING_SNAKE_CASE__ :List[Any] = '\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n '
return [html_string_a, html_string_a]
@require_bsa
class _SCREAMING_SNAKE_CASE( __a , unittest.TestCase ):
A_ : Union[str, Any] = MarkupLMFeatureExtractor if is_bsa_available() else None
def __lowerCamelCase ( self : Tuple ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ :Optional[Any] = MarkupLMFeatureExtractionTester(self )
@property
def __lowerCamelCase ( self : Optional[int] ) -> Optional[Any]:
return self.feature_extract_tester.prepare_feat_extract_dict()
def __lowerCamelCase ( self : str ) -> Dict:
SCREAMING_SNAKE_CASE__ :Union[str, Any] = self.feature_extraction_class()
# Test not batched input
SCREAMING_SNAKE_CASE__ :List[Any] = get_html_strings()[0]
SCREAMING_SNAKE_CASE__ :Optional[Any] = feature_extractor(lowercase_ )
# fmt: off
SCREAMING_SNAKE_CASE__ :List[Any] = [['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
SCREAMING_SNAKE_CASE__ :List[Any] = [['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
# fmt: on
self.assertEqual(encoding.nodes , lowercase_ )
self.assertEqual(encoding.xpaths , lowercase_ )
# Test batched
SCREAMING_SNAKE_CASE__ :Optional[Any] = get_html_strings()
SCREAMING_SNAKE_CASE__ :Union[str, Any] = feature_extractor(lowercase_ )
# fmt: off
SCREAMING_SNAKE_CASE__ :Union[str, Any] = expected_nodes + [['My First Heading', 'My first paragraph.']]
SCREAMING_SNAKE_CASE__ :str = expected_xpaths + [['/html/body/h1', '/html/body/p']]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , lowercase_ )
self.assertEqual(encoding.xpaths , lowercase_ )
| 209 | '''simple docstring'''
def A_ ( SCREAMING_SNAKE_CASE_ ) ->bool:
if num < 0:
return False
lowercase_ = num
lowercase_ = 0
while num > 0:
lowercase_ = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod()
| 451 | 0 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowercase_ (A : Optional[int] , A : Any=None ):
snake_case__ : Any = None
if token is not None:
snake_case__ : List[Any] = {'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}
snake_case__ : Union[str, Any] = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
snake_case__ : Any = requests.get(A , headers=A ).json()
snake_case__ : int = {}
try:
job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
snake_case__ : Optional[Any] = math.ceil((result['total_count'] - 1_0_0) / 1_0_0 )
for i in range(A ):
snake_case__ : Any = requests.get(url + F'''&page={i + 2}''' , headers=A ).json()
job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
return job_links
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
def lowercase_ (A : Optional[int] , A : Optional[Any]=None ):
snake_case__ : str = None
if token is not None:
snake_case__ : Union[str, Any] = {'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}
snake_case__ : Union[str, Any] = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'''
snake_case__ : Union[str, Any] = requests.get(A , headers=A ).json()
snake_case__ : List[str] = {}
try:
artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
snake_case__ : Optional[int] = math.ceil((result['total_count'] - 1_0_0) / 1_0_0 )
for i in range(A ):
snake_case__ : List[Any] = requests.get(url + F'''&page={i + 2}''' , headers=A ).json()
artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
return artifacts
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
def lowercase_ (A : str , A : Optional[Any] , A : Optional[Any] , A : Optional[int] ):
snake_case__ : Union[str, Any] = None
if token is not None:
snake_case__ : Tuple = {'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}
snake_case__ : Tuple = requests.get(A , headers=A , allow_redirects=A )
snake_case__ : Any = result.headers['Location']
snake_case__ : Union[str, Any] = requests.get(A , allow_redirects=A )
snake_case__ : str = os.path.join(A , F'''{artifact_name}.zip''' )
with open(A , 'wb' ) as fp:
fp.write(response.content )
def lowercase_ (A : Optional[Any] , A : Any=None ):
snake_case__ : Tuple = []
snake_case__ : Tuple = []
snake_case__ : Optional[int] = None
with zipfile.ZipFile(A ) as z:
for filename in z.namelist():
if not os.path.isdir(A ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(A ) as f:
for line in f:
snake_case__ : int = line.decode('UTF-8' ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
snake_case__ : Optional[int] = line[: line.index(': ' )]
snake_case__ : Optional[Any] = line[line.index(': ' ) + len(': ' ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith('FAILED ' ):
# `test` is the test method that failed
snake_case__ : Optional[Any] = line[len('FAILED ' ) :]
failed_tests.append(A )
elif filename == "job_name.txt":
snake_case__ : List[Any] = line
if len(A ) != len(A ):
raise ValueError(
F'''`errors` and `failed_tests` should have the same number of elements. Got {len(A )} for `errors` '''
F'''and {len(A )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'''
' problem.' )
snake_case__ : Union[str, Any] = None
if job_name and job_links:
snake_case__ : Any = job_links.get(A , A )
# A list with elements of the form (line of error, error, failed test)
snake_case__ : Union[str, Any] = [x + [y] + [job_link] for x, y in zip(A , A )]
return result
def lowercase_ (A : Optional[int] , A : List[str]=None ):
snake_case__ : Union[str, Any] = []
snake_case__ : Optional[Any] = [os.path.join(A , A ) for p in os.listdir(A ) if p.endswith('.zip' )]
for p in paths:
errors.extend(get_errors_from_single_artifact(A , job_links=A ) )
return errors
def lowercase_ (A : Optional[Any] , A : Optional[int]=None ):
snake_case__ : Dict = Counter()
counter.update([x[1] for x in logs] )
snake_case__ : Any = counter.most_common()
snake_case__ : Optional[int] = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
snake_case__ : List[str] = {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}
snake_case__ : int = dict(sorted(r.items() , key=lambda A : item[1]["count"] , reverse=A ) )
return r
def lowercase_ (A : List[Any] ):
snake_case__ : Any = test.split('::' )[0]
if test.startswith('tests/models/' ):
snake_case__ : Tuple = test.split('/' )[2]
else:
snake_case__ : Tuple = None
return test
def lowercase_ (A : Tuple , A : List[Any]=None ):
snake_case__ : List[Any] = [(x[0], x[1], get_model(x[2] )) for x in logs]
snake_case__ : int = [x for x in logs if x[2] is not None]
snake_case__ : Dict = {x[2] for x in logs}
snake_case__ : List[Any] = {}
for test in tests:
snake_case__ : List[Any] = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
snake_case__ : Tuple = counter.most_common()
snake_case__ : List[str] = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
snake_case__ : List[Any] = sum(error_counts.values() )
if n_errors > 0:
snake_case__ : Optional[Any] = {'count': n_errors, 'errors': error_counts}
snake_case__ : List[str] = dict(sorted(r.items() , key=lambda A : item[1]["count"] , reverse=A ) )
return r
def lowercase_ (A : Tuple ):
snake_case__ : Tuple = '| no. | error | status |'
snake_case__ : Dict = '|-:|:-|:-|'
snake_case__ : Dict = [header, sep]
for error in reduced_by_error:
snake_case__ : List[str] = reduced_by_error[error]['count']
snake_case__ : Tuple = F'''| {count} | {error[:1_0_0]} | |'''
lines.append(A )
return "\n".join(A )
def lowercase_ (A : Optional[int] ):
snake_case__ : Dict = '| model | no. of errors | major error | count |'
snake_case__ : Optional[Any] = '|-:|-:|-:|-:|'
snake_case__ : Any = [header, sep]
for model in reduced_by_model:
snake_case__ : List[Any] = reduced_by_model[model]['count']
snake_case__ : int = list(reduced_by_model[model]['errors'].items() )[0]
snake_case__ : List[str] = F'''| {model} | {count} | {error[:6_0]} | {_count} |'''
lines.append(A )
return "\n".join(A )
if __name__ == "__main__":
a_ :str = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Where to store the downloaded artifacts and other result files.",
)
parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
a_ :int = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
a_ :List[Any] = get_job_links(args.workflow_run_id, token=args.token)
a_ :Optional[Any] = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
a_ :List[Any] = k.find(" / ")
a_ :int = k[index + len(" / ") :]
a_ :Dict = v
with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
a_ :Dict = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
a_ :Optional[Any] = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
a_ :Optional[int] = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
a_ :Tuple = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
a_ :List[str] = reduce_by_error(errors)
a_ :Union[str, Any] = reduce_by_model(errors)
a_ :Optional[Any] = make_github_table(reduced_by_error)
a_ :Optional[Any] = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
fp.write(sa)
| 704 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ :str = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFormerConfig",
"InstructBlipVisionConfig",
],
"processing_instructblip": ["InstructBlipProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ :Optional[int] = [
"INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"InstructBlipQFormerModel",
"InstructBlipPreTrainedModel",
"InstructBlipForConditionalGeneration",
"InstructBlipVisionModel",
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ :Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 243 | 0 |
'''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ):
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty''' )
lowercase__ : str = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(A__ ) )
return round(A__ , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 152 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = 'blenderbot-small'
SCREAMING_SNAKE_CASE : int = ['past_key_values']
SCREAMING_SNAKE_CASE : List[str] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Optional[int] ,lowercase__ : List[str]=5_0_2_6_5 ,lowercase__ : Optional[Any]=5_1_2 ,lowercase__ : Optional[int]=8 ,lowercase__ : List[Any]=2_0_4_8 ,lowercase__ : List[str]=1_6 ,lowercase__ : str=8 ,lowercase__ : Any=2_0_4_8 ,lowercase__ : Tuple=1_6 ,lowercase__ : Tuple=0.0 ,lowercase__ : List[str]=0.0 ,lowercase__ : Any=True ,lowercase__ : str=True ,lowercase__ : int="gelu" ,lowercase__ : Tuple=5_1_2 ,lowercase__ : List[Any]=0.1 ,lowercase__ : Tuple=0.0 ,lowercase__ : str=0.0 ,lowercase__ : Any=0.0_2 ,lowercase__ : Union[str, Any]=1 ,lowercase__ : List[Any]=False ,lowercase__ : Optional[int]=0 ,lowercase__ : Optional[int]=1 ,lowercase__ : str=2 ,lowercase__ : int=2 ,**lowercase__ : List[str] ,):
__lowercase = vocab_size
__lowercase = max_position_embeddings
__lowercase = d_model
__lowercase = encoder_ffn_dim
__lowercase = encoder_layers
__lowercase = encoder_attention_heads
__lowercase = decoder_ffn_dim
__lowercase = decoder_layers
__lowercase = decoder_attention_heads
__lowercase = dropout
__lowercase = attention_dropout
__lowercase = activation_dropout
__lowercase = activation_function
__lowercase = init_std
__lowercase = encoder_layerdrop
__lowercase = decoder_layerdrop
__lowercase = use_cache
__lowercase = encoder_layers
__lowercase = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=lowercase__ ,bos_token_id=lowercase__ ,eos_token_id=lowercase__ ,is_encoder_decoder=lowercase__ ,decoder_start_token_id=lowercase__ ,forced_eos_token_id=lowercase__ ,**lowercase__ ,)
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
@property
def SCREAMING_SNAKE_CASE ( self : Dict ):
if self.task in ["default", "seq2seq-lm"]:
__lowercase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
__lowercase = {0: '''batch'''}
__lowercase = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
__lowercase = {0: '''batch''', 1: '''decoder_sequence'''}
__lowercase = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowercase__ ,direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
__lowercase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
__lowercase , __lowercase = self.num_layers
for i in range(lowercase__ ):
__lowercase = {0: '''batch''', 2: '''past_sequence + sequence'''}
__lowercase = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
__lowercase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
if self.task in ["default", "seq2seq-lm"]:
__lowercase = super().outputs
else:
__lowercase = super(lowercase__ ,self ).outputs
if self.use_past:
__lowercase , __lowercase = self.num_layers
for i in range(lowercase__ ):
__lowercase = {0: '''batch''', 2: '''past_sequence + sequence'''}
__lowercase = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def SCREAMING_SNAKE_CASE ( self : Dict ,lowercase__ : PreTrainedTokenizer ,lowercase__ : int = -1 ,lowercase__ : int = -1 ,lowercase__ : bool = False ,lowercase__ : Optional[TensorType] = None ,):
__lowercase = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ )
# Generate decoder inputs
__lowercase = seq_length if not self.use_past else 1
__lowercase = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ )
__lowercase = {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
__lowercase = dict(**lowercase__ ,**lowercase__ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
__lowercase , __lowercase = common_inputs['''input_ids'''].shape
__lowercase = common_inputs['''decoder_input_ids'''].shape[1]
__lowercase , __lowercase = self.num_attention_heads
__lowercase = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__lowercase = decoder_seq_length + 3
__lowercase = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
__lowercase = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(lowercase__ ,lowercase__ )] ,dim=1 )
__lowercase = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
__lowercase , __lowercase = self.num_layers
__lowercase = min(lowercase__ ,lowercase__ )
__lowercase = max(lowercase__ ,lowercase__ ) - min_num_layers
__lowercase = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(lowercase__ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase__ ),
torch.zeros(lowercase__ ),
torch.zeros(lowercase__ ),
torch.zeros(lowercase__ ),
) )
# TODO: test this.
__lowercase = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(lowercase__ ,lowercase__ ):
common_inputs["past_key_values"].append((torch.zeros(lowercase__ ), torch.zeros(lowercase__ )) )
return common_inputs
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : PreTrainedTokenizer ,lowercase__ : int = -1 ,lowercase__ : int = -1 ,lowercase__ : bool = False ,lowercase__ : Optional[TensorType] = None ,):
__lowercase = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
__lowercase , __lowercase = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
__lowercase = seqlen + 2
__lowercase , __lowercase = self.num_layers
__lowercase , __lowercase = self.num_attention_heads
__lowercase = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__lowercase = common_inputs['''attention_mask'''].dtype
__lowercase = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(lowercase__ ,lowercase__ ,dtype=lowercase__ )] ,dim=1 )
__lowercase = [
(torch.zeros(lowercase__ ), torch.zeros(lowercase__ )) for _ in range(lowercase__ )
]
return common_inputs
def SCREAMING_SNAKE_CASE ( self : List[str] ,lowercase__ : PreTrainedTokenizer ,lowercase__ : int = -1 ,lowercase__ : int = -1 ,lowercase__ : bool = False ,lowercase__ : Optional[TensorType] = None ,):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
__lowercase = compute_effective_axis_dimension(
lowercase__ ,fixed_dimension=OnnxConfig.default_fixed_batch ,num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
__lowercase = tokenizer.num_special_tokens_to_add(lowercase__ )
__lowercase = compute_effective_axis_dimension(
lowercase__ ,fixed_dimension=OnnxConfig.default_fixed_sequence ,num_token_to_add=lowercase__ )
# Generate dummy inputs according to compute batch and sequence
__lowercase = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
__lowercase = dict(tokenizer(lowercase__ ,return_tensors=lowercase__ ) )
return common_inputs
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,lowercase__ : PreTrainedTokenizer ,lowercase__ : int = -1 ,lowercase__ : int = -1 ,lowercase__ : bool = False ,lowercase__ : Optional[TensorType] = None ,):
if self.task in ["default", "seq2seq-lm"]:
__lowercase = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase__ ,batch_size=lowercase__ ,seq_length=lowercase__ ,is_pair=lowercase__ ,framework=lowercase__ )
elif self.task == "causal-lm":
__lowercase = self._generate_dummy_inputs_for_causal_lm(
lowercase__ ,batch_size=lowercase__ ,seq_length=lowercase__ ,is_pair=lowercase__ ,framework=lowercase__ )
else:
__lowercase = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase__ ,batch_size=lowercase__ ,seq_length=lowercase__ ,is_pair=lowercase__ ,framework=lowercase__ )
return common_inputs
def SCREAMING_SNAKE_CASE ( self : Tuple ,lowercase__ : List[Any] ,lowercase__ : Tuple ,lowercase__ : List[Any] ,lowercase__ : Optional[Any] ):
if self.task in ["default", "seq2seq-lm"]:
__lowercase = super()._flatten_past_key_values_(lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ )
else:
__lowercase = super(lowercase__ ,self )._flatten_past_key_values_(
lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ )
| 41 | 0 |
def _UpperCAmelCase ( a : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 99 |
from collections.abc import Callable
def _UpperCAmelCase ( a : Callable[[float], float] , a : float , a : float ):
snake_case__ = a
snake_case__ = b
if function(a ) == 0: # one of the a or b is a root for the function
return a
elif function(a ) == 0:
return b
elif (
function(a ) * function(a ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
snake_case__ = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(a ) == 0:
return mid
elif function(a ) * function(a ) < 0:
snake_case__ = mid
else:
snake_case__ = mid
snake_case__ = start + (end - start) / 2.0
return mid
def _UpperCAmelCase ( a : float ):
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1_0_0_0))
import doctest
doctest.testmod()
| 99 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {
"""configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
"""tokenization_roberta""": ["""RobertaTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = ["""RobertaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
"""ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaForCausalLM""",
"""RobertaForMaskedLM""",
"""RobertaForMultipleChoice""",
"""RobertaForQuestionAnswering""",
"""RobertaForSequenceClassification""",
"""RobertaForTokenClassification""",
"""RobertaModel""",
"""RobertaPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
"""TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaForCausalLM""",
"""TFRobertaForMaskedLM""",
"""TFRobertaForMultipleChoice""",
"""TFRobertaForQuestionAnswering""",
"""TFRobertaForSequenceClassification""",
"""TFRobertaForTokenClassification""",
"""TFRobertaMainLayer""",
"""TFRobertaModel""",
"""TFRobertaPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
"""FlaxRobertaForCausalLM""",
"""FlaxRobertaForMaskedLM""",
"""FlaxRobertaForMultipleChoice""",
"""FlaxRobertaForQuestionAnswering""",
"""FlaxRobertaForSequenceClassification""",
"""FlaxRobertaForTokenClassification""",
"""FlaxRobertaModel""",
"""FlaxRobertaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 558 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def UpperCamelCase ( __lowercase : int ,__lowercase : List[str] ,__lowercase : str ,__lowercase : Optional[Any] ,__lowercase : Any ):
'''simple docstring'''
for attribute in key.split('.' ):
A_ : Dict = getattr(__lowercase ,__lowercase )
if weight_type is not None:
A_ : Any = getattr(__lowercase ,__lowercase ).shape
else:
A_ : Optional[Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
A_ : int = value
elif weight_type == "weight_g":
A_ : Tuple = value
elif weight_type == "weight_v":
A_ : Union[str, Any] = value
elif weight_type == "bias":
A_ : Any = value
else:
A_ : str = value
logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def UpperCamelCase ( __lowercase : str ,__lowercase : Dict ,__lowercase : Tuple ):
'''simple docstring'''
A_ : Optional[Any] = []
A_ : Tuple = fairseq_model.state_dict()
A_ : Any = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
A_ : Union[str, Any] = False
if "conv_layers" in name:
load_conv_layer(
__lowercase ,__lowercase ,__lowercase ,__lowercase ,hf_model.config.feat_extract_norm == 'group' ,)
A_ : List[str] = True
else:
for key, mapped_key in MAPPING.items():
A_ : str = 'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
A_ : int = True
if "*" in mapped_key:
A_ : str = name.split(__lowercase )[0].split('.' )[-2]
A_ : Optional[Any] = mapped_key.replace('*' ,__lowercase )
if "weight_g" in name:
A_ : Dict = 'weight_g'
elif "weight_v" in name:
A_ : Tuple = 'weight_v'
elif "weight" in name:
A_ : Union[str, Any] = 'weight'
elif "bias" in name:
A_ : Optional[Any] = 'bias'
else:
A_ : Union[str, Any] = None
set_recursively(__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase )
continue
if not is_used:
unused_weights.append(__lowercase )
logger.warning(f'''Unused weights: {unused_weights}''' )
def UpperCamelCase ( __lowercase : Optional[Any] ,__lowercase : Union[str, Any] ,__lowercase : Any ,__lowercase : List[Any] ,__lowercase : Union[str, Any] ):
'''simple docstring'''
A_ : Optional[int] = full_name.split('conv_layers.' )[-1]
A_ : Any = name.split('.' )
A_ : Dict = int(items[0] )
A_ : Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
A_ : Optional[int] = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
A_ : Union[str, Any] = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
A_ : Any = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
A_ : Tuple = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__lowercase )
def UpperCamelCase ( __lowercase : List[str] ,__lowercase : str ):
'''simple docstring'''
A_ : Union[str, Any] = SEWConfig()
if is_finetuned:
A_ : Any = model.wav_encoder.wav_model.cfg
else:
A_ : int = model.cfg
A_ : Any = fs_config.conv_bias
A_ : Dict = eval(fs_config.conv_feature_layers )
A_ : List[Any] = [x[0] for x in conv_layers]
A_ : Optional[Any] = [x[1] for x in conv_layers]
A_ : List[Any] = [x[2] for x in conv_layers]
A_ : Optional[int] = 'gelu'
A_ : Union[str, Any] = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
A_ : Tuple = 0.0
A_ : Dict = fs_config.activation_fn.name
A_ : List[Any] = fs_config.encoder_embed_dim
A_ : int = 0.02
A_ : List[str] = fs_config.encoder_ffn_embed_dim
A_ : Any = 1e-5
A_ : Optional[Any] = fs_config.encoder_layerdrop
A_ : Optional[int] = fs_config.encoder_attention_heads
A_ : Any = fs_config.conv_pos_groups
A_ : int = fs_config.conv_pos
A_ : Tuple = len(__lowercase )
A_ : List[Any] = fs_config.encoder_layers
A_ : Any = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
A_ : Union[str, Any] = model.cfg
A_ : str = fs_config.final_dropout
A_ : Any = fs_config.layerdrop
A_ : str = fs_config.activation_dropout
A_ : Any = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
A_ : str = fs_config.attention_dropout
A_ : Any = fs_config.dropout_input
A_ : Dict = fs_config.dropout
A_ : Optional[Any] = fs_config.mask_channel_length
A_ : List[str] = fs_config.mask_channel_prob
A_ : Tuple = fs_config.mask_length
A_ : Dict = fs_config.mask_prob
A_ : Any = 'Wav2Vec2FeatureExtractor'
A_ : Union[str, Any] = 'Wav2Vec2CTCTokenizer'
return config
@torch.no_grad()
def UpperCamelCase ( __lowercase : List[Any] ,__lowercase : int ,__lowercase : Optional[int]=None ,__lowercase : Optional[Any]=None ,__lowercase : str=True ):
'''simple docstring'''
if is_finetuned:
A_ , A_ , A_ : List[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
A_ , A_ , A_ : Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
A_ : Union[str, Any] = SEWConfig.from_pretrained(__lowercase )
else:
A_ : Dict = convert_config(model[0] ,__lowercase )
A_ : Union[str, Any] = model[0].eval()
A_ : Optional[int] = True if config.feat_extract_norm == 'layer' else False
A_ : List[Any] = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=1_60_00 ,padding_value=0 ,do_normalize=__lowercase ,return_attention_mask=__lowercase ,)
if is_finetuned:
if dict_path:
A_ : Optional[int] = Dictionary.load(__lowercase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
A_ : int = target_dict.pad_index
A_ : List[Any] = target_dict.bos_index
A_ : Optional[Any] = target_dict.pad_index
A_ : str = target_dict.bos_index
A_ : str = target_dict.eos_index
A_ : str = len(target_dict.symbols )
A_ : Union[str, Any] = os.path.join(__lowercase ,'vocab.json' )
if not os.path.isdir(__lowercase ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__lowercase ) )
return
os.makedirs(__lowercase ,exist_ok=__lowercase )
with open(__lowercase ,'w' ,encoding='utf-8' ) as vocab_handle:
json.dump(target_dict.indices ,__lowercase )
A_ : Any = WavaVecaCTCTokenizer(
__lowercase ,unk_token=target_dict.unk_word ,pad_token=target_dict.pad_word ,bos_token=target_dict.bos_word ,eos_token=target_dict.eos_word ,word_delimiter_token='|' ,do_lower_case=__lowercase ,)
A_ : Tuple = WavaVecaProcessor(feature_extractor=__lowercase ,tokenizer=__lowercase )
processor.save_pretrained(__lowercase )
A_ : Dict = SEWForCTC(__lowercase )
else:
A_ : Tuple = SEWModel(__lowercase )
feature_extractor.save_pretrained(__lowercase )
recursively_load_weights(__lowercase ,__lowercase ,__lowercase )
hf_model.save_pretrained(__lowercase )
if __name__ == "__main__":
_UpperCAmelCase = 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(
"""--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
_UpperCAmelCase = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 558 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class lowerCAmelCase__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = 1
@register_to_config
def __init__( self , lowercase__=2_0_0_0 , lowercase__=0.1 , lowercase__=2_0 , lowercase__=1E-3 ):
'''simple docstring'''
__A =None
__A =None
__A =None
def __UpperCamelCase ( self , lowercase__ , lowercase__ = None ):
'''simple docstring'''
__A =torch.linspace(1 , self.config.sampling_eps , __snake_case , device=__snake_case )
def __UpperCamelCase ( self , lowercase__ , lowercase__ , lowercase__ , lowercase__=None ):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
'''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
__A =(
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
__A =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
__A =std.flatten()
while len(std.shape ) < len(score.shape ):
__A =std.unsqueeze(-1 )
__A =-score / std
# compute
__A =-1.0 / len(self.timesteps )
__A =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
__A =beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
__A =beta_t.unsqueeze(-1 )
__A =-0.5 * beta_t * x
__A =torch.sqrt(__snake_case )
__A =drift - diffusion**2 * score
__A =x + drift * dt
# add noise
__A =randn_tensor(x.shape , layout=x.layout , generator=__snake_case , device=x.device , dtype=x.dtype )
__A =x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self ):
'''simple docstring'''
return self.config.num_train_timesteps
| 705 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
def A__ ( *__A : Optional[int] , **__A : Union[str, Any] ) ->Dict:
requires_backends(__A , ['''torch'''] )
def A__ ( *__A : str , **__A : int ) ->List[Any]:
requires_backends(__A , ['''torch'''] )
def A__ ( *__A : Dict , **__A : str ) ->Tuple:
requires_backends(__A , ['''torch'''] )
def A__ ( *__A : Optional[Any] , **__A : Dict ) ->Optional[Any]:
requires_backends(__A , ['''torch'''] )
def A__ ( *__A : str , **__A : str ) ->Any:
requires_backends(__A , ['''torch'''] )
def A__ ( *__A : List[Any] , **__A : Dict ) ->Union[str, Any]:
requires_backends(__A , ['''torch'''] )
def A__ ( *__A : Optional[int] , **__A : Optional[int] ) ->Any:
requires_backends(__A , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class lowerCAmelCase__ ( metaclass=__magic_name__ ):
'''simple docstring'''
lowercase_ = ["""torch"""]
def __init__( self , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def __UpperCamelCase ( cls , *lowercase__ , **lowercase__ ):
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
| 516 | 0 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging
UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowercase__ ( A_ ):
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , ) -> Union[str, Any]:
super().__init__()
if safety_checker is None:
logger.warning(
F'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
""" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
""" results in services or applications open to the public. Both the diffusers team and Hugging Face"""
""" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
""" it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
""" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .""")
self.register_modules(
speech_model=SCREAMING_SNAKE_CASE , speech_processor=SCREAMING_SNAKE_CASE , vae=SCREAMING_SNAKE_CASE , text_encoder=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE , unet=SCREAMING_SNAKE_CASE , scheduler=SCREAMING_SNAKE_CASE , feature_extractor=SCREAMING_SNAKE_CASE , )
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE = "auto") -> Dict:
if slice_size == "auto":
_lowerCamelCase : int = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self) -> Any:
self.enable_attention_slicing(SCREAMING_SNAKE_CASE)
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=1_6000 , SCREAMING_SNAKE_CASE = 512 , SCREAMING_SNAKE_CASE = 512 , SCREAMING_SNAKE_CASE = 50 , SCREAMING_SNAKE_CASE = 7.5 , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 0.0 , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = "pil" , SCREAMING_SNAKE_CASE = True , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = 1 , **SCREAMING_SNAKE_CASE , ) -> Dict:
_lowerCamelCase : Dict = self.speech_processor.feature_extractor(
SCREAMING_SNAKE_CASE , return_tensors="""pt""" , sampling_rate=SCREAMING_SNAKE_CASE).input_features.to(self.device)
_lowerCamelCase : Union[str, Any] = self.speech_model.generate(SCREAMING_SNAKE_CASE , max_length=48_0000)
_lowerCamelCase : Optional[int] = self.speech_processor.tokenizer.batch_decode(SCREAMING_SNAKE_CASE , skip_special_tokens=SCREAMING_SNAKE_CASE , normalize=SCREAMING_SNAKE_CASE)[
0
]
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE):
_lowerCamelCase : Tuple = 1
elif isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE):
_lowerCamelCase : Union[str, Any] = len(SCREAMING_SNAKE_CASE)
else:
raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE)}')
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.')
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) or callback_steps <= 0)
):
raise ValueError(
F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
F' {type(SCREAMING_SNAKE_CASE)}.')
# get prompt text embeddings
_lowerCamelCase : Tuple = self.tokenizer(
SCREAMING_SNAKE_CASE , padding="""max_length""" , max_length=self.tokenizer.model_max_length , return_tensors="""pt""" , )
_lowerCamelCase : Any = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
_lowerCamelCase : Union[str, Any] = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
"""The following part of your input was truncated because CLIP can only handle sequences up to"""
F' {self.tokenizer.model_max_length} tokens: {removed_text}')
_lowerCamelCase : int = text_input_ids[:, : self.tokenizer.model_max_length]
_lowerCamelCase : Union[str, Any] = self.text_encoder(text_input_ids.to(self.device))[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : int = text_embeddings.shape
_lowerCamelCase : Optional[int] = text_embeddings.repeat(1 , SCREAMING_SNAKE_CASE , 1)
_lowerCamelCase : Optional[int] = text_embeddings.view(bs_embed * num_images_per_prompt , SCREAMING_SNAKE_CASE , -1)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_lowerCamelCase : Optional[int] = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_lowerCamelCase : List[str]
if negative_prompt is None:
_lowerCamelCase : List[Any] = [""""""] * batch_size
elif type(SCREAMING_SNAKE_CASE) is not type(SCREAMING_SNAKE_CASE):
raise TypeError(
F'`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE)} !='
F' {type(SCREAMING_SNAKE_CASE)}.')
elif isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE):
_lowerCamelCase : Optional[Any] = [negative_prompt]
elif batch_size != len(SCREAMING_SNAKE_CASE):
raise ValueError(
F'`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE)}, but `prompt`:'
F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
""" the batch size of `prompt`.""")
else:
_lowerCamelCase : Any = negative_prompt
_lowerCamelCase : int = text_input_ids.shape[-1]
_lowerCamelCase : str = self.tokenizer(
SCREAMING_SNAKE_CASE , padding="""max_length""" , max_length=SCREAMING_SNAKE_CASE , truncation=SCREAMING_SNAKE_CASE , return_tensors="""pt""" , )
_lowerCamelCase : List[Any] = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
_lowerCamelCase : List[Any] = uncond_embeddings.shape[1]
_lowerCamelCase : Optional[int] = uncond_embeddings.repeat(1 , SCREAMING_SNAKE_CASE , 1)
_lowerCamelCase : List[Any] = uncond_embeddings.view(batch_size * num_images_per_prompt , SCREAMING_SNAKE_CASE , -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_lowerCamelCase : Optional[Any] = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_lowerCamelCase : Optional[int] = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
_lowerCamelCase : Any = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
_lowerCamelCase : str = torch.randn(SCREAMING_SNAKE_CASE , generator=SCREAMING_SNAKE_CASE , device="""cpu""" , dtype=SCREAMING_SNAKE_CASE).to(
self.device)
else:
_lowerCamelCase : List[str] = torch.randn(SCREAMING_SNAKE_CASE , generator=SCREAMING_SNAKE_CASE , device=self.device , dtype=SCREAMING_SNAKE_CASE)
else:
if latents.shape != latents_shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
_lowerCamelCase : str = latents.to(self.device)
# set timesteps
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
_lowerCamelCase : Union[str, Any] = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
_lowerCamelCase : Any = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_lowerCamelCase : str = """eta""" in set(inspect.signature(self.scheduler.step).parameters.keys())
_lowerCamelCase : Dict = {}
if accepts_eta:
_lowerCamelCase : Any = eta
for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE)):
# expand the latents if we are doing classifier free guidance
_lowerCamelCase : Optional[int] = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
_lowerCamelCase : str = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
# predict the noise residual
_lowerCamelCase : str = self.unet(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , encoder_hidden_states=SCREAMING_SNAKE_CASE).sample
# perform guidance
if do_classifier_free_guidance:
_lowerCamelCase , _lowerCamelCase : Dict = noise_pred.chunk(2)
_lowerCamelCase : int = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
_lowerCamelCase : Tuple = self.scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
_lowerCamelCase : Dict = 1 / 0.1_82_15 * latents
_lowerCamelCase : Tuple = self.vae.decode(SCREAMING_SNAKE_CASE).sample
_lowerCamelCase : int = (image / 2 + 0.5).clamp(0 , 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
_lowerCamelCase : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1).float().numpy()
if output_type == "pil":
_lowerCamelCase : Optional[Any] = self.numpy_to_pil(SCREAMING_SNAKE_CASE)
if not return_dict:
return image
return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE , nsfw_content_detected=SCREAMING_SNAKE_CASE)
| 88 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int = 1_00_00_00 ) -> int:
__snake_case = 1
__snake_case = 1
__snake_case = {1: 1}
for inputa in range(2 , _UpperCAmelCase ):
__snake_case = 0
__snake_case = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
__snake_case = (3 * number) + 1
counter += 1
if inputa not in counters:
__snake_case = counter
if counter > pre_counter:
__snake_case = inputa
__snake_case = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 69 | 0 |
'''simple docstring'''
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
"""tensor(bool)""": np.bool_,
"""tensor(int8)""": np.inta,
"""tensor(uint8)""": np.uinta,
"""tensor(int16)""": np.intaa,
"""tensor(uint16)""": np.uintaa,
"""tensor(int32)""": np.intaa,
"""tensor(uint32)""": np.uintaa,
"""tensor(int64)""": np.intaa,
"""tensor(uint64)""": np.uintaa,
"""tensor(float16)""": np.floataa,
"""tensor(float)""": np.floataa,
"""tensor(double)""": np.floataa,
}
class __magic_name__ :
'''simple docstring'''
def __init__( self: List[Any] , _lowerCamelCase: Tuple=None , **_lowerCamelCase: str ):
logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.''' )
SCREAMING_SNAKE_CASE_ = model
SCREAMING_SNAKE_CASE_ = kwargs.get('''model_save_dir''' , _lowerCamelCase )
SCREAMING_SNAKE_CASE_ = kwargs.get('''latest_model_name''' , _lowerCamelCase )
def __call__( self: List[Any] , **_lowerCamelCase: List[str] ):
SCREAMING_SNAKE_CASE_ = {k: np.array(_lowerCamelCase ) for k, v in kwargs.items()}
return self.model.run(_lowerCamelCase , _lowerCamelCase )
@staticmethod
def _A ( _lowerCamelCase: Union[str, Path] , _lowerCamelCase: str=None , _lowerCamelCase: Tuple=None ):
if provider is None:
logger.info('''No onnxruntime provider specified, using CPUExecutionProvider''' )
SCREAMING_SNAKE_CASE_ = '''CPUExecutionProvider'''
return ort.InferenceSession(_lowerCamelCase , providers=[provider] , sess_options=_lowerCamelCase )
def _A ( self: Optional[int] , _lowerCamelCase: Union[str, Path] , _lowerCamelCase: Optional[str] = None , **_lowerCamelCase: int ):
SCREAMING_SNAKE_CASE_ = file_name if file_name is not None else ONNX_WEIGHTS_NAME
SCREAMING_SNAKE_CASE_ = self.model_save_dir.joinpath(self.latest_model_name )
SCREAMING_SNAKE_CASE_ = Path(_lowerCamelCase ).joinpath(_lowerCamelCase )
try:
shutil.copyfile(_lowerCamelCase , _lowerCamelCase )
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
SCREAMING_SNAKE_CASE_ = self.model_save_dir.joinpath(_lowerCamelCase )
if src_path.exists():
SCREAMING_SNAKE_CASE_ = Path(_lowerCamelCase ).joinpath(_lowerCamelCase )
try:
shutil.copyfile(_lowerCamelCase , _lowerCamelCase )
except shutil.SameFileError:
pass
def _A ( self: str , _lowerCamelCase: Union[str, os.PathLike] , **_lowerCamelCase: Dict , ):
if os.path.isfile(_lowerCamelCase ):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file" )
return
os.makedirs(_lowerCamelCase , exist_ok=_lowerCamelCase )
# saving model weights/files
self._save_pretrained(_lowerCamelCase , **_lowerCamelCase )
@classmethod
def _A ( cls: Optional[Any] , _lowerCamelCase: Union[str, Path] , _lowerCamelCase: Optional[Union[bool, str, None]] = None , _lowerCamelCase: Optional[Union[str, None]] = None , _lowerCamelCase: bool = False , _lowerCamelCase: Optional[str] = None , _lowerCamelCase: Optional[str] = None , _lowerCamelCase: Optional[str] = None , _lowerCamelCase: Optional["ort.SessionOptions"] = None , **_lowerCamelCase: Any , ):
SCREAMING_SNAKE_CASE_ = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(_lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = OnnxRuntimeModel.load_model(
os.path.join(_lowerCamelCase , _lowerCamelCase ) , provider=_lowerCamelCase , sess_options=_lowerCamelCase )
SCREAMING_SNAKE_CASE_ = Path(_lowerCamelCase )
# load model from hub
else:
# download model
SCREAMING_SNAKE_CASE_ = hf_hub_download(
repo_id=_lowerCamelCase , filename=_lowerCamelCase , use_auth_token=_lowerCamelCase , revision=_lowerCamelCase , cache_dir=_lowerCamelCase , force_download=_lowerCamelCase , )
SCREAMING_SNAKE_CASE_ = Path(_lowerCamelCase ).parent
SCREAMING_SNAKE_CASE_ = Path(_lowerCamelCase ).name
SCREAMING_SNAKE_CASE_ = OnnxRuntimeModel.load_model(_lowerCamelCase , provider=_lowerCamelCase , sess_options=_lowerCamelCase )
return cls(model=_lowerCamelCase , **_lowerCamelCase )
@classmethod
def _A ( cls: int , _lowerCamelCase: Union[str, Path] , _lowerCamelCase: bool = True , _lowerCamelCase: Optional[str] = None , _lowerCamelCase: Optional[str] = None , **_lowerCamelCase: Optional[int] , ):
SCREAMING_SNAKE_CASE_ = None
if len(str(_lowerCamelCase ).split('''@''' ) ) == 2:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = model_id.split('''@''' )
return cls._from_pretrained(
model_id=_lowerCamelCase , revision=_lowerCamelCase , cache_dir=_lowerCamelCase , force_download=_lowerCamelCase , use_auth_token=_lowerCamelCase , **_lowerCamelCase , )
| 720 |
from __future__ import annotations
__SCREAMING_SNAKE_CASE ={
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
class __magic_name__ :
'''simple docstring'''
def __init__( self: List[Any] , _lowerCamelCase: dict[str, list[str]] , _lowerCamelCase: str ):
SCREAMING_SNAKE_CASE_ = graph
# mapping node to its parent in resulting breadth first tree
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = source_vertex
def _A ( self: Tuple ):
SCREAMING_SNAKE_CASE_ = {self.source_vertex}
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = [self.source_vertex] # first in first out queue
while queue:
SCREAMING_SNAKE_CASE_ = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(_lowerCamelCase )
SCREAMING_SNAKE_CASE_ = vertex
queue.append(_lowerCamelCase )
def _A ( self: List[str] , _lowerCamelCase: str ):
if target_vertex == self.source_vertex:
return self.source_vertex
SCREAMING_SNAKE_CASE_ = self.parent.get(_lowerCamelCase )
if target_vertex_parent is None:
SCREAMING_SNAKE_CASE_ = (
f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
)
raise ValueError(_lowerCamelCase )
return self.shortest_path(_lowerCamelCase ) + f"->{target_vertex}"
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE =Graph(graph, """G""")
g.breath_first_search()
print(g.shortest_path("""D"""))
print(g.shortest_path("""G"""))
print(g.shortest_path("""Foo"""))
| 89 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase ) -> list[int]:
"""simple docstring"""
return [ord(_UpperCamelCase ) - 96 for elem in plain]
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
return "".join(chr(elem + 96 ) for elem in encoded )
def lowerCamelCase_ ( ) -> None:
"""simple docstring"""
snake_case_ : List[Any] = encode(input('''-> ''' ).strip().lower() )
print('''Encoded: ''' , _UpperCamelCase )
print('''Decoded:''' , decode(_UpperCamelCase ) )
if __name__ == "__main__":
main()
| 60 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case : Union[str, Any] = {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : Union[str, Any] = [
"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
_snake_case : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 441 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
_A : int = logging.get_logger(__name__)
_A : Any = {
'''openai/imagegpt-small''': '''''',
'''openai/imagegpt-medium''': '''''',
'''openai/imagegpt-large''': '''''',
}
class _lowercase ( __lowerCamelCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : List[Any] = """imagegpt"""
_SCREAMING_SNAKE_CASE : Optional[Any] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE : Optional[int] = {
"""hidden_size""": """n_embd""",
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : Dict=5_12 + 1 , SCREAMING_SNAKE_CASE__ : int=32 * 32 , SCREAMING_SNAKE_CASE__ : Optional[Any]=5_12 , SCREAMING_SNAKE_CASE__ : List[Any]=24 , SCREAMING_SNAKE_CASE__ : Any=8 , SCREAMING_SNAKE_CASE__ : int=None , SCREAMING_SNAKE_CASE__ : List[str]="quick_gelu" , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.1 , SCREAMING_SNAKE_CASE__ : Optional[Any]=0.1 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.1 , SCREAMING_SNAKE_CASE__ : Optional[int]=1e-5 , SCREAMING_SNAKE_CASE__ : Dict=0.0_2 , SCREAMING_SNAKE_CASE__ : List[str]=True , SCREAMING_SNAKE_CASE__ : List[str]=True , SCREAMING_SNAKE_CASE__ : List[Any]=False , SCREAMING_SNAKE_CASE__ : List[Any]=False , SCREAMING_SNAKE_CASE__ : Any=False , **SCREAMING_SNAKE_CASE__ : List[str] , ) -> Optional[Any]:
__lowerCAmelCase = vocab_size
__lowerCAmelCase = n_positions
__lowerCAmelCase = n_embd
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
__lowerCAmelCase = n_inner
__lowerCAmelCase = activation_function
__lowerCAmelCase = resid_pdrop
__lowerCAmelCase = embd_pdrop
__lowerCAmelCase = attn_pdrop
__lowerCAmelCase = layer_norm_epsilon
__lowerCAmelCase = initializer_range
__lowerCAmelCase = scale_attn_weights
__lowerCAmelCase = use_cache
__lowerCAmelCase = scale_attn_by_inverse_layer_idx
__lowerCAmelCase = reorder_and_upcast_attn
__lowerCAmelCase = tie_word_embeddings
super().__init__(tie_word_embeddings=UpperCAmelCase_ , **UpperCAmelCase_ )
class _lowercase ( __lowerCamelCase ):
'''simple docstring'''
@property
def a ( self : Optional[Any] ) -> Any:
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
] )
def a ( self : List[Any] , SCREAMING_SNAKE_CASE__ : "FeatureExtractionMixin" , SCREAMING_SNAKE_CASE__ : int = 1 , SCREAMING_SNAKE_CASE__ : int = -1 , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : Optional["TensorType"] = None , SCREAMING_SNAKE_CASE__ : int = 3 , SCREAMING_SNAKE_CASE__ : int = 32 , SCREAMING_SNAKE_CASE__ : int = 32 , ) -> List[Any]:
__lowerCAmelCase = self._generate_dummy_images(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
__lowerCAmelCase = dict(preprocessor(images=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ ) )
return inputs
| 706 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Union[str, Any] = logging.get_logger(__name__)
_A : Union[str, Any] = {
'''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 _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : str = """dpr"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : Tuple=3_05_22 , SCREAMING_SNAKE_CASE__ : List[Any]=7_68 , SCREAMING_SNAKE_CASE__ : int=12 , SCREAMING_SNAKE_CASE__ : List[str]=12 , SCREAMING_SNAKE_CASE__ : Tuple=30_72 , SCREAMING_SNAKE_CASE__ : int="gelu" , SCREAMING_SNAKE_CASE__ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE__ : str=0.1 , SCREAMING_SNAKE_CASE__ : List[str]=5_12 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=2 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.0_2 , SCREAMING_SNAKE_CASE__ : Optional[Any]=1e-1_2 , SCREAMING_SNAKE_CASE__ : Optional[Any]=0 , SCREAMING_SNAKE_CASE__ : int="absolute" , SCREAMING_SNAKE_CASE__ : int = 0 , **SCREAMING_SNAKE_CASE__ : str , ) -> Tuple:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = hidden_act
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = type_vocab_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = projection_dim
__lowerCAmelCase = position_embedding_type
| 330 | 0 |
"""simple docstring"""
from collections import namedtuple
import requests
from lxml import html # type: ignore
__lowerCamelCase = namedtuple('covid_data', 'cases deaths recovered')
def a ( __UpperCAmelCase : str = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
__magic_name__: Dict = """//div[@class = \"maincounter-number\"]/span/text()"""
return covid_data(*html.fromstring(requests.get(__UpperCAmelCase ).content ).xpath(__UpperCAmelCase ) )
__lowerCamelCase = 'Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'
print(fmt.format(*covid_stats()))
| 96 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def lowercase (_lowerCAmelCase ):
if is_torch_version("""<""" , """2.0.0""" ) or not hasattr(_lowerCAmelCase , """_dynamo""" ):
return False
return isinstance(_lowerCAmelCase , torch._dynamo.eval_frame.OptimizedModule )
def lowercase (_lowerCAmelCase , _lowerCAmelCase = True ):
__lowerCAmelCase = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
__lowerCAmelCase = is_compiled_module(_lowerCAmelCase )
if is_compiled:
__lowerCAmelCase = model
__lowerCAmelCase = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(_lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = model.module
if not keep_fpaa_wrapper:
__lowerCAmelCase = getattr(_lowerCAmelCase , """forward""" )
__lowerCAmelCase = model.__dict__.pop("""_original_forward""" , _lowerCAmelCase )
if original_forward is not None:
while hasattr(_lowerCAmelCase , """__wrapped__""" ):
__lowerCAmelCase = forward.__wrapped__
if forward == original_forward:
break
__lowerCAmelCase = forward
if getattr(_lowerCAmelCase , """_converted_to_transformer_engine""" , _lowerCAmelCase ):
convert_model(_lowerCAmelCase , to_transformer_engine=_lowerCAmelCase )
if is_compiled:
__lowerCAmelCase = model
__lowerCAmelCase = compiled_model
return model
def lowercase ():
PartialState().wait_for_everyone()
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
if PartialState().distributed_type == DistributedType.TPU:
xm.save(_lowerCAmelCase , _lowerCAmelCase )
elif PartialState().local_process_index == 0:
torch.save(_lowerCAmelCase , _lowerCAmelCase )
@contextmanager
def lowercase (**_lowerCAmelCase ):
for key, value in kwargs.items():
__lowerCAmelCase = str(_lowerCAmelCase )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def lowercase (_lowerCAmelCase ):
if not hasattr(_lowerCAmelCase , """__qualname__""" ) and not hasattr(_lowerCAmelCase , """__name__""" ):
__lowerCAmelCase = getattr(_lowerCAmelCase , """__class__""" , _lowerCAmelCase )
if hasattr(_lowerCAmelCase , """__qualname__""" ):
return obj.__qualname__
if hasattr(_lowerCAmelCase , """__name__""" ):
return obj.__name__
return str(_lowerCAmelCase )
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
for key, value in source.items():
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = destination.setdefault(_lowerCAmelCase , {} )
merge_dicts(_lowerCAmelCase , _lowerCAmelCase )
else:
__lowerCAmelCase = value
return destination
def lowercase (_lowerCAmelCase = None ):
if port is None:
__lowerCAmelCase = 2_9500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(("""localhost""", port) ) == 0
| 465 | 0 |
def UpperCamelCase__ ( lowerCAmelCase__ ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
2: [4, 5],
3: [4],
4: [2, 3],
5: [2],
6: [0, 7],
7: [6],
8: [],
9: [1],
},
{
0: [4],
1: [6],
2: [],
3: [5, 6, 7],
4: [0, 6],
5: [3, 8, 9],
6: [1, 3, 4, 7],
7: [3, 6, 8, 9],
8: [5, 7],
9: [5, 7],
},
{
0: [1, 3],
1: [0, 2, 4],
2: [1, 3, 4],
3: [0, 2, 4],
4: [1, 2, 3],
},
][index]
def UpperCamelCase__ ( lowerCAmelCase__ ):
lowercase = 0
lowercase = len(__SCREAMING_SNAKE_CASE ) # No of vertices in graph
lowercase = [0] * n
lowercase = [False] * n
def dfs(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
lowercase = True
lowercase = id_
id_ += 1
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
dfs(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,id_ )
lowercase = min(low[at] ,low[to] )
if id_ <= low[to]:
bridges.append((at, to) if at < to else (to, at) )
else:
# This edge is a back edge and cannot be a bridge
lowercase = min(low[at] ,low[to] )
lowercase = []
for i in range(__SCREAMING_SNAKE_CASE ):
if not visited[i]:
dfs(__SCREAMING_SNAKE_CASE ,-1 ,__SCREAMING_SNAKE_CASE ,id_ )
return bridges
if __name__ == "__main__":
import doctest
doctest.testmod()
| 703 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : str =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : str ={
'''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class A_ ( __a ):
_A :Tuple = '''data2vec-audio'''
def __init__( self : Optional[Any] , snake_case__ : List[Any]=32 , snake_case__ : List[Any]=7_68 , snake_case__ : int=12 , snake_case__ : Dict=12 , snake_case__ : List[str]=30_72 , snake_case__ : List[str]="gelu" , snake_case__ : Optional[int]=0.1 , snake_case__ : List[Any]=0.1 , snake_case__ : int=0.1 , snake_case__ : Tuple=0.0 , snake_case__ : Tuple=0.1 , snake_case__ : Any=0.1 , snake_case__ : Dict=0.02 , snake_case__ : List[str]=1E-5 , snake_case__ : Optional[Any]="gelu" , snake_case__ : Union[str, Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , snake_case__ : List[str]=(5, 2, 2, 2, 2, 2, 2) , snake_case__ : str=(10, 3, 3, 3, 3, 2, 2) , snake_case__ : Any=False , snake_case__ : List[str]=16 , snake_case__ : Any=19 , snake_case__ : Optional[Any]=5 , snake_case__ : str=0.05 , snake_case__ : Tuple=10 , snake_case__ : Optional[Any]=2 , snake_case__ : Dict=0.0 , snake_case__ : int=10 , snake_case__ : Any=0 , snake_case__ : int="sum" , snake_case__ : str=False , snake_case__ : str=False , snake_case__ : Optional[int]=2_56 , snake_case__ : List[str]=(5_12, 5_12, 5_12, 5_12, 15_00) , snake_case__ : List[str]=(5, 3, 3, 1, 1) , snake_case__ : int=(1, 2, 3, 1, 1) , snake_case__ : Optional[Any]=5_12 , snake_case__ : Dict=0 , snake_case__ : Optional[Any]=1 , snake_case__ : Tuple=2 , snake_case__ : Tuple=False , snake_case__ : List[str]=3 , snake_case__ : List[str]=2 , snake_case__ : Tuple=3 , snake_case__ : List[str]=None , **snake_case__ : str , ):
super().__init__(**snake_case__ , pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ )
lowercase = hidden_size
lowercase = feat_extract_activation
lowercase = list(snake_case__ )
lowercase = list(snake_case__ )
lowercase = list(snake_case__ )
lowercase = conv_bias
lowercase = num_conv_pos_embeddings
lowercase = num_conv_pos_embedding_groups
lowercase = conv_pos_kernel_size
lowercase = len(self.conv_dim )
lowercase = num_hidden_layers
lowercase = intermediate_size
lowercase = hidden_act
lowercase = num_attention_heads
lowercase = hidden_dropout
lowercase = attention_dropout
lowercase = activation_dropout
lowercase = feat_proj_dropout
lowercase = final_dropout
lowercase = layerdrop
lowercase = layer_norm_eps
lowercase = initializer_range
lowercase = vocab_size
lowercase = use_weighted_layer_sum
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
lowercase = mask_time_prob
lowercase = mask_time_length
lowercase = mask_time_min_masks
lowercase = mask_feature_prob
lowercase = mask_feature_length
lowercase = mask_feature_min_masks
# ctc loss
lowercase = ctc_loss_reduction
lowercase = ctc_zero_infinity
# adapter
lowercase = add_adapter
lowercase = adapter_kernel_size
lowercase = adapter_stride
lowercase = num_adapter_layers
lowercase = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowercase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowercase = list(snake_case__ )
lowercase = list(snake_case__ )
lowercase = list(snake_case__ )
lowercase = xvector_output_dim
@property
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
return math.prod(self.conv_stride )
| 72 | 0 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _UpperCAmelCase ( ):
"""simple docstring"""
__lowerCAmelCase = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
__lowerCAmelCase = parser.add_subparsers(help="diffusers-cli command helpers" )
# Register commands
EnvironmentCommand.register_subcommand(UpperCamelCase )
# Let's go
__lowerCAmelCase = parser.parse_args()
if not hasattr(UpperCamelCase , "func" ):
parser.print_help()
exit(1 )
# Run
__lowerCAmelCase = args.func(UpperCamelCase )
service.run()
if __name__ == "__main__":
main()
| 611 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}
class a ( __UpperCAmelCase ):
lowercase_ : Optional[int] = 'layoutlmv3'
def __init__( self : Dict , snake_case__ : Dict=50_265 , snake_case__ : Optional[Any]=768 , snake_case__ : Dict=12 , snake_case__ : List[Any]=12 , snake_case__ : int=3_072 , snake_case__ : Dict="gelu" , snake_case__ : Any=0.1 , snake_case__ : Optional[Any]=0.1 , snake_case__ : Tuple=512 , snake_case__ : str=2 , snake_case__ : Optional[int]=0.0_2 , snake_case__ : Optional[Any]=1E-5 , snake_case__ : Tuple=1 , snake_case__ : str=0 , snake_case__ : Dict=2 , snake_case__ : int=1_024 , snake_case__ : Optional[Any]=128 , snake_case__ : List[str]=128 , snake_case__ : Dict=True , snake_case__ : Optional[int]=32 , snake_case__ : str=128 , snake_case__ : Dict=64 , snake_case__ : Any=256 , snake_case__ : Union[str, Any]=True , snake_case__ : Union[str, Any]=True , snake_case__ : Tuple=True , snake_case__ : List[Any]=224 , snake_case__ : str=3 , snake_case__ : Dict=16 , snake_case__ : Tuple=None , **snake_case__ : Any , ):
"""simple docstring"""
super().__init__(
vocab_size=snake_case__ , hidden_size=snake_case__ , num_hidden_layers=snake_case__ , num_attention_heads=snake_case__ , intermediate_size=snake_case__ , hidden_act=snake_case__ , hidden_dropout_prob=snake_case__ , attention_probs_dropout_prob=snake_case__ , max_position_embeddings=snake_case__ , type_vocab_size=snake_case__ , initializer_range=snake_case__ , layer_norm_eps=snake_case__ , pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ , )
__lowerCAmelCase = max_ad_position_embeddings
__lowerCAmelCase = coordinate_size
__lowerCAmelCase = shape_size
__lowerCAmelCase = has_relative_attention_bias
__lowerCAmelCase = rel_pos_bins
__lowerCAmelCase = max_rel_pos
__lowerCAmelCase = has_spatial_attention_bias
__lowerCAmelCase = rel_ad_pos_bins
__lowerCAmelCase = max_rel_ad_pos
__lowerCAmelCase = text_embed
__lowerCAmelCase = visual_embed
__lowerCAmelCase = input_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = patch_size
__lowerCAmelCase = classifier_dropout
class a ( __UpperCAmelCase ):
lowercase_ : int = version.parse('1.12' )
@property
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
if self.task in ["question-answering", "sequence-classification"]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("attention_mask", {0: "batch", 1: "sequence"}),
("bbox", {0: "batch", 1: "sequence"}),
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
else:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("bbox", {0: "batch", 1: "sequence"}),
("attention_mask", {0: "batch", 1: "sequence"}),
("pixel_values", {0: "batch", 1: "num_channels"}),
] )
@property
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
return 1E-5
@property
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
return 12
def UpperCAmelCase__ ( self : Dict , snake_case__ : "ProcessorMixin" , snake_case__ : int = -1 , snake_case__ : int = -1 , snake_case__ : bool = False , snake_case__ : Optional["TensorType"] = None , snake_case__ : int = 3 , snake_case__ : int = 40 , snake_case__ : int = 40 , ):
"""simple docstring"""
setattr(processor.image_processor , "apply_ocr" , snake_case__ )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
__lowerCAmelCase = compute_effective_axis_dimension(
snake_case__ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
__lowerCAmelCase = processor.tokenizer.num_special_tokens_to_add(snake_case__ )
__lowerCAmelCase = compute_effective_axis_dimension(
snake_case__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case__ )
# Generate dummy inputs according to compute batch and sequence
__lowerCAmelCase = [[" ".join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
__lowerCAmelCase = [[[48, 84, 73, 128]]] * batch_size
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
__lowerCAmelCase = self._generate_dummy_images(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
__lowerCAmelCase = dict(
processor(
snake_case__ , text=snake_case__ , boxes=snake_case__ , return_tensors=snake_case__ , ) )
return inputs
| 611 | 1 |
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
__UpperCAmelCase = """src/diffusers"""
__UpperCAmelCase = """."""
# This is to make sure the diffusers module imported is the one in the repo.
__UpperCAmelCase = importlib.util.spec_from_file_location(
"""diffusers""",
os.path.join(DIFFUSERS_PATH, """__init__.py"""),
submodule_search_locations=[DIFFUSERS_PATH],
)
__UpperCAmelCase = spec.loader.load_module()
def _lowerCamelCase ( A_ : Union[str, Any] , A_ : Dict ) -> List[str]:
'''simple docstring'''
return line.startswith(A_ ) or len(A_ ) <= 1 or re.search(R"^\s*\)(\s*->.*:|:)\s*$" , A_ ) is not None
def _lowerCamelCase ( A_ : Optional[Any] ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ : Any =object_name.split("." )
UpperCamelCase__ : List[Any] =0
# First let's find the module where our object lives.
UpperCamelCase__ : Optional[int] =parts[i]
while i < len(A_ ) and not os.path.isfile(os.path.join(A_ , f'''{module}.py''' ) ):
i += 1
if i < len(A_ ):
UpperCamelCase__ : List[Any] =os.path.join(A_ , parts[i] )
if i >= len(A_ ):
raise ValueError(f'''`object_name` should begin with the name of a module of diffusers but got {object_name}.''' )
with open(os.path.join(A_ , f'''{module}.py''' ) , "r" , encoding="utf-8" , newline="\n" ) as f:
UpperCamelCase__ : Union[str, Any] =f.readlines()
# Now let's find the class / func in the code!
UpperCamelCase__ : str =""
UpperCamelCase__ : Optional[Any] =0
for name in parts[i + 1 :]:
while (
line_index < len(A_ ) 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(A_ ):
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__ : Union[str, Any] =line_index
while line_index < len(A_ ) and _should_continue(lines[line_index] , A_ ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
UpperCamelCase__ : Tuple =lines[start_index:line_index]
return "".join(A_ )
__UpperCAmelCase = re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
__UpperCAmelCase = re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
__UpperCAmelCase = re.compile(r"""<FILL\s+[^>]*>""")
def _lowerCamelCase ( A_ : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] =code.split("\n" )
UpperCamelCase__ : Any =0
while idx < len(A_ ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(A_ ):
return re.search(R"^(\s*)\S" , lines[idx] ).groups()[0]
return ""
def _lowerCamelCase ( A_ : Optional[int] ) -> Any:
'''simple docstring'''
UpperCamelCase__ : int =len(get_indent(A_ ) ) > 0
if has_indent:
UpperCamelCase__ : List[Any] =f'''class Bla:\n{code}'''
UpperCamelCase__ : Optional[int] =black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_1_9 , preview=A_ )
UpperCamelCase__ : Tuple =black.format_str(A_ , mode=A_ )
UpperCamelCase__ , UpperCamelCase__ : List[Any] =style_docstrings_in_code(A_ )
return result[len("class Bla:\n" ) :] if has_indent else result
def _lowerCamelCase ( A_ : Union[str, Any] , A_ : List[str]=False ) -> Union[str, Any]:
'''simple docstring'''
with open(A_ , "r" , encoding="utf-8" , newline="\n" ) as f:
UpperCamelCase__ : List[Any] =f.readlines()
UpperCamelCase__ : List[str] =[]
UpperCamelCase__ : List[str] =0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(A_ ):
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__ : Optional[Any] =search.groups()
UpperCamelCase__ : List[str] =find_code_in_diffusers(A_ )
UpperCamelCase__ : str =get_indent(A_ )
UpperCamelCase__ : Optional[int] =line_index + 1 if indent == theoretical_indent else line_index + 2
UpperCamelCase__ : str =theoretical_indent
UpperCamelCase__ : int =start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
UpperCamelCase__ : List[str] =True
while line_index < len(A_ ) and should_continue:
line_index += 1
if line_index >= len(A_ ):
break
UpperCamelCase__ : int =lines[line_index]
UpperCamelCase__ : List[str] =_should_continue(A_ , A_ ) and re.search(f'''^{indent}# End copy''' , A_ ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
UpperCamelCase__ : str =lines[start_index:line_index]
UpperCamelCase__ : Dict ="".join(A_ )
# Remove any nested `Copied from` comments to avoid circular copies
UpperCamelCase__ : Optional[int] =[line for line in theoretical_code.split("\n" ) if _re_copy_warning.search(A_ ) is None]
UpperCamelCase__ : Dict ="\n".join(A_ )
# Before comparing, use the `replace_pattern` on the original code.
if len(A_ ) > 0:
UpperCamelCase__ : Any =replace_pattern.replace("with" , "" ).split("," )
UpperCamelCase__ : Union[str, Any] =[_re_replace_pattern.search(A_ ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : str =pattern.groups()
UpperCamelCase__ : List[str] =re.sub(A_ , A_ , A_ )
if option.strip() == "all-casing":
UpperCamelCase__ : Any =re.sub(obja.lower() , obja.lower() , A_ )
UpperCamelCase__ : List[str] =re.sub(obja.upper() , obja.upper() , A_ )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
UpperCamelCase__ : Dict =blackify(lines[start_index - 1] + theoretical_code )
UpperCamelCase__ : int =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__ : List[Any] =lines[:start_index] + [theoretical_code] + lines[line_index:]
UpperCamelCase__ : List[Any] =start_index + 1
if overwrite and len(A_ ) > 0:
# Warn the user a file has been modified.
print(f'''Detected changes, rewriting {filename}.''' )
with open(A_ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(A_ )
return diffs
def _lowerCamelCase ( A_ : bool = False ) -> int:
'''simple docstring'''
UpperCamelCase__ : List[str] =glob.glob(os.path.join(A_ , "**/*.py" ) , recursive=A_ )
UpperCamelCase__ : int =[]
for filename in all_files:
UpperCamelCase__ : str =is_copy_consistent(A_ , A_ )
diffs += [f'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
if not overwrite and len(A_ ) > 0:
UpperCamelCase__ : Any ="\n".join(A_ )
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__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
__UpperCAmelCase = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 582 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
__UpperCAmelCase = parser.parse_args()
if args.model_type == "roberta":
__UpperCAmelCase = RobertaForMaskedLM.from_pretrained(args.model_name)
__UpperCAmelCase = """roberta"""
elif args.model_type == "gpt2":
__UpperCAmelCase = GPTaLMHeadModel.from_pretrained(args.model_name)
__UpperCAmelCase = """transformer"""
__UpperCAmelCase = model.state_dict()
__UpperCAmelCase = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
__UpperCAmelCase = state_dict[F"""{prefix}.{param_name}"""]
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
__UpperCAmelCase = F"""{prefix}.embeddings.{w}.weight"""
__UpperCAmelCase = state_dict[param_name]
for w in ["weight", "bias"]:
__UpperCAmelCase = F"""{prefix}.embeddings.LayerNorm.{w}"""
__UpperCAmelCase = state_dict[param_name]
# Transformer Blocks #
__UpperCAmelCase = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
__UpperCAmelCase = state_dict[
F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
]
__UpperCAmelCase = state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
__UpperCAmelCase = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
__UpperCAmelCase = state_dict[F"""{layer}"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
__UpperCAmelCase = state_dict[F"""lm_head.dense.{w}"""]
__UpperCAmelCase = state_dict[F"""lm_head.layer_norm.{w}"""]
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
__UpperCAmelCase = state_dict[F"""{prefix}.ln_f.{w}"""]
__UpperCAmelCase = state_dict["""lm_head.weight"""]
print(F"""N layers selected for distillation: {std_idx}""")
print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 582 | 1 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_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 SCREAMING_SNAKE_CASE ( a_ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] =BertTokenizer
lowerCamelCase : List[Any] =BertTokenizerFast
lowerCamelCase : List[str] =True
lowerCamelCase : Any =True
lowerCamelCase : Dict =filter_non_english
def SCREAMING_SNAKE_CASE ( self : int ) -> Optional[Any]:
"""simple docstring"""
super().setUp()
__lowerCAmelCase : Any = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
__lowerCAmelCase : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase : str ) -> Any:
"""simple docstring"""
__lowerCAmelCase : List[str] = """UNwant\u00E9d,running"""
__lowerCAmelCase : Optional[int] = """unwanted, running"""
return input_text, output_text
def SCREAMING_SNAKE_CASE ( self : str ) -> str:
"""simple docstring"""
__lowerCAmelCase : List[str] = self.tokenizer_class(self.vocab_file )
__lowerCAmelCase : List[str] = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(lowerCAmelCase , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , [9, 6, 7, 12, 10, 11] )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Any:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__lowerCAmelCase : List[str] = self.get_tokenizer()
__lowerCAmelCase : Any = self.get_rust_tokenizer()
__lowerCAmelCase : List[str] = """UNwant\u00E9d,running"""
__lowerCAmelCase : str = tokenizer.tokenize(lowerCAmelCase )
__lowerCAmelCase : Optional[int] = rust_tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : Dict = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
__lowerCAmelCase : Any = rust_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : str = self.get_rust_tokenizer()
__lowerCAmelCase : Any = tokenizer.encode(lowerCAmelCase )
__lowerCAmelCase : List[Any] = rust_tokenizer.encode(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
# With lower casing
__lowerCAmelCase : int = self.get_tokenizer(do_lower_case=lowerCAmelCase )
__lowerCAmelCase : Optional[int] = self.get_rust_tokenizer(do_lower_case=lowerCAmelCase )
__lowerCAmelCase : Optional[Any] = """UNwant\u00E9d,running"""
__lowerCAmelCase : Any = tokenizer.tokenize(lowerCAmelCase )
__lowerCAmelCase : Optional[int] = rust_tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : Tuple = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
__lowerCAmelCase : Optional[Any] = rust_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : Any = self.get_rust_tokenizer()
__lowerCAmelCase : str = tokenizer.encode(lowerCAmelCase )
__lowerCAmelCase : str = rust_tokenizer.encode(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Dict = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def SCREAMING_SNAKE_CASE ( self : str ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : int = BasicTokenizer(do_lower_case=lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : List[str] = BasicTokenizer(do_lower_case=lowerCAmelCase , strip_accents=lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def SCREAMING_SNAKE_CASE ( self : int ) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Dict = BasicTokenizer(do_lower_case=lowerCAmelCase , strip_accents=lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> int:
"""simple docstring"""
__lowerCAmelCase : Tuple = BasicTokenizer(do_lower_case=lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
"""simple docstring"""
__lowerCAmelCase : Any = BasicTokenizer(do_lower_case=lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = BasicTokenizer(do_lower_case=lowerCAmelCase , strip_accents=lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def SCREAMING_SNAKE_CASE ( self : Dict ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = BasicTokenizer(do_lower_case=lowerCAmelCase , strip_accents=lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Tuple = BasicTokenizer(do_lower_case=lowerCAmelCase , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def SCREAMING_SNAKE_CASE ( self : int ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Tuple = BasicTokenizer()
__lowerCAmelCase : Dict = """a\n'll !!to?'d of, can't."""
__lowerCAmelCase : int = ["""a""", """'""", """ll""", """!""", """!""", """to""", """?""", """'""", """d""", """of""", """,""", """can""", """'""", """t""", """."""]
self.assertListEqual(tokenizer.tokenize(lowerCAmelCase ) , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Dict ) -> str:
"""simple docstring"""
__lowerCAmelCase : Tuple = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
__lowerCAmelCase : Tuple = {}
for i, token in enumerate(lowerCAmelCase ):
__lowerCAmelCase : Optional[int] = i
__lowerCAmelCase : Optional[int] = WordpieceTokenizer(vocab=lowerCAmelCase , 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 SCREAMING_SNAKE_CASE ( self : int ) -> Any:
"""simple docstring"""
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Tuple:
"""simple docstring"""
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Tuple:
"""simple docstring"""
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = self.get_tokenizer()
__lowerCAmelCase : Dict = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(lowerCAmelCase ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
self.assertListEqual(
[rust_tokenizer.tokenize(lowerCAmelCase ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = self.tokenizer_class.from_pretrained("""bert-base-uncased""" )
__lowerCAmelCase : List[Any] = tokenizer.encode("""sequence builders""" , add_special_tokens=lowerCAmelCase )
__lowerCAmelCase : int = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowerCAmelCase )
__lowerCAmelCase : List[str] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase )
__lowerCAmelCase : Optional[int] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase , lowerCAmelCase )
assert encoded_sentence == [1_01] + text + [1_02]
assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__lowerCAmelCase : List[str] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase , **lowerCAmelCase )
__lowerCAmelCase : Any = f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
__lowerCAmelCase : List[Any] = tokenizer_r.encode_plus(
lowerCAmelCase , return_attention_mask=lowerCAmelCase , return_token_type_ids=lowerCAmelCase , return_offsets_mapping=lowerCAmelCase , add_special_tokens=lowerCAmelCase , )
__lowerCAmelCase : List[Any] = tokenizer_r.do_lower_case if hasattr(lowerCAmelCase , """do_lower_case""" ) else False
__lowerCAmelCase : Any = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), """A"""),
((1, 2), ""","""),
((3, 5), """na"""),
((5, 6), """##ï"""),
((6, 8), """##ve"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """Allen"""),
((21, 23), """##NL"""),
((23, 24), """##P"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((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, 15), tokenizer_r.mask_token),
((16, 21), """allen"""),
((21, 23), """##nl"""),
((23, 24), """##p"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((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 SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : str = ["""的""", """人""", """有"""]
__lowerCAmelCase : Tuple = """""".join(lowerCAmelCase )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__lowerCAmelCase : Dict = True
__lowerCAmelCase : Dict = self.tokenizer_class.from_pretrained(lowerCAmelCase , **lowerCAmelCase )
__lowerCAmelCase : Any = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase , **lowerCAmelCase )
__lowerCAmelCase : Dict = tokenizer_p.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = tokenizer_r.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
__lowerCAmelCase : Optional[int] = tokenizer_r.convert_ids_to_tokens(lowerCAmelCase )
__lowerCAmelCase : int = tokenizer_p.convert_ids_to_tokens(lowerCAmelCase )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : Optional[Any] = False
__lowerCAmelCase : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase , **lowerCAmelCase )
__lowerCAmelCase : Tuple = self.tokenizer_class.from_pretrained(lowerCAmelCase , **lowerCAmelCase )
__lowerCAmelCase : Any = tokenizer_r.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
__lowerCAmelCase : Tuple = tokenizer_p.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
__lowerCAmelCase : Tuple = tokenizer_r.convert_ids_to_tokens(lowerCAmelCase )
__lowerCAmelCase : List[Any] = tokenizer_p.convert_ids_to_tokens(lowerCAmelCase )
# it is expected that only the first Chinese character is not preceded by "##".
__lowerCAmelCase : Tuple = [
f'''##{token}''' if idx != 0 else token for idx, token in enumerate(lowerCAmelCase )
]
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
| 651 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
__UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"""text-classification""",
"""language-modeling""",
"""summarization""",
"""token-classification""",
"""question-answering""",
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
__UpperCAmelCase = logging.getLogger()
def snake_case_ () -> Optional[Any]:
__lowerCAmelCase : Tuple = argparse.ArgumentParser()
parser.add_argument("""-f""" )
__lowerCAmelCase : Dict = parser.parse_args()
return args.f
def snake_case_ (__A : Dict , __A : List[str]="eval" ) -> int:
__lowerCAmelCase : int = os.path.join(__A , f'''{split}_results.json''' )
if os.path.exists(__A ):
with open(__A , """r""" ) as f:
return json.load(__A )
raise ValueError(f'''can\'t find {path}''' )
__UpperCAmelCase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Tuple = self.get_auto_remove_tmp_dir()
__lowerCAmelCase : Optional[Any] = f'''
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
'''.split()
with patch.object(lowerCAmelCase , """argv""" , lowerCAmelCase ):
run_flax_glue.main()
__lowerCAmelCase : Dict = get_results(lowerCAmelCase )
self.assertGreaterEqual(result["""eval_accuracy"""] , 0.75 )
@slow
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = self.get_auto_remove_tmp_dir()
__lowerCAmelCase : Any = f'''
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(lowerCAmelCase , """argv""" , lowerCAmelCase ):
run_clm_flax.main()
__lowerCAmelCase : int = get_results(lowerCAmelCase )
self.assertLess(result["""eval_perplexity"""] , 1_00 )
@slow
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Tuple = self.get_auto_remove_tmp_dir()
__lowerCAmelCase : int = f'''
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
'''.split()
with patch.object(lowerCAmelCase , """argv""" , lowerCAmelCase ):
run_summarization_flax.main()
__lowerCAmelCase : Union[str, Any] = get_results(lowerCAmelCase , split="""test""" )
self.assertGreaterEqual(result["""test_rouge1"""] , 10 )
self.assertGreaterEqual(result["""test_rouge2"""] , 2 )
self.assertGreaterEqual(result["""test_rougeL"""] , 7 )
self.assertGreaterEqual(result["""test_rougeLsum"""] , 7 )
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Tuple = self.get_auto_remove_tmp_dir()
__lowerCAmelCase : List[str] = f'''
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
'''.split()
with patch.object(lowerCAmelCase , """argv""" , lowerCAmelCase ):
run_mlm_flax.main()
__lowerCAmelCase : List[Any] = get_results(lowerCAmelCase )
self.assertLess(result["""eval_perplexity"""] , 42 )
@slow
def SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = self.get_auto_remove_tmp_dir()
__lowerCAmelCase : List[str] = f'''
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(lowerCAmelCase , """argv""" , lowerCAmelCase ):
run_ta_mlm_flax.main()
__lowerCAmelCase : Union[str, Any] = get_results(lowerCAmelCase )
self.assertGreaterEqual(result["""eval_accuracy"""] , 0.42 )
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowerCAmelCase : List[Any] = 7 if get_gpu_count() > 1 else 2
__lowerCAmelCase : Optional[int] = self.get_auto_remove_tmp_dir()
__lowerCAmelCase : List[Any] = f'''
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
'''.split()
with patch.object(lowerCAmelCase , """argv""" , lowerCAmelCase ):
run_flax_ner.main()
__lowerCAmelCase : Dict = get_results(lowerCAmelCase )
self.assertGreaterEqual(result["""eval_accuracy"""] , 0.75 )
self.assertGreaterEqual(result["""eval_f1"""] , 0.3 )
@slow
def SCREAMING_SNAKE_CASE ( self : int ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = self.get_auto_remove_tmp_dir()
__lowerCAmelCase : List[str] = f'''
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
'''.split()
with patch.object(lowerCAmelCase , """argv""" , lowerCAmelCase ):
run_qa.main()
__lowerCAmelCase : Union[str, Any] = get_results(lowerCAmelCase )
self.assertGreaterEqual(result["""eval_f1"""] , 30 )
self.assertGreaterEqual(result["""eval_exact"""] , 30 )
| 651 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ = logging.get_logger(__name__)
a__ = {
"""ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}
class __magic_name__( SCREAMING_SNAKE_CASE__ ):
UpperCAmelCase_ : Dict = """deta"""
UpperCAmelCase_ : str = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self : Union[str, Any] , __UpperCamelCase : int=None , __UpperCamelCase : str=9_0_0 , __UpperCamelCase : Optional[int]=2_0_4_8 , __UpperCamelCase : Optional[Any]=6 , __UpperCamelCase : Union[str, Any]=2_0_4_8 , __UpperCamelCase : Union[str, Any]=8 , __UpperCamelCase : str=6 , __UpperCamelCase : Optional[int]=1_0_2_4 , __UpperCamelCase : Any=8 , __UpperCamelCase : Any=0.0 , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : str="relu" , __UpperCamelCase : List[str]=2_5_6 , __UpperCamelCase : List[Any]=0.1 , __UpperCamelCase : str=0.0 , __UpperCamelCase : Union[str, Any]=0.0 , __UpperCamelCase : Union[str, Any]=0.02 , __UpperCamelCase : Optional[Any]=1.0 , __UpperCamelCase : List[Any]=True , __UpperCamelCase : Optional[Any]=False , __UpperCamelCase : Union[str, Any]="sine" , __UpperCamelCase : Optional[int]=5 , __UpperCamelCase : Dict=4 , __UpperCamelCase : Union[str, Any]=4 , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : Dict=3_0_0 , __UpperCamelCase : int=True , __UpperCamelCase : str=True , __UpperCamelCase : Dict=1 , __UpperCamelCase : Dict=5 , __UpperCamelCase : Dict=2 , __UpperCamelCase : Union[str, Any]=1 , __UpperCamelCase : Any=1 , __UpperCamelCase : Optional[int]=5 , __UpperCamelCase : Optional[Any]=2 , __UpperCamelCase : List[str]=0.1 , __UpperCamelCase : List[str]=0.25 , **__UpperCamelCase : Dict , ):
'''simple docstring'''
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
snake_case__ = CONFIG_MAPPING["resnet"](out_features=["""stage2""", """stage3""", """stage4"""] )
else:
if isinstance(snake_case__ , snake_case__ ):
snake_case__ = backbone_config.pop("""model_type""" )
snake_case__ = CONFIG_MAPPING[backbone_model_type]
snake_case__ = config_class.from_dict(snake_case__ )
snake_case__ = backbone_config
snake_case__ = num_queries
snake_case__ = max_position_embeddings
snake_case__ = d_model
snake_case__ = encoder_ffn_dim
snake_case__ = encoder_layers
snake_case__ = encoder_attention_heads
snake_case__ = decoder_ffn_dim
snake_case__ = decoder_layers
snake_case__ = decoder_attention_heads
snake_case__ = dropout
snake_case__ = attention_dropout
snake_case__ = activation_dropout
snake_case__ = activation_function
snake_case__ = init_std
snake_case__ = init_xavier_std
snake_case__ = encoder_layerdrop
snake_case__ = auxiliary_loss
snake_case__ = position_embedding_type
# deformable attributes
snake_case__ = num_feature_levels
snake_case__ = encoder_n_points
snake_case__ = decoder_n_points
snake_case__ = two_stage
snake_case__ = two_stage_num_proposals
snake_case__ = with_box_refine
snake_case__ = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError("""If two_stage is True, with_box_refine must be True.""" )
# Hungarian matcher
snake_case__ = class_cost
snake_case__ = bbox_cost
snake_case__ = giou_cost
# Loss coefficients
snake_case__ = mask_loss_coefficient
snake_case__ = dice_loss_coefficient
snake_case__ = bbox_loss_coefficient
snake_case__ = giou_loss_coefficient
snake_case__ = eos_coefficient
snake_case__ = focal_alpha
super().__init__(is_encoder_decoder=snake_case__ , **snake_case__ )
@property
def __lowerCAmelCase( self : str ):
'''simple docstring'''
return self.encoder_attention_heads
@property
def __lowerCAmelCase( self : str ):
'''simple docstring'''
return self.d_model
def __lowerCAmelCase( self : Optional[int] ):
'''simple docstring'''
snake_case__ = copy.deepcopy(self.__dict__ )
snake_case__ = self.backbone_config.to_dict()
snake_case__ = self.__class__.model_type
return output | 719 |
'''simple docstring'''
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
a__ = logging.get_logger(__name__)
def snake_case__ ( a , a ) -> Optional[int]:
'''simple docstring'''
snake_case__ = set()
snake_case__ = []
def parse_line(a ):
for line in fp:
if isinstance(a , a ):
snake_case__ = line.decode("""UTF-8""" )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(""" """ ):
# process a single warning and move it to `selected_warnings`.
if len(a ) > 0:
snake_case__ = """\n""".join(a )
# Only keep the warnings specified in `targets`
if any(F""": {x}: """ in warning for x in targets ):
selected_warnings.add(a )
buffer.clear()
continue
else:
snake_case__ = line.strip()
buffer.append(a )
if from_gh:
for filename in os.listdir(a ):
snake_case__ = os.path.join(a , a )
if not os.path.isdir(a ):
# read the file
if filename != "warnings.txt":
continue
with open(a ) as fp:
parse_line(a )
else:
try:
with zipfile.ZipFile(a ) as z:
for filename in z.namelist():
if not os.path.isdir(a ):
# read the file
if filename != "warnings.txt":
continue
with z.open(a ) as fp:
parse_line(a )
except Exception:
logger.warning(
F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.""" )
return selected_warnings
def snake_case__ ( a , a ) -> int:
'''simple docstring'''
snake_case__ = set()
snake_case__ = [os.path.join(a , a ) for p in os.listdir(a ) if (p.endswith(""".zip""" ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(a , a ) )
return selected_warnings
if __name__ == "__main__":
def snake_case__ ( a ) -> int:
'''simple docstring'''
return values.split(""",""" )
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
parser.add_argument(
'''--output_dir''',
type=str,
required=True,
help='''Where to store the downloaded artifacts and other result files.''',
)
parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
# optional parameters
parser.add_argument(
'''--targets''',
default='''DeprecationWarning,UserWarning,FutureWarning''',
type=list_str,
help='''Comma-separated list of target warning(s) which we want to extract.''',
)
parser.add_argument(
'''--from_gh''',
action='''store_true''',
help='''If running from a GitHub action workflow and collecting warnings from its artifacts.''',
)
a__ = parser.parse_args()
a__ = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
a__ = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print('''=''' * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
a__ = extract_warnings(args.output_dir, args.targets)
a__ = sorted(selected_warnings)
with open(os.path.join(args.output_dir, '''selected_warnings.json'''), '''w''', encoding='''UTF-8''') as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4) | 566 | 0 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : str )-> List[str]:
'''simple docstring'''
__snake_case = old_name
if "patch_embed" in old_name:
__snake_case , __snake_case , __snake_case = old_name.split('''.''' )
if layer == "0":
__snake_case = old_name.replace('''0''' , '''convolution1''' )
elif layer == "1":
__snake_case = old_name.replace('''1''' , '''batchnorm_before''' )
elif layer == "3":
__snake_case = old_name.replace('''3''' , '''convolution2''' )
else:
__snake_case = old_name.replace('''4''' , '''batchnorm_after''' )
if "network" in old_name and re.search(R'''\d\.\d''' , _lowerCamelCase ):
__snake_case = R'''\b\d{2}\b'''
if bool(re.search(_lowerCamelCase , _lowerCamelCase ) ):
__snake_case = re.search(R'''\d\.\d\d.''' , _lowerCamelCase ).group()
else:
__snake_case = re.search(R'''\d\.\d.''' , _lowerCamelCase ).group()
if int(match[0] ) < 6:
__snake_case = old_name.replace(_lowerCamelCase , '''''' )
__snake_case = trimmed_name.replace('''network''' , match[0] + '''.meta4D_layers.blocks.''' + match[2:-1] )
__snake_case = '''intermediate_stages.''' + trimmed_name
else:
__snake_case = old_name.replace(_lowerCamelCase , '''''' )
if int(match[2] ) < num_meta4D_last_stage:
__snake_case = trimmed_name.replace('''network''' , '''meta4D_layers.blocks.''' + match[2] )
else:
__snake_case = str(int(match[2] ) - num_meta4D_last_stage )
__snake_case = trimmed_name.replace('''network''' , '''meta3D_layers.blocks.''' + layer_index )
if "norm1" in old_name:
__snake_case = trimmed_name.replace('''norm1''' , '''layernorm1''' )
elif "norm2" in old_name:
__snake_case = trimmed_name.replace('''norm2''' , '''layernorm2''' )
elif "fc1" in old_name:
__snake_case = trimmed_name.replace('''fc1''' , '''linear_in''' )
elif "fc2" in old_name:
__snake_case = trimmed_name.replace('''fc2''' , '''linear_out''' )
__snake_case = '''last_stage.''' + trimmed_name
elif "network" in old_name and re.search(R'''.\d.''' , _lowerCamelCase ):
__snake_case = old_name.replace('''network''' , '''intermediate_stages''' )
if "fc" in new_name:
__snake_case = new_name.replace('''fc''' , '''convolution''' )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
__snake_case = new_name.replace('''norm1''' , '''batchnorm_before''' )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
__snake_case = new_name.replace('''norm2''' , '''batchnorm_after''' )
if "proj" in new_name:
__snake_case = new_name.replace('''proj''' , '''projection''' )
if "dist_head" in new_name:
__snake_case = new_name.replace('''dist_head''' , '''distillation_classifier''' )
elif "head" in new_name:
__snake_case = new_name.replace('''head''' , '''classifier''' )
elif "patch_embed" in new_name:
__snake_case = '''efficientformer.''' + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
__snake_case = new_name.replace('''norm''' , '''layernorm''' )
__snake_case = '''efficientformer.''' + new_name
else:
__snake_case = '''efficientformer.encoder.''' + new_name
return new_name
def _UpperCamelCase (_lowerCamelCase : str , _lowerCamelCase : Tuple )-> List[str]:
'''simple docstring'''
for key in checkpoint.copy().keys():
__snake_case = checkpoint.pop(_lowerCamelCase )
__snake_case = val
return checkpoint
def _UpperCamelCase ()-> Tuple:
'''simple docstring'''
__snake_case = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
__snake_case = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase ).raw )
return image
def _UpperCamelCase (_lowerCamelCase : Path , _lowerCamelCase : Path , _lowerCamelCase : Path , _lowerCamelCase : bool )-> Optional[Any]:
'''simple docstring'''
__snake_case = torch.load(_lowerCamelCase , map_location='''cpu''' )['''model''']
__snake_case = EfficientFormerConfig.from_json_file(_lowerCamelCase )
__snake_case = EfficientFormerForImageClassificationWithTeacher(_lowerCamelCase )
__snake_case = '''_'''.join(checkpoint_path.split('''/''' )[-1].split('''.''' )[0].split('''_''' )[:-1] )
__snake_case = config.depths[-1] - config.num_metaad_blocks + 1
__snake_case = convert_torch_checkpoint(_lowerCamelCase , _lowerCamelCase )
model.load_state_dict(_lowerCamelCase )
model.eval()
__snake_case = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
# prepare image
__snake_case = prepare_img()
__snake_case = 2_56
__snake_case = 2_24
__snake_case = EfficientFormerImageProcessor(
size={'''shortest_edge''': image_size} , crop_size={'''height''': crop_size, '''width''': crop_size} , resample=pillow_resamplings['''bicubic'''] , )
__snake_case = processor(images=_lowerCamelCase , return_tensors='''pt''' ).pixel_values
# original processing pipeline
__snake_case = Compose(
[
Resize(_lowerCamelCase , interpolation=pillow_resamplings['''bicubic'''] ),
CenterCrop(_lowerCamelCase ),
ToTensor(),
Normalize(_lowerCamelCase , _lowerCamelCase ),
] )
__snake_case = image_transforms(_lowerCamelCase ).unsqueeze(0 )
assert torch.allclose(_lowerCamelCase , _lowerCamelCase )
__snake_case = model(_lowerCamelCase )
__snake_case = outputs.logits
__snake_case = (1, 10_00)
if "l1" in model_name:
__snake_case = torch.Tensor(
[-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328] )
assert torch.allclose(logits[0, :10] , _lowerCamelCase , atol=1E-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
__snake_case = torch.Tensor(
[-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127] )
assert torch.allclose(logits[0, :10] , _lowerCamelCase , atol=1E-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
__snake_case = torch.Tensor(
[-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878] )
assert logits.shape == expected_shape
else:
raise ValueError(
f'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7''' )
# Save Checkpoints
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
model.save_pretrained(_lowerCamelCase )
print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
processor.save_pretrained(_lowerCamelCase )
print(f'''Processor successfuly saved at {pytorch_dump_path}''' )
if push_to_hub:
print('''Pushing model to the hub...''' )
model.push_to_hub(
repo_id=f'''Bearnardd/{pytorch_dump_path}''' , commit_message='''Add model''' , use_temp_dir=_lowerCamelCase , )
processor.push_to_hub(
repo_id=f'''Bearnardd/{pytorch_dump_path}''' , commit_message='''Add image processor''' , use_temp_dir=_lowerCamelCase , )
if __name__ == "__main__":
UpperCAmelCase_ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--pytorch_model_path''',
default=None,
type=str,
required=True,
help='''Path to EfficientFormer pytorch checkpoint.''',
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The json file for EfficientFormer model config.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
parser.add_argument(
'''--no-push_to_hub''',
dest='''push_to_hub''',
action='''store_false''',
help='''Do not push model and image processor to the hub''',
)
parser.set_defaults(push_to_hub=True)
UpperCAmelCase_ : Union[str, Any] = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 24 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
_snake_case = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def snake_case ( )-> int:
'''simple docstring'''
lowerCamelCase__ = Github(os.environ['GITHUB_TOKEN'] )
lowerCamelCase__ = g.get_repo('huggingface/diffusers' )
lowerCamelCase__ = repo.get_issues(state='open' )
for issue in open_issues:
lowerCamelCase__ = sorted(issue.get_comments() , key=lambda _a : i.created_at , reverse=_a )
lowerCamelCase__ = comments[0] if len(_a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state='closed' )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state='open' )
issue.remove_from_labels('stale' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
issue.add_to_labels('stale' )
if __name__ == "__main__":
main()
| 510 | 0 |
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 __snake_case ( lowerCamelCase_ ):
def __init__( self : Optional[int] , _lowercase : VQModel , _lowercase : UNetaDModel , _lowercase : DDIMScheduler ):
"""simple docstring"""
super().__init__()
self.register_modules(vqvae=_lowercase , unet=_lowercase , scheduler=_lowercase )
@torch.no_grad()
def __call__( self : List[Any] , _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 : Optional[Any] , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=_lowercase , )
SCREAMING_SNAKE_CASE__ = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
SCREAMING_SNAKE_CASE__ = 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
SCREAMING_SNAKE_CASE__ = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
SCREAMING_SNAKE_CASE__ = {}
if accepts_eta:
SCREAMING_SNAKE_CASE__ = eta
for t in self.progress_bar(self.scheduler.timesteps ):
SCREAMING_SNAKE_CASE__ = self.scheduler.scale_model_input(_lowercase , _lowercase )
# predict the noise residual
SCREAMING_SNAKE_CASE__ = self.unet(_lowercase , _lowercase ).sample
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE__ = self.scheduler.step(_lowercase , _lowercase , _lowercase , **_lowercase ).prev_sample
# decode the image latents with the VAE
SCREAMING_SNAKE_CASE__ = self.vqvae.decode(_lowercase ).sample
SCREAMING_SNAKE_CASE__ = (image / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE__ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE__ = self.numpy_to_pil(_lowercase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_lowercase )
| 721 | def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [1]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 0, 0, 0
SCREAMING_SNAKE_CASE__ = ugly_nums[ia] * 2
SCREAMING_SNAKE_CASE__ = ugly_nums[ia] * 3
SCREAMING_SNAKE_CASE__ = ugly_nums[ia] * 5
for _ in range(1 , __UpperCamelCase ):
SCREAMING_SNAKE_CASE__ = min(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
ugly_nums.append(__UpperCamelCase )
if next_num == next_a:
ia += 1
SCREAMING_SNAKE_CASE__ = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
SCREAMING_SNAKE_CASE__ = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
SCREAMING_SNAKE_CASE__ = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(F"""{ugly_numbers(200) = }""")
| 379 | 0 |
UpperCAmelCase_ = """Alexander Joslin"""
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> int:
_A = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
_A = Stack()
_A = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(_snake_case ) )
elif i in operators:
# RULE 2
operator_stack.push(_snake_case )
elif i == ")":
# RULE 4
_A = operator_stack.peek()
operator_stack.pop()
_A = operand_stack.peek()
operand_stack.pop()
_A = operand_stack.peek()
operand_stack.pop()
_A = operators[opr](_snake_case , _snake_case )
operand_stack.push(_snake_case )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
UpperCAmelCase_ = """(5 + ((4 * 2) * (2 + 3)))"""
# answer = 45
print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')
| 2 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 408 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import GLPNImageProcessor
class lowerCAmelCase ( unittest.TestCase ):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=7 , lowerCAmelCase__=3 , lowerCAmelCase__=18 , lowerCAmelCase__=30 , lowerCAmelCase__=400 , lowerCAmelCase__=True , lowerCAmelCase__=32 , lowerCAmelCase__=True , ):
_A= parent
_A= batch_size
_A= num_channels
_A= image_size
_A= min_resolution
_A= max_resolution
_A= do_resize
_A= size_divisor
_A= do_rescale
def a__ ( self ):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class lowerCAmelCase ( _a , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : int =GLPNImageProcessor if is_vision_available() else None
def a__ ( self ):
_A= GLPNImageProcessingTester(self )
@property
def a__ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def a__ ( self ):
_A= self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , 'do_resize' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'size_divisor' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'resample' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'do_rescale' ) )
def a__ ( self ):
pass
def a__ ( self ):
# 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=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
_A= image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def a__ ( self ):
# 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=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
_A= image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def a__ ( self ):
# 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=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input (GLPNImageProcessor doesn't support batching)
_A= image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 ) | 476 | import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase ( _a , _a , _a , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Optional[int] =AltDiffusionPipeline
_SCREAMING_SNAKE_CASE : int =TEXT_TO_IMAGE_PARAMS
_SCREAMING_SNAKE_CASE : List[Any] =TEXT_TO_IMAGE_BATCH_PARAMS
_SCREAMING_SNAKE_CASE : Dict =TEXT_TO_IMAGE_IMAGE_PARAMS
_SCREAMING_SNAKE_CASE : Any =TEXT_TO_IMAGE_IMAGE_PARAMS
def a__ ( self ):
torch.manual_seed(0 )
_A= UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
_A= DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=lowerCAmelCase__ , set_alpha_to_one=lowerCAmelCase__ , )
torch.manual_seed(0 )
_A= AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
# TODO: address the non-deterministic text encoder (fails for save-load tests)
# torch.manual_seed(0)
# text_encoder_config = RobertaSeriesConfig(
# hidden_size=32,
# project_dim=32,
# intermediate_size=37,
# layer_norm_eps=1e-05,
# num_attention_heads=4,
# num_hidden_layers=5,
# vocab_size=5002,
# )
# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
torch.manual_seed(0 )
_A= CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5002 , )
_A= CLIPTextModel(lowerCAmelCase__ )
_A= XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta' )
_A= 77
_A= {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def a__ ( self , lowerCAmelCase__ , lowerCAmelCase__=0 ):
if str(lowerCAmelCase__ ).startswith('mps' ):
_A= torch.manual_seed(lowerCAmelCase__ )
else:
_A= torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
_A= {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def a__ ( self ):
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def a__ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def a__ ( self ):
_A= 'cpu' # ensure determinism for the device-dependent torch.Generator
_A= self.get_dummy_components()
torch.manual_seed(0 )
_A= RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
# TODO: remove after fixing the non-deterministic text encoder
_A= RobertaSeriesModelWithTransformation(lowerCAmelCase__ )
_A= text_encoder
_A= AltDiffusionPipeline(**lowerCAmelCase__ )
_A= alt_pipe.to(lowerCAmelCase__ )
alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_A= self.get_dummy_inputs(lowerCAmelCase__ )
_A= 'A photo of an astronaut'
_A= alt_pipe(**lowerCAmelCase__ )
_A= output.images
_A= image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_A= np.array(
[0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def a__ ( self ):
_A= 'cpu' # ensure determinism for the device-dependent torch.Generator
_A= self.get_dummy_components()
_A= PNDMScheduler(skip_prk_steps=lowerCAmelCase__ )
torch.manual_seed(0 )
_A= RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
# TODO: remove after fixing the non-deterministic text encoder
_A= RobertaSeriesModelWithTransformation(lowerCAmelCase__ )
_A= text_encoder
_A= AltDiffusionPipeline(**lowerCAmelCase__ )
_A= alt_pipe.to(lowerCAmelCase__ )
alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_A= self.get_dummy_inputs(lowerCAmelCase__ )
_A= alt_pipe(**lowerCAmelCase__ )
_A= output.images
_A= image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_A= np.array(
[0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class lowerCAmelCase ( unittest.TestCase ):
def a__ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a__ ( self ):
# make sure here that pndm scheduler skips prk
_A= AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion' , safety_checker=lowerCAmelCase__ )
_A= alt_pipe.to(lowerCAmelCase__ )
alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_A= 'A painting of a squirrel eating a burger'
_A= torch.manual_seed(0 )
_A= alt_pipe([prompt] , generator=lowerCAmelCase__ , guidance_scale=6.0 , num_inference_steps=20 , output_type='np' )
_A= output.images
_A= image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_A= np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def a__ ( self ):
_A= DDIMScheduler.from_pretrained('BAAI/AltDiffusion' , subfolder='scheduler' )
_A= AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion' , scheduler=lowerCAmelCase__ , safety_checker=lowerCAmelCase__ )
_A= alt_pipe.to(lowerCAmelCase__ )
alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_A= 'A painting of a squirrel eating a burger'
_A= torch.manual_seed(0 )
_A= alt_pipe([prompt] , generator=lowerCAmelCase__ , num_inference_steps=2 , output_type='numpy' )
_A= output.images
_A= image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_A= np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 | 476 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
_a : str = logging.get_logger(__name__)
def _a (lowercase__ : Tuple ) -> Optional[int]:
"""simple docstring"""
__snake_case = torch.load(lowercase__ , map_location='cpu' )
if "model" in sd.keys():
__snake_case = torch.load(lowercase__ , map_location='cpu' )['model']
# pop unnecessary weights
__snake_case = [
'decoder.version',
'decoder.output_projection.weight',
]
for key in keys_to_delete:
if key in sd:
sd.pop(lowercase__ )
__snake_case = {
'decoder.project_in_dim.weight': 'decoder.project_in.weight',
'decoder.project_out_dim.weight': 'decoder.project_out.weight',
'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
__snake_case = sd.pop(lowercase__ )
__snake_case = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
__snake_case = sd[key]
# We split QKV in separate Q,K,V
__snake_case = key.replace('.qkv_proj.' , '.q_proj.' )
__snake_case = key.replace('.qkv_proj.' , '.k_proj.' )
__snake_case = key.replace('.qkv_proj.' , '.v_proj.' )
__snake_case = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
__snake_case , __snake_case , __snake_case = torch.split(lowercase__ , depth // 3 , dim=0 )
__snake_case = q
__snake_case = k
__snake_case = v
del sd[key]
return sd
@torch.no_grad()
def _a (lowercase__ : str , lowercase__ : Tuple , lowercase__ : List[Any]=None ) -> Optional[int]:
"""simple docstring"""
__snake_case = load_checkpoint(lowercase__ )
if config is not None:
__snake_case = OPTConfig.from_pretrained(lowercase__ )
else:
__snake_case = OPTConfig()
__snake_case = OPTModel(lowercase__ ).half().eval()
model.load_state_dict(lowercase__ )
# Check results
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
model.save_pretrained(lowercase__ )
if __name__ == "__main__":
_a : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fairseq_path",
type=str,
help=(
"path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
" https://huggingface.co/models?other=opt_metasq"
),
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
_a : List[Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 56 |
'''simple docstring'''
from __future__ import annotations
import math
def _a (lowercase__ : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowercase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
_a : Dict = [num for num in range(3, 100_001, 2) if not is_prime(num)]
def _a (lowercase__ : int ) -> list[int]:
"""simple docstring"""
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('n must be an integer' )
if n <= 0:
raise ValueError('n must be >= 0' )
__snake_case = []
for num in range(len(lowercase__ ) ):
__snake_case = 0
while 2 * i * i <= odd_composites[num]:
__snake_case = odd_composites[num] - 2 * i * i
if is_prime(lowercase__ ):
break
i += 1
else:
list_nums.append(odd_composites[num] )
if len(lowercase__ ) == n:
return list_nums
return []
def _a () -> int:
"""simple docstring"""
return compute_nums(1 )[0]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 56 | 1 |
'''simple docstring'''
import math
import qiskit
def lowerCAmelCase( a__ : int = 1 , a__ : int = 1 , a__ : int = 1 ):
'''simple docstring'''
if (
isinstance(a__ , a__ )
or isinstance(a__ , a__ )
or isinstance(a__ , a__ )
):
raise TypeError("inputs must be integers." )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError("inputs must be positive." )
if (
(math.floor(a__ ) != input_a)
or (math.floor(a__ ) != input_a)
or (math.floor(a__ ) != carry_in)
):
raise ValueError("inputs must be exact integers." )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError("inputs must be less or equal to 2." )
# build registers
lowerCamelCase__ = qiskit.QuantumRegister(4 , "qr" )
lowerCamelCase__ = qiskit.ClassicalRegister(2 , "cr" )
# list the entries
lowerCamelCase__ = [input_a, input_a, carry_in]
lowerCamelCase__ = qiskit.QuantumCircuit(a__ , a__ )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(a__ ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(a__ ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(a__ ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , a__ ) # measure the last two qbits
lowerCamelCase__ = qiskit.Aer.get_backend("aer_simulator" )
lowerCamelCase__ = qiskit.execute(a__ , a__ , shots=1000 )
return job.result().get_counts(a__ )
if __name__ == "__main__":
print(f'Total sum count for state is: {quantum_full_adder(1, 1, 1)}')
| 426 |
'''simple docstring'''
def lowerCAmelCase( a__ : str ):
'''simple docstring'''
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
lowerCamelCase__ = ""
while len(a__ ) % 3 != 0:
lowerCamelCase__ = "0" + bin_string
lowerCamelCase__ = [
bin_string[index : index + 3]
for index in range(len(a__ ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
lowerCamelCase__ = 0
for index, val in enumerate(a__ ):
oct_val += int(2 ** (2 - index) * int(a__ ) )
oct_string += str(a__ )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 426 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( a__) -> str:
"""simple docstring"""
if isinstance(a__ , a__):
raise TypeError('\'float\' object cannot be interpreted as an integer')
if isinstance(a__ , a__):
raise TypeError('\'str\' object cannot be interpreted as an integer')
if num == 0:
return "0b0"
_snake_case : Dict = False
if num < 0:
_snake_case : str = True
_snake_case : int = -num
_snake_case : list[int] = []
while num > 0:
binary.insert(0 , num % 2)
num >>= 1
if negative:
return "-0b" + "".join(str(a__) for e in binary)
return "0b" + "".join(str(a__) for e in binary)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 517 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( a__) -> float:
"""simple docstring"""
if not nums:
raise ValueError('List is empty')
return sum(a__) / len(a__)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 517 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
__UpperCAmelCase =logging.get_logger(__name__) # pylint: disable=invalid-name
__UpperCAmelCase ="""
Examples:
```py
>>> from PIL import Image
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif, load_image
>>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")
>>> repo = \"openai/shap-e-img2img\"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> guidance_scale = 3.0
>>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
>>> image = load_image(image_url).convert(\"RGB\")
>>> images = pipe(
... image,
... guidance_scale=guidance_scale,
... num_inference_steps=64,
... frame_size=256,
... ).images
>>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
```
"""
@dataclass
class lowerCAmelCase__ ( UpperCAmelCase_ ):
lowercase__ : Union[PIL.Image.Image, np.ndarray]
class lowerCAmelCase__ ( UpperCAmelCase_ ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ):
'''simple docstring'''
super().__init__()
self.register_modules(
prior=UpperCamelCase__ , image_encoder=UpperCamelCase__ , image_processor=UpperCamelCase__ , scheduler=UpperCamelCase__ , renderer=UpperCamelCase__ , )
def lowercase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if latents is None:
A__ = randn_tensor(UpperCamelCase__ , generator=UpperCamelCase__ , device=UpperCamelCase__ , dtype=UpperCamelCase__ )
else:
if latents.shape != shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
A__ = latents.to(UpperCamelCase__ )
A__ = latents * scheduler.init_noise_sigma
return latents
def lowercase_ ( self , UpperCamelCase__=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
A__ = torch.device(f"""cuda:{gpu_id}""" )
A__ = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(UpperCamelCase__ , UpperCamelCase__ )
@property
def lowercase_ ( self ):
'''simple docstring'''
if self.device != torch.device("meta" ) or not hasattr(self.image_encoder , "_hf_hook" ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(UpperCamelCase__ , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def lowercase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ):
'''simple docstring'''
if isinstance(UpperCamelCase__ , UpperCamelCase__ ) and isinstance(image[0] , torch.Tensor ):
A__ = torch.cat(UpperCamelCase__ , axis=0 ) if image[0].ndim == 4 else torch.stack(UpperCamelCase__ , axis=0 )
if not isinstance(UpperCamelCase__ , torch.Tensor ):
A__ = self.image_processor(UpperCamelCase__ , return_tensors="pt" ).pixel_values[0].unsqueeze(0 )
A__ = image.to(dtype=self.image_encoder.dtype , device=UpperCamelCase__ )
A__ = self.image_encoder(UpperCamelCase__ )["last_hidden_state"]
A__ = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
A__ = image_embeds.repeat_interleave(UpperCamelCase__ , dim=0 )
if do_classifier_free_guidance:
A__ = torch.zeros_like(UpperCamelCase__ )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
A__ = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(UpperCamelCase__ )
def __call__( self , UpperCamelCase__ , UpperCamelCase__ = 1 , UpperCamelCase__ = 25 , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = 4.0 , UpperCamelCase__ = 64 , UpperCamelCase__ = "pil" , UpperCamelCase__ = True , ):
'''simple docstring'''
if isinstance(UpperCamelCase__ , PIL.Image.Image ):
A__ = 1
elif isinstance(UpperCamelCase__ , torch.Tensor ):
A__ = image.shape[0]
elif isinstance(UpperCamelCase__ , UpperCamelCase__ ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
A__ = len(UpperCamelCase__ )
else:
raise ValueError(
f"""`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(UpperCamelCase__ )}""" )
A__ = self._execution_device
A__ = batch_size * num_images_per_prompt
A__ = guidance_scale > 1.0
A__ = self._encode_image(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# prior
self.scheduler.set_timesteps(UpperCamelCase__ , device=UpperCamelCase__ )
A__ = self.scheduler.timesteps
A__ = self.prior.config.num_embeddings
A__ = self.prior.config.embedding_dim
A__ = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
A__ = latents.reshape(latents.shape[0] , UpperCamelCase__ , UpperCamelCase__ )
for i, t in enumerate(self.progress_bar(UpperCamelCase__ ) ):
# expand the latents if we are doing classifier free guidance
A__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
A__ = self.scheduler.scale_model_input(UpperCamelCase__ , UpperCamelCase__ )
A__ = self.prior(
UpperCamelCase__ , timestep=UpperCamelCase__ , proj_embedding=UpperCamelCase__ , ).predicted_image_embedding
# remove the variance
A__ , A__ = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
A__ , A__ = noise_pred.chunk(2 )
A__ = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
A__ = self.scheduler.step(
UpperCamelCase__ , timestep=UpperCamelCase__ , sample=UpperCamelCase__ , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=UpperCamelCase__ )
A__ = []
for i, latent in enumerate(UpperCamelCase__ ):
print()
A__ = self.renderer.decode(
latent[None, :] , UpperCamelCase__ , size=UpperCamelCase__ , ray_batch_size=40_96 , n_coarse_samples=64 , n_fine_samples=1_28 , )
images.append(UpperCamelCase__ )
A__ = torch.stack(UpperCamelCase__ )
if output_type not in ["np", "pil"]:
raise ValueError(f"""Only the output types `pil` and `np` are supported not output_type={output_type}""" )
A__ = images.cpu().numpy()
if output_type == "pil":
A__ = [self.numpy_to_pil(UpperCamelCase__ ) for image in images]
# Offload last model to CPU
if hasattr(self , "final_offload_hook" ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=UpperCamelCase__ ) | 261 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
__UpperCAmelCase ="""https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
__UpperCAmelCase =requests.get(url, headers={"""UserAgent""": UserAgent().random})
# res.raise_for_status()
with open("""project1a.html""", """wb""") as out_file: # only for knowing the class
for data in res.iter_content(1_0000):
out_file.write(data)
__UpperCAmelCase =BeautifulSoup(res.text, """html.parser""")
__UpperCAmelCase =list(soup.select(""".eZt8xd"""))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("""href"""))
else:
webbrowser.open(F'''https://google.com{link.get("href")}''') | 261 | 1 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowercase_ = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def lowercase ( lowerCAmelCase__ : Optional[int] ) -> List[str]:
__a = test_results.split(''' ''' )
__a = 0
__a = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
__a = expressions[-2] if '''=''' in expressions[-1] else expressions[-1]
for i, expression in enumerate(lowerCAmelCase__ ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def lowercase ( lowerCAmelCase__ : int ) -> str:
__a = {}
__a = None
__a = False
for line in failures_short_lines.split('''\n''' ):
if re.search(r'''_ \[doctest\]''' , lowerCAmelCase__ ):
__a = True
__a = line.split(''' ''' )[2]
elif in_error and not line.split(''' ''' )[0].isdigit():
__a = line
__a = False
return failures
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self , _a , _a ):
__a = title
__a = doc_test_results['''time_spent'''].split(''',''' )[0]
__a = doc_test_results['''success''']
__a = doc_test_results['''failures''']
__a = self.n_success + self.n_failures
# Failures and success of the modeling tests
__a = doc_test_results
@property
def __UpperCAmelCase ( self ):
__a = [self._time_spent]
__a = 0
for time in time_spent:
__a = time.split(''':''' )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(_a ) == 1:
__a = [0, 0, time_parts[0]]
__a , __a , __a = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 3_600 + minutes * 60 + seconds
__a , __a , __a = total_secs // 3_600, (total_secs % 3_600) // 60, total_secs % 60
return f'''{int(_a )}h{int(_a )}m{int(_a )}s'''
@property
def __UpperCAmelCase ( self ):
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def __UpperCAmelCase ( self ):
return {
"type": "section",
"text": {
"type": "plain_text",
"text": f'''🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.''',
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
@property
def __UpperCAmelCase ( self ):
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
f'''There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'''
f''' {self.time}.'''
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
@property
def __UpperCAmelCase ( self ):
__a = 40
__a = {k: v['''failed'''] for k, v in doc_test_results.items() if isinstance(_a , _a )}
__a = ''''''
for category, failures in category_failures.items():
if len(_a ) == 0:
continue
if report != "":
report += "\n\n"
report += f'''*{category} failures*:'''.ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(_a )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": f'''The following examples had failures:\n\n\n{report}\n''',
},
}
@property
def __UpperCAmelCase ( self ):
__a = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(_a )
@staticmethod
def __UpperCAmelCase ( ):
__a = [
{
'''type''': '''section''',
'''text''': {
'''type''': '''plain_text''',
'''text''': '''There was an issue running the tests.''',
},
'''accessory''': {
'''type''': '''button''',
'''text''': {'''type''': '''plain_text''', '''text''': '''Check Action results''', '''emoji''': True},
'''url''': f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
]
print('''Sending the following payload''' )
print(json.dumps({'''blocks''': json.loads(_a )} ) )
client.chat_postMessage(
channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY'''] , text='''There was an issue running the tests.''' , blocks=_a , )
def __UpperCAmelCase ( self ):
print('''Sending the following payload''' )
print(json.dumps({'''blocks''': json.loads(self.payload )} ) )
__a = f'''{self.n_failures} failures out of {self.n_tests} tests,''' if self.n_failures else '''All tests passed.'''
__a = client.chat_postMessage(
channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY'''] , blocks=self.payload , text=_a , )
def __UpperCAmelCase ( self , _a , _a , _a , _a ):
__a = ''''''
for key, value in failures.items():
__a = value[:200] + ''' [Truncated]''' if len(_a ) > 250 else value
failures_text += f'''*{key}*\n_{value}_\n\n'''
__a = job_name
__a = {'''type''': '''section''', '''text''': {'''type''': '''mrkdwn''', '''text''': text}}
if job_link is not None:
__a = {
'''type''': '''button''',
'''text''': {'''type''': '''plain_text''', '''text''': '''GitHub Action job''', '''emoji''': True},
'''url''': job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def __UpperCAmelCase ( self ):
if self.thread_ts is None:
raise ValueError('''Can only post reply if a post has been made.''' )
__a = self.doc_test_results.pop('''job_link''' )
self.doc_test_results.pop('''failures''' )
self.doc_test_results.pop('''success''' )
self.doc_test_results.pop('''time_spent''' )
__a = sorted(self.doc_test_results.items() , key=lambda _a : t[0] )
for job, job_result in sorted_dict:
if len(job_result['''failures'''] ):
__a = f'''*Num failures* :{len(job_result['failed'] )} \n'''
__a = job_result['''failures''']
__a = self.get_reply_blocks(_a , _a , _a , text=_a )
print('''Sending the following reply''' )
print(json.dumps({'''blocks''': blocks} ) )
client.chat_postMessage(
channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY'''] , text=f'''Results for {job}''' , blocks=_a , thread_ts=self.thread_ts['''ts'''] , )
time.sleep(1 )
def lowercase ( ) -> Any:
__a = os.environ['''GITHUB_RUN_ID''']
__a = f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''
__a = requests.get(lowerCAmelCase__ ).json()
__a = {}
try:
jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
__a = math.ceil((result['''total_count'''] - 100) / 100 )
for i in range(lowerCAmelCase__ ):
__a = requests.get(url + f'''&page={i + 2}''' ).json()
jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
return jobs
except Exception as e:
print('''Unknown error, could not fetch links.''' , lowerCAmelCase__ )
return {}
def lowercase ( lowerCAmelCase__ : str ) -> Dict:
__a = {}
if os.path.exists(lowerCAmelCase__ ):
__a = os.listdir(lowerCAmelCase__ )
for file in files:
try:
with open(os.path.join(lowerCAmelCase__ , lowerCAmelCase__ ) , encoding='''utf-8''' ) as f:
__a = f.read()
except UnicodeDecodeError as e:
raise ValueError(f'''Could not open {os.path.join(lowerCAmelCase__ , lowerCAmelCase__ )}.''' ) from e
return _artifact
def lowercase ( ) -> Dict:
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self , _a ):
__a = name
__a = []
def __str__( self ):
return self.name
def __UpperCAmelCase ( self , _a ):
self.paths.append({'''name''': self.name, '''path''': path} )
__a = {}
__a = filter(os.path.isdir , os.listdir() )
for directory in directories:
__a = directory
if artifact_name not in _available_artifacts:
__a = Artifact(lowerCAmelCase__ )
_available_artifacts[artifact_name].add_path(lowerCAmelCase__ )
return _available_artifacts
if __name__ == "__main__":
lowercase_ = get_job_links()
lowercase_ = retrieve_available_artifacts()
lowercase_ = collections.OrderedDict(
[
("*.py", "API Examples"),
("*.md", "MD Examples"),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
lowercase_ = {
v: {
"failed": [],
"failures": {},
}
for v in docs.values()
}
# Link to the GitHub Action job
lowercase_ = github_actions_job_links.get("run_doctests")
lowercase_ = available_artifacts["doc_tests_gpu_test_reports"].paths[0]
lowercase_ = retrieve_artifact(artifact_path["name"])
if "stats" in artifact:
lowercase_ , lowercase_ , lowercase_ = handle_test_results(artifact["stats"])
lowercase_ = failed
lowercase_ = success
lowercase_ = time_spent[1:-1] + ", "
lowercase_ = extract_first_line_failure(artifact["failures_short"])
for line in artifact["summary_short"].split("\n"):
if re.search("FAILED", line):
lowercase_ = line.replace("FAILED ", "")
lowercase_ = line.split()[0].replace("\n", "")
if "::" in line:
lowercase_ , lowercase_ = line.split("::")
else:
lowercase_ , lowercase_ = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
lowercase_ = docs[file_regex]
doc_test_results[category]["failed"].append(test)
lowercase_ = all_failures[test] if test in all_failures else "N/A"
lowercase_ = failure
break
lowercase_ = Message("🤗 Results of the doc tests.", doc_test_results)
message.post()
message.post_reply()
| 695 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float ) -> float:
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('''daily_interest_rate must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return principal * daily_interest_rate * days_between_payments
def lowercase ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , ) -> float:
if number_of_compounding_periods <= 0:
raise ValueError('''number_of_compounding_periods must be > 0''' )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError('''nominal_annual_interest_rate_percentage must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def lowercase ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , ) -> float:
if number_of_years <= 0:
raise ValueError('''number_of_years must be > 0''' )
if nominal_annual_percentage_rate < 0:
raise ValueError('''nominal_annual_percentage_rate must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return compound_interest(
lowerCAmelCase__ , nominal_annual_percentage_rate / 365 , number_of_years * 365 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 695 | 1 |
import math
from numpy import inf
from scipy.integrate import quad
def _UpperCamelCase ( lowerCAmelCase_ ) ->float:
if num <= 0:
raise ValueError("""math domain error""" )
return quad(lowerCAmelCase_ , 0 , lowerCAmelCase_ , args=(lowerCAmelCase_) )[0]
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) ->float:
return math.pow(lowerCAmelCase_ , z - 1 ) * math.exp(-x )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 627 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _UpperCamelCase ( lowerCAmelCase_ ) ->int:
UpperCAmelCase = {}
UpperCAmelCase = tokenizer(example["""content"""] , truncation=lowerCAmelCase_ )["""input_ids"""]
UpperCAmelCase = len(example["""content"""] ) / len(output["""input_ids"""] )
return output
__a = HfArgumentParser(PretokenizationArguments)
__a = parser.parse_args()
if args.num_workers is None:
__a = multiprocessing.cpu_count()
__a = AutoTokenizer.from_pretrained(args.tokenizer_dir)
__a = time.time()
__a = load_dataset(args.dataset_name, split="""train""")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")
__a = time.time()
__a = ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
"""repo_name""",
"""path""",
"""copies""",
"""size""",
"""content""",
"""license""",
"""hash""",
"""line_mean""",
"""line_max""",
"""alpha_frac""",
"""autogenerated""",
],
)
print(F"""Dataset tokenized in {time.time()-t_start:.2f}s""")
__a = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")
| 627 | 1 |
from __future__ import annotations
__lowerCAmelCase =[]
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
for i in range(len(_lowerCAmelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(_lowerCAmelCase ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(_lowerCAmelCase , -1 , -1 ) , range(_lowerCAmelCase , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(_lowerCAmelCase , -1 , -1 ) , range(_lowerCAmelCase , len(_lowerCAmelCase ) ) ):
if board[i][j] == 1:
return False
return True
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
if row >= len(_lowerCAmelCase ):
solution.append(_lowerCAmelCase )
printboard(_lowerCAmelCase )
print()
return True
for i in range(len(_lowerCAmelCase ) ):
if is_safe(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase = 1
solve(_lowerCAmelCase , row + 1 )
UpperCAmelCase = 0
return False
def __UpperCamelCase ( _lowerCAmelCase ):
"""simple docstring"""
for i in range(len(_lowerCAmelCase ) ):
for j in range(len(_lowerCAmelCase ) ):
if board[i][j] == 1:
print("Q" , end=" " )
else:
print("." , end=" " )
print()
# n=int(input("The no. of queens"))
__lowerCAmelCase =8
__lowerCAmelCase =[[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))
| 333 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class __magic_name__ :
def __init__( self : str ,__SCREAMING_SNAKE_CASE : Union[str, Any] ,__SCREAMING_SNAKE_CASE : str=1_3 ,__SCREAMING_SNAKE_CASE : Optional[Any]=7 ,__SCREAMING_SNAKE_CASE : Optional[Any]=True ,__SCREAMING_SNAKE_CASE : List[str]=True ,__SCREAMING_SNAKE_CASE : int=True ,__SCREAMING_SNAKE_CASE : int=True ,__SCREAMING_SNAKE_CASE : Tuple=9_9 ,__SCREAMING_SNAKE_CASE : str=3_2 ,__SCREAMING_SNAKE_CASE : Any=2 ,__SCREAMING_SNAKE_CASE : Union[str, Any]=4 ,__SCREAMING_SNAKE_CASE : Tuple=3_7 ,__SCREAMING_SNAKE_CASE : List[str]="gelu" ,__SCREAMING_SNAKE_CASE : List[Any]=0.1 ,__SCREAMING_SNAKE_CASE : Optional[int]=0.1 ,__SCREAMING_SNAKE_CASE : Tuple=5_1_2 ,__SCREAMING_SNAKE_CASE : Dict=1_6 ,__SCREAMING_SNAKE_CASE : Tuple=2 ,__SCREAMING_SNAKE_CASE : List[str]=0.02 ,__SCREAMING_SNAKE_CASE : Optional[Any]=3 ,__SCREAMING_SNAKE_CASE : Dict=4 ,__SCREAMING_SNAKE_CASE : Union[str, Any]=None ,__SCREAMING_SNAKE_CASE : Dict=1_0_0_0 ,):
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 = 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 = type_vocab_size
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = num_labels
UpperCAmelCase = num_choices
UpperCAmelCase = scope
UpperCAmelCase = range_bbox
def _UpperCAmelCase ( self : Dict ):
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] ,self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
UpperCAmelCase = bbox[i, j, 3]
UpperCAmelCase = bbox[i, j, 1]
UpperCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
UpperCAmelCase = bbox[i, j, 2]
UpperCAmelCase = bbox[i, j, 0]
UpperCAmelCase = t
UpperCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
UpperCAmelCase = None
if self.use_input_mask:
UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase = None
if self.use_token_type_ids:
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
UpperCAmelCase = ids_tensor([self.batch_size] ,self.num_choices )
UpperCAmelCase = LayoutLMConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,initializer_range=self.initializer_range ,)
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self : Dict ,__SCREAMING_SNAKE_CASE : List[str] ,__SCREAMING_SNAKE_CASE : Optional[Any] ,__SCREAMING_SNAKE_CASE : Dict ,__SCREAMING_SNAKE_CASE : Union[str, Any] ,__SCREAMING_SNAKE_CASE : int ,__SCREAMING_SNAKE_CASE : int ,__SCREAMING_SNAKE_CASE : Optional[Any] ,__SCREAMING_SNAKE_CASE : str ):
UpperCAmelCase = TFLayoutLMModel(config=__SCREAMING_SNAKE_CASE )
UpperCAmelCase = model(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,attention_mask=__SCREAMING_SNAKE_CASE ,token_type_ids=__SCREAMING_SNAKE_CASE )
UpperCAmelCase = model(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,token_type_ids=__SCREAMING_SNAKE_CASE )
UpperCAmelCase = model(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
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 _UpperCAmelCase ( self : Dict ,__SCREAMING_SNAKE_CASE : Tuple ,__SCREAMING_SNAKE_CASE : Tuple ,__SCREAMING_SNAKE_CASE : List[str] ,__SCREAMING_SNAKE_CASE : str ,__SCREAMING_SNAKE_CASE : Union[str, Any] ,__SCREAMING_SNAKE_CASE : Any ,__SCREAMING_SNAKE_CASE : List[Any] ,__SCREAMING_SNAKE_CASE : List[Any] ):
UpperCAmelCase = TFLayoutLMForMaskedLM(config=__SCREAMING_SNAKE_CASE )
UpperCAmelCase = model(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,attention_mask=__SCREAMING_SNAKE_CASE ,token_type_ids=__SCREAMING_SNAKE_CASE ,labels=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _UpperCAmelCase ( self : Union[str, Any] ,__SCREAMING_SNAKE_CASE : str ,__SCREAMING_SNAKE_CASE : List[Any] ,__SCREAMING_SNAKE_CASE : int ,__SCREAMING_SNAKE_CASE : Tuple ,__SCREAMING_SNAKE_CASE : List[str] ,__SCREAMING_SNAKE_CASE : Optional[int] ,__SCREAMING_SNAKE_CASE : Union[str, Any] ,__SCREAMING_SNAKE_CASE : Union[str, Any] ):
UpperCAmelCase = self.num_labels
UpperCAmelCase = TFLayoutLMForSequenceClassification(config=__SCREAMING_SNAKE_CASE )
UpperCAmelCase = model(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,attention_mask=__SCREAMING_SNAKE_CASE ,token_type_ids=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _UpperCAmelCase ( self : List[str] ,__SCREAMING_SNAKE_CASE : List[str] ,__SCREAMING_SNAKE_CASE : int ,__SCREAMING_SNAKE_CASE : List[str] ,__SCREAMING_SNAKE_CASE : int ,__SCREAMING_SNAKE_CASE : Dict ,__SCREAMING_SNAKE_CASE : Dict ,__SCREAMING_SNAKE_CASE : str ,__SCREAMING_SNAKE_CASE : Union[str, Any] ):
UpperCAmelCase = self.num_labels
UpperCAmelCase = TFLayoutLMForTokenClassification(config=__SCREAMING_SNAKE_CASE )
UpperCAmelCase = model(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,attention_mask=__SCREAMING_SNAKE_CASE ,token_type_ids=__SCREAMING_SNAKE_CASE ,labels=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _UpperCAmelCase ( self : Optional[int] ,__SCREAMING_SNAKE_CASE : Union[str, Any] ,__SCREAMING_SNAKE_CASE : Optional[int] ,__SCREAMING_SNAKE_CASE : Optional[Any] ,__SCREAMING_SNAKE_CASE : Dict ,__SCREAMING_SNAKE_CASE : int ,__SCREAMING_SNAKE_CASE : Optional[Any] ,__SCREAMING_SNAKE_CASE : List[str] ,__SCREAMING_SNAKE_CASE : str ):
UpperCAmelCase = TFLayoutLMForQuestionAnswering(config=__SCREAMING_SNAKE_CASE )
UpperCAmelCase = model(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,attention_mask=__SCREAMING_SNAKE_CASE ,token_type_ids=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def _UpperCAmelCase ( self : List[Any] ):
UpperCAmelCase = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) = config_and_inputs
UpperCAmelCase = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_tf
class __magic_name__ ( _a , _a , unittest.TestCase):
_UpperCAmelCase : Optional[int] = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
_UpperCAmelCase : str = (
{
'feature-extraction': TFLayoutLMModel,
'fill-mask': TFLayoutLMForMaskedLM,
'text-classification': TFLayoutLMForSequenceClassification,
'token-classification': TFLayoutLMForTokenClassification,
'zero-shot': TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
_UpperCAmelCase : Tuple = False
_UpperCAmelCase : int = True
_UpperCAmelCase : Union[str, Any] = 10
def _UpperCAmelCase ( self : Tuple ):
UpperCAmelCase = TFLayoutLMModelTester(self )
UpperCAmelCase = ConfigTester(self ,config_class=__SCREAMING_SNAKE_CASE ,hidden_size=3_7 )
def _UpperCAmelCase ( self : List[str] ):
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self : List[str] ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self : Dict ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self : Dict ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self : Any ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self : List[str] ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE )
@slow
def _UpperCAmelCase ( self : List[str] ):
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = TFLayoutLMModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
@unittest.skip("Onnx compliancy broke with TF 2.10" )
def _UpperCAmelCase ( self : List[str] ):
pass
def __UpperCamelCase ( ):
"""simple docstring"""
UpperCAmelCase = tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]] ) # noqa: E231
UpperCAmelCase = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ) # noqa: E231
UpperCAmelCase = tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]] ) # noqa: E231
UpperCAmelCase = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ) # noqa: E231
# these are sequence labels (i.e. at the token level)
UpperCAmelCase = tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class __magic_name__ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self : Any ):
UpperCAmelCase = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased" )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
UpperCAmelCase = model(input_ids=__SCREAMING_SNAKE_CASE ,bbox=__SCREAMING_SNAKE_CASE ,attention_mask=__SCREAMING_SNAKE_CASE ,token_type_ids=__SCREAMING_SNAKE_CASE )
# test the sequence output on [0, :3, :3]
UpperCAmelCase = tf.convert_to_tensor(
[[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]] ,)
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] ,__SCREAMING_SNAKE_CASE ,atol=1e-3 ) )
# test the pooled output on [1, :3]
UpperCAmelCase = tf.convert_to_tensor([-0.6580, -0.0214, 0.8552] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3] ,__SCREAMING_SNAKE_CASE ,atol=1e-3 ) )
@slow
def _UpperCAmelCase ( self : Union[str, Any] ):
# initialize model with randomly initialized sequence classification head
UpperCAmelCase = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased" ,num_labels=2 )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
UpperCAmelCase = model(
input_ids=__SCREAMING_SNAKE_CASE ,bbox=__SCREAMING_SNAKE_CASE ,attention_mask=__SCREAMING_SNAKE_CASE ,token_type_ids=__SCREAMING_SNAKE_CASE ,labels=tf.convert_to_tensor([1, 1] ) ,)
# test whether we get a loss as a scalar
UpperCAmelCase = outputs.loss
UpperCAmelCase = (2,)
self.assertEqual(loss.shape ,__SCREAMING_SNAKE_CASE )
# test the shape of the logits
UpperCAmelCase = outputs.logits
UpperCAmelCase = (2, 2)
self.assertEqual(logits.shape ,__SCREAMING_SNAKE_CASE )
@slow
def _UpperCAmelCase ( self : Tuple ):
# initialize model with randomly initialized token classification head
UpperCAmelCase = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased" ,num_labels=1_3 )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
UpperCAmelCase = model(
input_ids=__SCREAMING_SNAKE_CASE ,bbox=__SCREAMING_SNAKE_CASE ,attention_mask=__SCREAMING_SNAKE_CASE ,token_type_ids=__SCREAMING_SNAKE_CASE ,labels=__SCREAMING_SNAKE_CASE )
# test the shape of the logits
UpperCAmelCase = outputs.logits
UpperCAmelCase = tf.convert_to_tensor((2, 2_5, 1_3) )
self.assertEqual(logits.shape ,__SCREAMING_SNAKE_CASE )
@slow
def _UpperCAmelCase ( self : List[Any] ):
# initialize model with randomly initialized token classification head
UpperCAmelCase = TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased" )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
UpperCAmelCase = model(input_ids=__SCREAMING_SNAKE_CASE ,bbox=__SCREAMING_SNAKE_CASE ,attention_mask=__SCREAMING_SNAKE_CASE ,token_type_ids=__SCREAMING_SNAKE_CASE )
# test the shape of the logits
UpperCAmelCase = tf.convert_to_tensor((2, 2_5) )
self.assertEqual(outputs.start_logits.shape ,__SCREAMING_SNAKE_CASE )
self.assertEqual(outputs.end_logits.shape ,__SCREAMING_SNAKE_CASE )
| 333 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class A ( _A ):
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=13 , SCREAMING_SNAKE_CASE=7 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=99 , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=5 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=37 , SCREAMING_SNAKE_CASE="gelu" , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=512 , SCREAMING_SNAKE_CASE=16 , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=None , ) -> Tuple:
"""simple docstring"""
A : Optional[Any] = parent
A : int = batch_size
A : Dict = seq_length
A : Any = is_training
A : Tuple = use_input_mask
A : Optional[int] = use_token_type_ids
A : Union[str, Any] = use_labels
A : Dict = vocab_size
A : str = hidden_size
A : int = num_hidden_layers
A : Optional[Any] = num_attention_heads
A : Any = intermediate_size
A : Optional[int] = hidden_act
A : Optional[Any] = hidden_dropout_prob
A : str = attention_probs_dropout_prob
A : Dict = max_position_embeddings
A : Union[str, Any] = type_vocab_size
A : List[str] = type_sequence_label_size
A : List[Any] = initializer_range
A : Tuple = num_labels
A : Union[str, Any] = num_choices
A : Tuple = scope
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
A : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A : Tuple = None
if self.use_input_mask:
A : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
A : Optional[int] = None
A : Dict = None
A : List[Any] = None
if self.use_labels:
A : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A : Union[str, Any] = ids_tensor([self.batch_size] , self.num_choices )
A : Optional[Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
A : Optional[Any] = DistilBertModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
A : Dict = model(__lowerCamelCase , __lowerCamelCase )
A : Dict = model(__lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
A : Dict = DistilBertForMaskedLM(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
A : int = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
A : Dict = DistilBertForQuestionAnswering(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
A : Optional[int] = model(
__lowerCamelCase , attention_mask=__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 __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
A : Tuple = self.num_labels
A : int = DistilBertForSequenceClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
A : Tuple = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
A : Optional[int] = self.num_labels
A : List[str] = DistilBertForTokenClassification(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
A : List[str] = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
A : Union[str, Any] = self.num_choices
A : Optional[Any] = DistilBertForMultipleChoice(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
A : int = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A : Union[str, Any] = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : Optional[Any] = self.prepare_config_and_inputs()
(A) : int = config_and_inputs
A : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class A ( _A , _A , unittest.TestCase ):
__magic_name__ = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
__magic_name__ = (
{
'''feature-extraction''': DistilBertModel,
'''fill-mask''': DistilBertForMaskedLM,
'''question-answering''': DistilBertForQuestionAnswering,
'''text-classification''': DistilBertForSequenceClassification,
'''token-classification''': DistilBertForTokenClassification,
'''zero-shot''': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__magic_name__ = True
__magic_name__ = True
__magic_name__ = True
__magic_name__ = True
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : Optional[int] = DistilBertModelTester(self )
A : List[str] = ConfigTester(self , config_class=__lowerCamelCase , dim=37 )
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
A : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*__lowerCamelCase )
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
A : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*__lowerCamelCase )
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
A : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*__lowerCamelCase )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*__lowerCamelCase )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*__lowerCamelCase )
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*__lowerCamelCase )
@slow
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A : Optional[Any] = DistilBertModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
@slow
@require_torch_gpu
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
A : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
A : List[str] = True
A : int = model_class(config=__lowerCamelCase )
A : Dict = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase )
A : str = torch.jit.trace(
__lowerCamelCase , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(__lowerCamelCase , os.path.join(__lowerCamelCase , '''traced_model.pt''' ) )
A : List[str] = torch.jit.load(os.path.join(__lowerCamelCase , '''traced_model.pt''' ) , map_location=__lowerCamelCase )
loaded(inputs_dict['''input_ids'''].to(__lowerCamelCase ) , inputs_dict['''attention_mask'''].to(__lowerCamelCase ) )
@require_torch
class A ( unittest.TestCase ):
@slow
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : List[Any] = DistilBertModel.from_pretrained('''distilbert-base-uncased''' )
A : Union[str, Any] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
A : List[str] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
A : Union[str, Any] = model(__lowerCamelCase , attention_mask=__lowerCamelCase )[0]
A : Optional[Any] = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , __lowerCamelCase )
A : List[str] = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __lowerCamelCase , atol=1e-4 ) )
| 717 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[int] = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : str = [
'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TrajectoryTransformerModel',
'TrajectoryTransformerPreTrainedModel',
'load_tf_weights_in_trajectory_transformer',
]
if TYPE_CHECKING:
from .configuration_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TrajectoryTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
load_tf_weights_in_trajectory_transformer,
)
else:
import sys
lowercase : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 343 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ , unittest.TestCase ):
__lowerCAmelCase : Any = ShapEImgaImgPipeline
__lowerCAmelCase : Any = ['image']
__lowerCAmelCase : List[Any] = ['image']
__lowerCAmelCase : List[str] = [
'num_images_per_prompt',
'num_inference_steps',
'generator',
'latents',
'guidance_scale',
'frame_size',
'output_type',
'return_dict',
]
__lowerCAmelCase : int = False
@property
def SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
return 32
@property
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
return 32
@property
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
return self.time_input_dim * 4
@property
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
return 8
@property
def SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Optional[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
UpperCAmelCase : int = CLIPVisionModel(_SCREAMING_SNAKE_CASE )
return model
@property
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = CLIPImageProcessor(
crop_size=224 , do_center_crop=_SCREAMING_SNAKE_CASE , do_normalize=_SCREAMING_SNAKE_CASE , do_resize=_SCREAMING_SNAKE_CASE , image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073] , image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711] , resample=3 , size=224 , )
return image_processor
@property
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Any = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 16,
"""embedding_dim""": self.time_input_dim,
"""num_embeddings""": 32,
"""embedding_proj_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""num_layers""": 1,
"""clip_embed_dim""": self.time_input_dim * 2,
"""additional_embeddings""": 0,
"""time_embed_act_fn""": """gelu""",
"""norm_in_type""": """layer""",
"""embedding_proj_norm_type""": """layer""",
"""encoder_hid_proj_type""": None,
"""added_emb_type""": None,
}
UpperCAmelCase : int = PriorTransformer(**_SCREAMING_SNAKE_CASE )
return model
@property
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Any = {
"""param_shapes""": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"""d_latent""": self.time_input_dim,
"""d_hidden""": self.renderer_dim,
"""n_output""": 12,
"""background""": (
0.1,
0.1,
0.1,
),
}
UpperCAmelCase : Optional[int] = ShapERenderer(**_SCREAMING_SNAKE_CASE )
return model
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : int = self.dummy_prior
UpperCAmelCase : Union[str, Any] = self.dummy_image_encoder
UpperCAmelCase : Optional[Any] = self.dummy_image_processor
UpperCAmelCase : Union[str, Any] = self.dummy_renderer
UpperCAmelCase : str = HeunDiscreteScheduler(
beta_schedule="""exp""" , num_train_timesteps=1024 , prediction_type="""sample""" , use_karras_sigmas=_SCREAMING_SNAKE_CASE , clip_sample=_SCREAMING_SNAKE_CASE , clip_sample_range=1.0 , )
UpperCAmelCase : Union[str, Any] = {
"""prior""": prior,
"""image_encoder""": image_encoder,
"""image_processor""": image_processor,
"""renderer""": renderer,
"""scheduler""": scheduler,
}
return components
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0 ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any = floats_tensor((1, 3, 64, 64) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
if str(_SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
UpperCAmelCase : Optional[int] = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase : int = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Union[str, Any] = {
"""image""": input_image,
"""generator""": generator,
"""num_inference_steps""": 1,
"""frame_size""": 32,
"""output_type""": """np""",
}
return inputs
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] = """cpu"""
UpperCAmelCase : List[str] = self.get_dummy_components()
UpperCAmelCase : str = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Dict = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Dict = pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE ) )
UpperCAmelCase : Union[str, Any] = output.images[0]
UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
UpperCAmelCase : Any = np.array(
[
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Optional[Any] = torch_device == """cpu"""
UpperCAmelCase : int = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_SCREAMING_SNAKE_CASE , relax_max_difference=_SCREAMING_SNAKE_CASE , )
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = self.get_dummy_components()
UpperCAmelCase : Optional[int] = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
UpperCAmelCase : int = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : int = 1
UpperCAmelCase : Optional[int] = 2
UpperCAmelCase : Optional[int] = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
for key in inputs.keys():
if key in self.batch_params:
UpperCAmelCase : Tuple = batch_size * [inputs[key]]
UpperCAmelCase : Any = pipe(**_SCREAMING_SNAKE_CASE , num_images_per_prompt=_SCREAMING_SNAKE_CASE )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png""" )
UpperCAmelCase : List[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/shap_e/test_shap_e_img2img_out.npy""" )
UpperCAmelCase : List[Any] = ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img""" )
UpperCAmelCase : Dict = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : List[Any] = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
UpperCAmelCase : Dict = pipe(
_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
| 160 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( UpperCamelCase : list[int] , UpperCamelCase : int ):
if len(UpperCamelCase ) < k or k < 0:
raise ValueError("""Invalid Input""" )
UpperCAmelCase : Optional[Any] = sum(array[:k] )
for i in range(len(UpperCamelCase ) - k ):
UpperCAmelCase : str = current_sum - array[i] + array[i + k]
UpperCAmelCase : Tuple = max(UpperCamelCase , UpperCamelCase )
return max_sum
if __name__ == "__main__":
from doctest import testmod
from random import randint
testmod()
A: str = [randint(-1_0_0_0, 1_0_0_0) for i in range(1_0_0)]
A: int = randint(0, 1_1_0)
print(f"""The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}""")
| 160 | 1 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def lowerCamelCase_ ( _a : List[str] ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def lowerCamelCase_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCAmelCase_ : List[str] = [1, 2, 3]
with pytest.raises(_a ):
with parallel_backend("""unsupported backend""" ):
map_nested(_a , _a , num_proc=2 )
with pytest.raises(_a ):
with parallel_backend("""unsupported backend""" ):
map_nested(_a , _a , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" , [2, -1] )
def lowerCamelCase_ ( _a : Any ):
'''simple docstring'''
UpperCAmelCase_ : Any = [1, 2]
UpperCAmelCase_ : Optional[Any] = {"""a""": 1, """b""": 2}
UpperCAmelCase_ : Tuple = {"""a""": [1, 2], """b""": [3, 4]}
UpperCAmelCase_ : int = {"""a""": {"""1""": 1}, """b""": 2}
UpperCAmelCase_ : Dict = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCAmelCase_ : Union[str, Any] = [2, 3]
UpperCAmelCase_ : Optional[int] = {"""a""": 2, """b""": 3}
UpperCAmelCase_ : List[Any] = {"""a""": [2, 3], """b""": [4, 5]}
UpperCAmelCase_ : int = {"""a""": {"""1""": 2}, """b""": 3}
UpperCAmelCase_ : Tuple = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(_a , _a , num_proc=_a ) == expected_map_nested_sa
assert map_nested(_a , _a , num_proc=_a ) == expected_map_nested_sa
assert map_nested(_a , _a , num_proc=_a ) == expected_map_nested_sa
assert map_nested(_a , _a , num_proc=_a ) == expected_map_nested_sa
assert map_nested(_a , _a , num_proc=_a ) == expected_map_nested_sa
| 718 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _snake_case ( unittest.TestCase , __snake_case ):
'''simple docstring'''
def A__ ( self: Any ) -> Optional[Any]:
UpperCAmelCase_ : Dict = load_tool("""text-classification""" )
self.tool.setup()
UpperCAmelCase_ : List[str] = load_tool("""text-classification""" ,remote=lowerCamelCase_ )
def A__ ( self: List[str] ) -> str:
UpperCAmelCase_ : Tuple = self.tool("""That's quite cool""" ,["""positive""", """negative"""] )
self.assertEqual(lowerCamelCase_ ,"""positive""" )
def A__ ( self: List[Any] ) -> Dict:
UpperCAmelCase_ : List[str] = self.remote_tool("""That's quite cool""" ,["""positive""", """negative"""] )
self.assertEqual(lowerCamelCase_ ,"""positive""" )
def A__ ( self: str ) -> Tuple:
UpperCAmelCase_ : Optional[int] = self.tool(text="""That's quite cool""" ,labels=["""positive""", """negative"""] )
self.assertEqual(lowerCamelCase_ ,"""positive""" )
def A__ ( self: str ) -> Any:
UpperCAmelCase_ : Union[str, Any] = self.remote_tool(text="""That's quite cool""" ,labels=["""positive""", """negative"""] )
self.assertEqual(lowerCamelCase_ ,"""positive""" )
| 322 | 0 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def a ( self : Dict ) -> List[Any]:
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(SCREAMING_SNAKE_CASE__ ):
lowerCAmelCase__ = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@slow
def a ( self : List[Any] ) -> str:
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(SCREAMING_SNAKE_CASE__ ):
lowerCAmelCase__ = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@slow
def a ( self : List[Any] ) -> str:
for model_name in ["bert-base-cased", "bert-large-uncased"]:
lowerCAmelCase__ = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = tokenizer("Do you support jax jitted function?" , return_tensors=TensorType.JAX )
@jax.jit
def eval(**SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
return model(**SCREAMING_SNAKE_CASE__ )
eval(**SCREAMING_SNAKE_CASE__ ).block_until_ready()
@slow
def a ( self : Tuple ) -> Any:
for model_name in ["roberta-base", "roberta-large"]:
lowerCAmelCase__ = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = FlaxRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = tokenizer("Do you support jax jitted function?" , return_tensors=TensorType.JAX )
@jax.jit
def eval(**SCREAMING_SNAKE_CASE__ : Dict ):
return model(**SCREAMING_SNAKE_CASE__ )
eval(**SCREAMING_SNAKE_CASE__ ).block_until_ready()
def a ( self : Union[str, Any] ) -> Dict:
with self.assertRaisesRegex(
SCREAMING_SNAKE_CASE__ , "bert-base is not a local folder and is not a valid model identifier" ):
lowerCAmelCase__ = FlaxAutoModel.from_pretrained("bert-base" )
def a ( self : Dict ) -> Dict:
with self.assertRaisesRegex(
SCREAMING_SNAKE_CASE__ , r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
lowerCAmelCase__ = FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE__ , revision="aaaaaa" )
def a ( self : Optional[Any] ) -> int:
with self.assertRaisesRegex(
SCREAMING_SNAKE_CASE__ , "hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack" , ):
lowerCAmelCase__ = FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model" )
def a ( self : Dict ) -> Any:
with self.assertRaisesRegex(SCREAMING_SNAKE_CASE__ , "Use `from_pt=True` to load this model" ):
lowerCAmelCase__ = FlaxAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only" )
| 61 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
_SCREAMING_SNAKE_CASE : int = re.compile(R'''\s+''')
def lowerCamelCase__ ( _lowerCamelCase : Dict ) -> List[str]:
return {"hash": hashlib.mda(re.sub(_lowerCamelCase , '' , example['content'] ).encode('utf-8' ) ).hexdigest()}
def lowerCamelCase__ ( _lowerCamelCase : Tuple ) -> int:
lowerCamelCase_ = [len(_lowerCamelCase ) for line in example['content'].splitlines()]
return {"line_mean": np.mean(_lowerCamelCase ), "line_max": max(_lowerCamelCase )}
def lowerCamelCase__ ( _lowerCamelCase : List[str] ) -> int:
lowerCamelCase_ = np.mean([c.isalnum() for c in example['content']] )
return {"alpha_frac": alpha_frac}
def lowerCamelCase__ ( _lowerCamelCase : int , _lowerCamelCase : Optional[int] ) -> Optional[Any]:
if example["hash"] in uniques:
uniques.remove(example['hash'] )
return True
else:
return False
def lowerCamelCase__ ( _lowerCamelCase : Any , _lowerCamelCase : Optional[Any]=5 ) -> int:
lowerCamelCase_ = ['auto-generated', 'autogenerated', 'automatically generated']
lowerCamelCase_ = example['content'].splitlines()
for _, line in zip(range(_lowerCamelCase ) , _lowerCamelCase ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def lowerCamelCase__ ( _lowerCamelCase : Tuple , _lowerCamelCase : Dict=5 , _lowerCamelCase : List[str]=0.05 ) -> Tuple:
lowerCamelCase_ = ['unit tests', 'test file', 'configuration file']
lowerCamelCase_ = example['content'].splitlines()
lowerCamelCase_ = 0
lowerCamelCase_ = 0
# first test
for _, line in zip(range(_lowerCamelCase ) , _lowerCamelCase ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
lowerCamelCase_ = example['content'].count('\n' )
lowerCamelCase_ = int(coeff * nlines )
for line in lines:
count_config += line.lower().count('config' )
count_test += line.lower().count('test' )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def lowerCamelCase__ ( _lowerCamelCase : Any ) -> List[str]:
lowerCamelCase_ = ['def ', 'class ', 'for ', 'while ']
lowerCamelCase_ = example['content'].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def lowerCamelCase__ ( _lowerCamelCase : Dict , _lowerCamelCase : Dict=4 ) -> Optional[Any]:
lowerCamelCase_ = example['content'].splitlines()
lowerCamelCase_ = 0
for line in lines:
counter += line.lower().count('=' )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def lowerCamelCase__ ( _lowerCamelCase : Dict ) -> List[str]:
lowerCamelCase_ = tokenizer(example['content'] , truncation=_lowerCamelCase )['input_ids']
lowerCamelCase_ = len(example['content'] ) / len(_lowerCamelCase )
return {"ratio": ratio}
def lowerCamelCase__ ( _lowerCamelCase : Optional[Any] ) -> List[Any]:
lowerCamelCase_ = {}
results.update(get_hash(_lowerCamelCase ) )
results.update(line_stats(_lowerCamelCase ) )
results.update(alpha_stats(_lowerCamelCase ) )
results.update(char_token_ratio(_lowerCamelCase ) )
results.update(is_autogenerated(_lowerCamelCase ) )
results.update(is_config_or_test(_lowerCamelCase ) )
results.update(has_no_keywords(_lowerCamelCase ) )
results.update(has_few_assignments(_lowerCamelCase ) )
return results
def lowerCamelCase__ ( _lowerCamelCase : Any , _lowerCamelCase : Dict , _lowerCamelCase : Optional[Any] ) -> Any:
if not check_uniques(_lowerCamelCase , _lowerCamelCase ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def lowerCamelCase__ ( _lowerCamelCase : str ) -> int:
with open(_lowerCamelCase , 'rb' ) as f_in:
with gzip.open(str(_lowerCamelCase ) + '.gz' , 'wb' , compresslevel=6 ) as f_out:
shutil.copyfileobj(_lowerCamelCase , _lowerCamelCase )
os.unlink(_lowerCamelCase )
# Settings
_SCREAMING_SNAKE_CASE : Optional[Any] = HfArgumentParser(PreprocessingArguments)
_SCREAMING_SNAKE_CASE : Any = parser.parse_args()
if args.num_workers is None:
_SCREAMING_SNAKE_CASE : List[str] = multiprocessing.cpu_count()
_SCREAMING_SNAKE_CASE : str = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
_SCREAMING_SNAKE_CASE : Optional[Any] = time.time()
_SCREAMING_SNAKE_CASE : int = load_dataset(args.dataset_name, split='''train''')
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')
# Run preprocessing
_SCREAMING_SNAKE_CASE : Optional[Any] = time.time()
_SCREAMING_SNAKE_CASE : Tuple = ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')
# Deduplicate hashes
_SCREAMING_SNAKE_CASE : List[Any] = set(ds.unique('''hash'''))
_SCREAMING_SNAKE_CASE : Optional[int] = len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')
# Deduplicate data and apply heuristics
_SCREAMING_SNAKE_CASE : Dict = time.time()
_SCREAMING_SNAKE_CASE : Tuple = ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(F'''Size of filtered dataset: {len(ds_filter)}''')
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
_SCREAMING_SNAKE_CASE : Optional[Any] = time.time()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Union[str, Any] = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
print(F'''Size of deduplicate dataset: {len(ds_filter)}''')
# Save data in batches of samples_per_file
_SCREAMING_SNAKE_CASE : Dict = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
json.dump(duplicate_clusters, f)
_SCREAMING_SNAKE_CASE : Optional[Any] = output_dir / '''data'''
data_dir.mkdir(exist_ok=True)
_SCREAMING_SNAKE_CASE : Optional[Any] = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
_SCREAMING_SNAKE_CASE : Dict = str(data_dir / F'''file-{file_number+1:012}.json''')
_SCREAMING_SNAKE_CASE : Any = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F'''Time to save dataset: {time.time()-t_start:.2f}''')
| 549 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 714 | """simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __UpperCAmelCase ( lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = []
for part_id in partition_order:
_UpperCAmelCase = df.where(f'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
for row_idx, row in enumerate(lowercase ):
expected_row_ids_and_row_dicts.append((f'''{part_id}_{row_idx}''', row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_UpperCAmelCase = spark.range(1_00 ).repartition(1 )
_UpperCAmelCase = Spark(lowercase )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_UpperCAmelCase = spark.range(10 ).repartition(2 )
_UpperCAmelCase = [1, 0]
_UpperCAmelCase = _generate_iterable_examples(lowercase ,lowercase ) # Reverse the partitions.
_UpperCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase ,lowercase )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
_UpperCAmelCase , _UpperCAmelCase = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_UpperCAmelCase = spark.range(10 ).repartition(1 )
_UpperCAmelCase = SparkExamplesIterable(lowercase )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(lowercase ):
assert row_id == f'''0_{i}'''
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_UpperCAmelCase = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("""numpy.random.Generator""" ) as generator_mock:
_UpperCAmelCase = lambda lowercase : x.reverse()
_UpperCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase ,[2, 1, 0] )
_UpperCAmelCase = SparkExamplesIterable(lowercase ).shuffle_data_sources(lowercase )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(lowercase ):
_UpperCAmelCase , _UpperCAmelCase = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_UpperCAmelCase = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
_UpperCAmelCase = SparkExamplesIterable(lowercase ).shard_data_sources(worker_id=0 ,num_workers=2 )
assert shard_it_a.n_shards == 2
_UpperCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase ,[0, 2] )
for i, (row_id, row_dict) in enumerate(lowercase ):
_UpperCAmelCase , _UpperCAmelCase = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
_UpperCAmelCase = SparkExamplesIterable(lowercase ).shard_data_sources(worker_id=1 ,num_workers=2 )
assert shard_it_a.n_shards == 2
_UpperCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase ,[1, 3] )
for i, (row_id, row_dict) in enumerate(lowercase ):
_UpperCAmelCase , _UpperCAmelCase = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_UpperCAmelCase = spark.range(1_00 ).repartition(1 )
_UpperCAmelCase = Spark(lowercase )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 1_00
| 275 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
@property
def UpperCAmelCase__ ( self : Optional[Any] ) -> List[str]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def UpperCAmelCase__ ( self : List[str] ) -> Optional[Any]:
lowerCAmelCase :str = ort.SessionOptions()
lowerCAmelCase :Optional[Any] = False
return options
def UpperCAmelCase__ ( self : str ) -> str:
lowerCAmelCase :str = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
lowerCAmelCase :Optional[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
lowerCAmelCase :Tuple = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy' )
# using the PNDM scheduler by default
lowerCAmelCase :Optional[Any] = OnnxStableDiffusionInpaintPipelineLegacy.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 :Optional[int] = 'A red cat sitting on a park bench'
lowerCAmelCase :Union[str, Any] = np.random.RandomState(0 )
lowerCAmelCase :str = pipe(
prompt=__lowerCamelCase , image=__lowerCamelCase , mask_image=__lowerCamelCase , strength=0.7_5 , guidance_scale=7.5 , num_inference_steps=15 , generator=__lowerCamelCase , output_type='np' , )
lowerCAmelCase :Any = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 1e-2 | 553 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCamelCase__ : Tuple = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
and Jeffrey Dean},
year={2016},
eprint={1609.08144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
UpperCamelCase__ : Union[str, Any] = """\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""
UpperCamelCase__ : Any = """\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
Args:
predictions (list of str): list of translations to score.
Each translation should be tokenized into a list of tokens.
references (list of list of str): list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
Returns:
'google_bleu': google_bleu score
Examples:
Example 1:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results[\"google_bleu\"], 2))
0.44
Example 2:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results[\"google_bleu\"], 2))
0.61
Example 3:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
>>> print(round(results[\"google_bleu\"], 2))
0.53
Example 4:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
>>> print(round(results[\"google_bleu\"], 2))
0.4
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase_ ( datasets.Metric ):
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' ,id='''token''' ) ,id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' ,id='''token''' ) ,id='''sequence''' ) ,id='''references''' ),
} ) ,)
def SCREAMING_SNAKE_CASE_ ( self : List[str] ,__lowerCamelCase : List[List[List[str]]] ,__lowerCamelCase : List[List[str]] ,__lowerCamelCase : int = 1 ,__lowerCamelCase : int = 4 ,):
'''simple docstring'''
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=__lowerCamelCase ,hypotheses=__lowerCamelCase ,min_len=__lowerCamelCase ,max_len=__lowerCamelCase )
}
| 387 | 0 |
'''simple docstring'''
__lowercase = 0 # The first color of the flag.
__lowercase = 1 # The second color of the flag.
__lowercase = 2 # The third color of the flag.
__lowercase = (red, white, blue)
def snake_case__ ( _A: list ) -> List[str]:
'''simple docstring'''
if not sequence:
return []
if len(__UpperCamelCase ) == 1:
return list(__UpperCamelCase )
lowerCAmelCase = 0
lowerCAmelCase = len(__UpperCamelCase ) - 1
lowerCAmelCase = 0
while mid <= high:
if sequence[mid] == colors[0]:
lowerCAmelCase = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
lowerCAmelCase = sequence[high], sequence[mid]
high -= 1
else:
lowerCAmelCase = f"The elements inside the sequence must contains only {colors} values"
raise ValueError(__UpperCamelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowercase = input('''Enter numbers separated by commas:\n''').strip()
__lowercase = [int(item.strip()) for item in user_input.split(''',''')]
print(f'{dutch_national_flag_sort(unsorted)}')
| 721 | '''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case__ ( _A: bytes ) -> bytes:
'''simple docstring'''
if len(_A ) != 32:
raise ValueError("""Input must be of length 32""" )
lowerCAmelCase = b""""""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def snake_case__ ( _A: int ) -> bytes:
'''simple docstring'''
if i < 0:
raise ValueError("""Input must be non-negative""" )
lowerCAmelCase = format(_A , """08x""" )[-8:]
lowerCAmelCase = b""""""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8""" )
return little_endian_hex
def snake_case__ ( _A: bytes ) -> bytes:
'''simple docstring'''
lowerCAmelCase = b""""""
for char in message:
bit_string += format(_A , """08b""" ).encode("""utf-8""" )
lowerCAmelCase = format(len(_A ) , """064b""" ).encode("""utf-8""" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(_A ) % 512 != 448:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )
return bit_string
def snake_case__ ( _A: bytes ) -> Generator[list[int], None, None]:
'''simple docstring'''
if len(_A ) % 512 != 0:
raise ValueError("""Input must have length that's a multiple of 512""" )
for pos in range(0 , len(_A ) , 512 ):
lowerCAmelCase = bit_string[pos : pos + 512]
lowerCAmelCase = []
for i in range(0 , 512 , 32 ):
block_words.append(int(to_little_endian(block[i : i + 32] ) , 2 ) )
yield block_words
def snake_case__ ( _A: int ) -> int:
'''simple docstring'''
if i < 0:
raise ValueError("""Input must be non-negative""" )
lowerCAmelCase = format(_A , """032b""" )
lowerCAmelCase = """"""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(_A , 2 )
def snake_case__ ( _A: int , _A: int ) -> int:
'''simple docstring'''
return (a + b) % 2**32
def snake_case__ ( _A: int , _A: int ) -> int:
'''simple docstring'''
if i < 0:
raise ValueError("""Input must be non-negative""" )
if shift < 0:
raise ValueError("""Shift must be non-negative""" )
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def snake_case__ ( _A: bytes ) -> bytes:
'''simple docstring'''
lowerCAmelCase = preprocess(_A )
lowerCAmelCase = [int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]
# Starting states
lowerCAmelCase = 0X6_7_4_5_2_3_0_1
lowerCAmelCase = 0Xe_f_c_d_a_b_8_9
lowerCAmelCase = 0X9_8_b_a_d_c_f_e
lowerCAmelCase = 0X1_0_3_2_5_4_7_6
lowerCAmelCase = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(_A ):
lowerCAmelCase = aa
lowerCAmelCase = ba
lowerCAmelCase = ca
lowerCAmelCase = da
# Hash current chunk
for i in range(64 ):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
lowerCAmelCase = d ^ (b & (c ^ d))
lowerCAmelCase = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
lowerCAmelCase = c ^ (d & (b ^ c))
lowerCAmelCase = (5 * i + 1) % 16
elif i <= 47:
lowerCAmelCase = b ^ c ^ d
lowerCAmelCase = (3 * i + 5) % 16
else:
lowerCAmelCase = c ^ (b | not_aa(_A ))
lowerCAmelCase = (7 * i) % 16
lowerCAmelCase = (f + a + added_consts[i] + block_words[g]) % 2**32
lowerCAmelCase = d
lowerCAmelCase = c
lowerCAmelCase = b
lowerCAmelCase = sum_aa(_A , left_rotate_aa(_A , shift_amounts[i] ) )
# Add hashed chunk to running total
lowerCAmelCase = sum_aa(_A , _A )
lowerCAmelCase = sum_aa(_A , _A )
lowerCAmelCase = sum_aa(_A , _A )
lowerCAmelCase = sum_aa(_A , _A )
lowerCAmelCase = reformat_hex(_A ) + reformat_hex(_A ) + reformat_hex(_A ) + reformat_hex(_A )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 605 | 0 |
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