code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
snake_case_ : Optional[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
snake_case_ : str = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
snake_case_ : Any = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 83 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _A ( ):
"""simple docstring"""
a =ArgumentParser(
description=(
'''PyTorch TPU distributed training launch '''
'''helper utility that will spawn up '''
'''multiple distributed processes'''
) )
# Optional arguments for the launch helper
parser.add_argument('''--num_cores''' , type=lowercase , default=1 , help='''Number of TPU cores to use (1 or 8).''' )
# positional
parser.add_argument(
'''training_script''' , type=lowercase , help=(
'''The full path to the single TPU training '''
'''program/script to be launched in parallel, '''
'''followed by all the arguments for the '''
'''training script'''
) , )
# rest from the training program
parser.add_argument('''training_script_args''' , nargs=lowercase )
return parser.parse_args()
def _A ( ):
"""simple docstring"""
a =parse_args()
# Import training_script as a module.
a =Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
a =script_fpath.stem
a =importlib.import_module(lowercase )
# Patch sys.argv
a =[args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main() | 81 | 0 |
'''simple docstring'''
import math
from collections.abc import Callable
def _snake_case ( A , A , A ) -> float:
lowerCAmelCase__ = xa
lowerCAmelCase__ = xa
while True:
if x_n == x_na or function(_lowerCamelCase ) == function(_lowerCamelCase ):
raise ZeroDivisionError('''float division by zero, could not find root''' )
lowerCAmelCase__ = x_na - (
function(_lowerCamelCase ) / ((function(_lowerCamelCase ) - function(_lowerCamelCase )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
lowerCAmelCase__ = x_na
lowerCAmelCase__ = x_na
def _snake_case ( A ) -> float:
return math.pow(_lowerCamelCase , 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5)) | 370 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
__UpperCAmelCase = logging.get_logger(__name__)
def _snake_case ( A , A , A ) -> Optional[Any]:
return [
int(1000 * (box[0] / width) ),
int(1000 * (box[1] / height) ),
int(1000 * (box[2] / width) ),
int(1000 * (box[3] / height) ),
]
def _snake_case ( A , A , A ) -> Union[str, Any]:
lowerCAmelCase__ = to_pil_image(A )
lowerCAmelCase__ , lowerCAmelCase__ = pil_image.size
lowerCAmelCase__ = pytesseract.image_to_data(A , lang=A , output_type='''dict''' , config=A )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']
# filter empty words and corresponding coordinates
lowerCAmelCase__ = [idx for idx, word in enumerate(A ) if not word.strip()]
lowerCAmelCase__ = [word for idx, word in enumerate(A ) if idx not in irrelevant_indices]
lowerCAmelCase__ = [coord for idx, coord in enumerate(A ) if idx not in irrelevant_indices]
lowerCAmelCase__ = [coord for idx, coord in enumerate(A ) if idx not in irrelevant_indices]
lowerCAmelCase__ = [coord for idx, coord in enumerate(A ) if idx not in irrelevant_indices]
lowerCAmelCase__ = [coord for idx, coord in enumerate(A ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
lowerCAmelCase__ = []
for x, y, w, h in zip(A , A , A , A ):
lowerCAmelCase__ = [x, y, x + w, y + h]
actual_boxes.append(A )
# finally, normalize the bounding boxes
lowerCAmelCase__ = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(A , A , A ) )
assert len(A ) == len(A ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class a__ ( a__ ):
'''simple docstring'''
lowercase__ : Any = ["pixel_values"]
def __init__( self , lowerCamelCase_ = True , lowerCamelCase_ = None , lowerCamelCase_ = PILImageResampling.BILINEAR , lowerCamelCase_ = True , lowerCamelCase_ = 1 / 2_55 , lowerCamelCase_ = True , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = True , lowerCamelCase_ = None , lowerCamelCase_ = "" , **lowerCamelCase_ , ) -> None:
super().__init__(**lowerCamelCase_ )
lowerCAmelCase__ = size if size is not None else {'''height''': 2_24, '''width''': 2_24}
lowerCAmelCase__ = get_size_dict(lowerCamelCase_ )
lowerCAmelCase__ = do_resize
lowerCAmelCase__ = size
lowerCAmelCase__ = resample
lowerCAmelCase__ = do_rescale
lowerCAmelCase__ = rescale_value
lowerCAmelCase__ = do_normalize
lowerCAmelCase__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
lowerCAmelCase__ = apply_ocr
lowerCAmelCase__ = ocr_lang
lowerCAmelCase__ = tesseract_config
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = PILImageResampling.BILINEAR , lowerCamelCase_ = None , **lowerCamelCase_ , ) -> np.ndarray:
lowerCAmelCase__ = get_size_dict(lowerCamelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
lowerCAmelCase__ = (size['''height'''], size['''width'''])
return resize(lowerCamelCase_ , size=lowerCamelCase_ , resample=lowerCamelCase_ , data_format=lowerCamelCase_ , **lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = None , **lowerCamelCase_ , ) -> np.ndarray:
return rescale(lowerCamelCase_ , scale=lowerCamelCase_ , data_format=lowerCamelCase_ , **lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = None , **lowerCamelCase_ , ) -> np.ndarray:
return normalize(lowerCamelCase_ , mean=lowerCamelCase_ , std=lowerCamelCase_ , data_format=lowerCamelCase_ , **lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_=None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = ChannelDimension.FIRST , **lowerCamelCase_ , ) -> PIL.Image.Image:
lowerCAmelCase__ = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase__ = size if size is not None else self.size
lowerCAmelCase__ = get_size_dict(lowerCamelCase_ )
lowerCAmelCase__ = resample if resample is not None else self.resample
lowerCAmelCase__ = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase__ = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase__ = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase__ = image_std if image_std is not None else self.image_std
lowerCAmelCase__ = apply_ocr if apply_ocr is not None else self.apply_ocr
lowerCAmelCase__ = ocr_lang if ocr_lang is not None else self.ocr_lang
lowerCAmelCase__ = tesseract_config if tesseract_config is not None else self.tesseract_config
lowerCAmelCase__ = 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_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.''' )
# All transformations expect numpy arrays.
lowerCAmelCase__ = [to_numpy_array(lowerCamelCase_ ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , '''pytesseract''' )
lowerCAmelCase__ = []
lowerCAmelCase__ = []
for image in images:
lowerCAmelCase__ , lowerCAmelCase__ = apply_tesseract(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
words_batch.append(lowerCamelCase_ )
boxes_batch.append(lowerCamelCase_ )
if do_resize:
lowerCAmelCase__ = [self.resize(image=lowerCamelCase_ , size=lowerCamelCase_ , resample=lowerCamelCase_ ) for image in images]
if do_rescale:
lowerCAmelCase__ = [self.rescale(image=lowerCamelCase_ , scale=lowerCamelCase_ ) for image in images]
if do_normalize:
lowerCAmelCase__ = [self.normalize(image=lowerCamelCase_ , mean=lowerCamelCase_ , std=lowerCamelCase_ ) for image in images]
lowerCAmelCase__ = [to_channel_dimension_format(lowerCamelCase_ , lowerCamelCase_ ) for image in images]
lowerCAmelCase__ = BatchFeature(data={'''pixel_values''': images} , tensor_type=lowerCamelCase_ )
if apply_ocr:
lowerCAmelCase__ = words_batch
lowerCAmelCase__ = boxes_batch
return data | 228 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ = {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['BeitFeatureExtractor']
lowerCAmelCase__ = ['BeitImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BeitForImageClassification',
'BeitForMaskedImageModeling',
'BeitForSemanticSegmentation',
'BeitModel',
'BeitPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'FlaxBeitForImageClassification',
'FlaxBeitForMaskedImageModeling',
'FlaxBeitModel',
'FlaxBeitPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 11 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "vocab.txt"}
_lowerCamelCase ={
"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",
},
}
_lowerCamelCase ={
"facebook/esm2_t6_8M_UR50D": 10_24,
"facebook/esm2_t12_35M_UR50D": 10_24,
}
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
with open(lowerCAmelCase_, 'r' ) as f:
SCREAMING_SNAKE_CASE =f.read().splitlines()
return [l.strip() for l in lines]
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ['input_ids', 'attention_mask']
def __init__( self : int ,snake_case : Dict ,snake_case : Dict="<unk>" ,snake_case : Optional[int]="<cls>" ,snake_case : Optional[int]="<pad>" ,snake_case : int="<mask>" ,snake_case : Optional[int]="<eos>" ,**snake_case : List[str] ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =load_vocab_file(snake_case )
SCREAMING_SNAKE_CASE =dict(enumerate(self.all_tokens ) )
SCREAMING_SNAKE_CASE ={tok: ind for ind, tok in enumerate(self.all_tokens )}
SCREAMING_SNAKE_CASE =unk_token
SCREAMING_SNAKE_CASE =cls_token
SCREAMING_SNAKE_CASE =pad_token
SCREAMING_SNAKE_CASE =mask_token
SCREAMING_SNAKE_CASE =eos_token
SCREAMING_SNAKE_CASE =self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : Dict ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Tuple ,snake_case : List[str] ,**snake_case : Any ):
return text.split()
def _lowerCAmelCase ( self : Optional[int] ,snake_case : str=False ):
return len(self._id_to_token )
def _lowerCAmelCase ( self : List[str] ):
return {token: i for i, token in enumerate(self.all_tokens )}
def _lowerCAmelCase ( self : List[Any] ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Any ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : List[str] ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE =[self.cls_token_id]
SCREAMING_SNAKE_CASE =[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 : Optional[int] ,snake_case : List ,snake_case : Optional[List] = None ,snake_case : bool = 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]
SCREAMING_SNAKE_CASE =[1] + ([0] * len(snake_case )) + [1]
if token_ids_a is not None:
mask += [0] * len(snake_case ) + [1]
return mask
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Dict ,snake_case : Any ):
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt' )
with open(snake_case ,'w' ) as f:
f.write('\n'.join(self.all_tokens ) )
return (vocab_file,)
@property
def _lowerCAmelCase ( self : int ):
return self.get_vocab_size(with_added_tokens=snake_case )
def _lowerCAmelCase ( self : str ,snake_case : Union[List[str], List[AddedToken]] ,snake_case : bool = False ):
return super()._add_tokens(snake_case ,special_tokens=snake_case )
| 334 | 0 |
class snake_case_ :
def __init__( self : List[str] ) -> Union[str, Any]:
lowercase__ : Optional[Any] = 0
lowercase__ : str = 0
lowercase__ : Union[str, Any] = {}
def __UpperCamelCase ( self : Any , lowercase_ : List[Any] ) -> int:
if vertex not in self.adjacency:
lowercase__ : List[str] = {}
self.num_vertices += 1
def __UpperCamelCase ( self : Union[str, Any] , lowercase_ : int , lowercase_ : List[Any] , lowercase_ : List[str] ) -> Any:
self.add_vertex(UpperCAmelCase__ )
self.add_vertex(UpperCAmelCase__ )
if head == tail:
return
lowercase__ : Any = weight
lowercase__ : Optional[int] = weight
def __UpperCamelCase ( self : Tuple ) -> int:
lowercase__ : List[str] = self.get_edges()
for edge in edges:
lowercase__ , lowercase__ , lowercase__ : Dict = edge
edges.remove((tail, head, weight) )
for i in range(len(UpperCAmelCase__ ) ):
lowercase__ : Optional[int] = list(edges[i] )
edges.sort(key=lambda lowercase_ : e[2] )
for i in range(len(UpperCAmelCase__ ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
lowercase__ : List[Any] = edges[i][2] + 1
for edge in edges:
lowercase__ , lowercase__ , lowercase__ : int = edge
lowercase__ : List[Any] = weight
lowercase__ : str = weight
def __str__( self : Dict ) -> Optional[int]:
lowercase__ : Optional[int] = ""
for tail in self.adjacency:
for head in self.adjacency[tail]:
lowercase__ : Any = self.adjacency[head][tail]
string += F'''{head} -> {tail} == {weight}\n'''
return string.rstrip("\n" )
def __UpperCamelCase ( self : str ) -> Tuple:
lowercase__ : int = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def __UpperCamelCase ( self : List[Any] ) -> Union[str, Any]:
return self.adjacency.keys()
@staticmethod
def __UpperCamelCase ( lowercase_ : Any=None , lowercase_ : Optional[Any]=None ) -> List[str]:
lowercase__ : Optional[Any] = Graph()
if vertices is None:
lowercase__ : str = []
if edges is None:
lowercase__ : Tuple = []
for vertex in vertices:
g.add_vertex(UpperCAmelCase__ )
for edge in edges:
g.add_edge(*UpperCAmelCase__ )
return g
class snake_case_ :
def __init__( self : Optional[Any] ) -> List[str]:
lowercase__ : Any = {}
lowercase__ : Optional[int] = {}
def __len__( self : Dict ) -> Dict:
return len(self.parent )
def __UpperCamelCase ( self : Optional[int] , lowercase_ : Optional[int] ) -> Optional[int]:
if item in self.parent:
return self.find(UpperCAmelCase__ )
lowercase__ : Optional[int] = item
lowercase__ : Tuple = 0
return item
def __UpperCamelCase ( self : Tuple , lowercase_ : int ) -> int:
if item not in self.parent:
return self.make_set(UpperCAmelCase__ )
if item != self.parent[item]:
lowercase__ : List[Any] = self.find(self.parent[item] )
return self.parent[item]
def __UpperCamelCase ( self : Any , lowercase_ : Union[str, Any] , lowercase_ : Any ) -> Optional[int]:
lowercase__ : List[str] = self.find(UpperCAmelCase__ )
lowercase__ : int = self.find(UpperCAmelCase__ )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
lowercase__ : Optional[int] = roota
return roota
if self.rank[roota] < self.rank[roota]:
lowercase__ : Union[str, Any] = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
lowercase__ : Tuple = roota
return roota
return None
@staticmethod
def __UpperCamelCase ( lowercase_ : Optional[Any] ) -> str:
lowercase__ : Tuple = graph.num_vertices
lowercase__ : Tuple = Graph.UnionFind()
lowercase__ : List[str] = []
while num_components > 1:
lowercase__ : Optional[int] = {}
for vertex in graph.get_vertices():
lowercase__ : Union[str, Any] = -1
lowercase__ : Union[str, Any] = graph.get_edges()
for edge in edges:
lowercase__ , lowercase__ , lowercase__ : List[Any] = edge
edges.remove((tail, head, weight) )
for edge in edges:
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = edge
lowercase__ : Tuple = union_find.find(UpperCAmelCase__ )
lowercase__ : Any = union_find.find(UpperCAmelCase__ )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowercase__ : int = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowercase__ : Union[str, Any] = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
lowercase__ , lowercase__ , lowercase__ : Tuple = cheap_edge[vertex]
if union_find.find(UpperCAmelCase__ ) != union_find.find(UpperCAmelCase__ ):
union_find.union(UpperCAmelCase__ , UpperCAmelCase__ )
mst_edges.append(cheap_edge[vertex] )
lowercase__ : List[Any] = num_components - 1
lowercase__ : int = Graph.build(edges=UpperCAmelCase__ )
return mst
| 361 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
'''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
'''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
'''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
'''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}
class snake_case_ ( __A ):
__A : Optional[int] = "rwkv"
__A : List[str] = {"max_position_embeddings": "context_length"}
def __init__( self : Dict , lowercase_ : List[Any]=5_02_77 , lowercase_ : Union[str, Any]=10_24 , lowercase_ : Any=40_96 , lowercase_ : int=32 , lowercase_ : Dict=None , lowercase_ : str=None , lowercase_ : Any=1E-5 , lowercase_ : Optional[Any]=0 , lowercase_ : Any=0 , lowercase_ : List[str]=6 , lowercase_ : List[Any]=False , lowercase_ : int=True , **lowercase_ : List[str] , ) -> int:
lowercase__ : List[str] = vocab_size
lowercase__ : str = context_length
lowercase__ : List[Any] = hidden_size
lowercase__ : Optional[Any] = num_hidden_layers
lowercase__ : Optional[Any] = attention_hidden_size if attention_hidden_size is not None else hidden_size
lowercase__ : str = intermediate_size if intermediate_size is not None else 4 * hidden_size
lowercase__ : List[Any] = layer_norm_epsilon
lowercase__ : str = rescale_every
lowercase__ : Optional[int] = use_cache
lowercase__ : int = bos_token_id
lowercase__ : Optional[Any] = eos_token_id
super().__init__(
tie_word_embeddings=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
| 333 | 0 |
"""simple docstring"""
import qiskit
def lowercase ( _SCREAMING_SNAKE_CASE : int = 2 ):
'''simple docstring'''
_UpperCAmelCase = qubits
# Using Aer's simulator
_UpperCAmelCase = qiskit.Aer.get_backend('''aer_simulator''' )
# Creating a Quantum Circuit acting on the q register
_UpperCAmelCase = qiskit.QuantumCircuit(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Adding a H gate on qubit 0 (now q0 in superposition)
circuit.h(0 )
for i in range(1 , _SCREAMING_SNAKE_CASE ):
# Adding CX (CNOT) gate
circuit.cx(i - 1 , _SCREAMING_SNAKE_CASE )
# Mapping the quantum measurement to the classical bits
circuit.measure(list(range(_SCREAMING_SNAKE_CASE ) ) , list(range(_SCREAMING_SNAKE_CASE ) ) )
# Now measuring any one qubit would affect other qubits to collapse
# their super position and have same state as the measured one.
# Executing the circuit on the simulator
_UpperCAmelCase = qiskit.execute(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , shots=1000 )
return job.result().get_counts(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(f'''Total count for various states are: {quantum_entanglement(3)}''')
| 260 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
__A : str = sys.version_info >= (3, 10)
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple=None , _SCREAMING_SNAKE_CASE : Tuple=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=_SCREAMING_SNAKE_CASE )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = field(default="""toto""" , metadata={"""help""": """help message"""})
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """titi"""
UpperCamelCase__ = """toto"""
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """titi"""
UpperCamelCase__ = """toto"""
UpperCamelCase__ = 42
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
def lowercase__ ( self : Tuple )->Optional[int]:
_UpperCAmelCase = BasicEnum(self.foo )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
def lowercase__ ( self : List[str] )->List[Any]:
_UpperCAmelCase = MixedTypeEnum(self.foo )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = None
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """help message"""})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[1, 2, 3])
UpperCamelCase__ = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
UpperCamelCase__ = list_field(default=[0.1, 0.2, 0.3])
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field()
UpperCamelCase__ = field()
UpperCamelCase__ = field()
def lowercase__ ( self : int )->str:
_UpperCAmelCase = BasicEnum(self.required_enum )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = field()
UpperCamelCase__ = None
UpperCamelCase__ = field(default="""toto""" , metadata={"""help""": """help message"""})
UpperCamelCase__ = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
if is_python_no_less_than_3_10:
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = None
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """help message"""})
UpperCamelCase__ = None
UpperCamelCase__ = list_field(default=[])
UpperCamelCase__ = list_field(default=[])
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : int , __UpperCamelCase : argparse.ArgumentParser , __UpperCamelCase : argparse.ArgumentParser )->Dict:
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
_UpperCAmelCase = {k: v for k, v in vars(__UpperCamelCase ).items() if k != '''container'''}
_UpperCAmelCase = {k: v for k, v in vars(__UpperCamelCase ).items() if k != '''container'''}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('''choices''' , __UpperCamelCase ) and yy.get('''choices''' , __UpperCamelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['''type'''](__UpperCamelCase ) , yy['''type'''](__UpperCamelCase ) )
del xx["type"], yy["type"]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : int )->str:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--bar''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--baz''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--flag''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = ['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
((_UpperCAmelCase) , ) = parser.parse_args_into_dataclasses(__UpperCamelCase , look_for_args_file=__UpperCamelCase )
self.assertFalse(example.flag )
def lowercase__ ( self : Dict )->List[Any]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=4_2 , type=__UpperCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=__UpperCamelCase , help='''help message''' )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Tuple )->List[str]:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
expected.add_argument('''--baz''' , type=__UpperCamelCase , default=__UpperCamelCase , const=__UpperCamelCase , nargs='''?''' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('''--no_baz''' , action='''store_false''' , default=__UpperCamelCase , dest='''baz''' )
expected.add_argument('''--opt''' , type=__UpperCamelCase , default=__UpperCamelCase )
_UpperCAmelCase = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''--no_baz'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''--baz'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False'''] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , baz=__UpperCamelCase , opt=__UpperCamelCase ) )
def lowercase__ ( self : Optional[Any] )->str:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=['''titi''', '''toto''', 4_2] , type=make_choice_type_function(['''titi''', '''toto''', 4_2] ) , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
_UpperCAmelCase = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['''--foo''', '''titi'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 4_2 )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['''--foo''', '''42'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def lowercase__ ( self : List[str] )->List[str]:
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = "toto"
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=('''titi''', '''toto''', 4_2) , type=make_choice_type_function(['''titi''', '''toto''', 4_2] ) , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
_UpperCAmelCase = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 4_2 )
def lowercase__ ( self : int )->int:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo_int''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
expected.add_argument('''--bar_int''' , nargs='''+''' , default=[1, 2, 3] , type=__UpperCamelCase )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=__UpperCamelCase )
expected.add_argument('''--foo_float''' , nargs='''+''' , default=[0.1, 0.2, 0.3] , type=__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(
__UpperCamelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['''Hallo''', '''Bonjour''', '''Hello'''] , foo_float=[0.1, 0.2, 0.3] ) , )
_UpperCAmelCase = parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split() )
self.assertEqual(__UpperCamelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['''a''', '''b''', '''c'''] , foo_float=[0.1, 0.7] ) )
def lowercase__ ( self : Union[str, Any] )->Tuple:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=__UpperCamelCase , type=__UpperCamelCase )
expected.add_argument('''--bar''' , default=__UpperCamelCase , type=__UpperCamelCase , help='''help message''' )
expected.add_argument('''--baz''' , default=__UpperCamelCase , type=__UpperCamelCase )
expected.add_argument('''--ces''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
expected.add_argument('''--des''' , nargs='''+''' , default=[] , type=__UpperCamelCase )
_UpperCAmelCase = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__UpperCamelCase , Namespace(foo=__UpperCamelCase , bar=__UpperCamelCase , baz=__UpperCamelCase , ces=[] , des=[] ) )
_UpperCAmelCase = parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split() )
self.assertEqual(__UpperCamelCase , Namespace(foo=1_2 , bar=3.1_4 , baz='''42''' , ces=['''a''', '''b''', '''c'''] , des=[1, 2, 3] ) )
def lowercase__ ( self : Any )->int:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--required_list''' , nargs='''+''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument('''--required_str''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=__UpperCamelCase , )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : str )->List[Any]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=__UpperCamelCase , required=__UpperCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=__UpperCamelCase , )
expected.add_argument('''--opt''' , type=__UpperCamelCase , default=__UpperCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=__UpperCamelCase , help='''help message''' )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=__UpperCamelCase )
self.argparsersEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->Optional[int]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
_UpperCAmelCase = parser.parse_dict(__UpperCamelCase )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->List[str]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
'''extra''': 4_2,
}
self.assertRaises(__UpperCamelCase , parser.parse_dict , __UpperCamelCase , allow_extra_keys=__UpperCamelCase )
def lowercase__ ( self : Optional[Any] )->Optional[int]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''temp_json''' )
os.mkdir(__UpperCamelCase )
with open(temp_local_path + '''.json''' , '''w+''' ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '''.json''' ) )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : Union[str, Any] )->Any:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
_UpperCAmelCase = {
'''foo''': 1_2,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''temp_yaml''' )
os.mkdir(__UpperCamelCase )
with open(temp_local_path + '''.yaml''' , '''w+''' ) as f:
yaml.dump(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '''.yaml''' ) )[0]
_UpperCAmelCase = BasicExample(**__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( self : int )->List[str]:
_UpperCAmelCase = HfArgumentParser(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
| 260 | 1 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a_ :
'''simple docstring'''
def __init__( self : Any , lowercase__ : Tuple , lowercase__ : Tuple=13 , lowercase__ : Optional[int]=30 , lowercase__ : List[str]=2 , lowercase__ : Dict=3 , lowercase__ : Optional[Any]=True , lowercase__ : List[str]=True , lowercase__ : str=32 , lowercase__ : Union[str, Any]=5 , lowercase__ : Optional[int]=4 , lowercase__ : Union[str, Any]=37 , lowercase__ : Tuple="gelu" , lowercase__ : List[str]=0.1 , lowercase__ : Optional[int]=0.1 , lowercase__ : str=10 , lowercase__ : int=0.02 , lowercase__ : Union[str, Any]=3 , lowercase__ : int=0.6 , lowercase__ : Optional[Any]=None , ):
'''simple docstring'''
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = patch_size
lowerCAmelCase__ = num_channels
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__ = type_sequence_label_size
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = mask_ratio
lowerCAmelCase__ = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
lowerCAmelCase__ = (image_size // patch_size) ** 2
lowerCAmelCase__ = int(math.ceil((1 - mask_ratio) * (num_patches + 1)))
def __snake_case ( self : List[Any]):
'''simple docstring'''
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
lowerCAmelCase__ = self.get_config()
return config, pixel_values, labels
def __snake_case ( self : str):
'''simple docstring'''
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase__ , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def __snake_case ( self : List[Any] , lowercase__ : List[str] , lowercase__ : Any , lowercase__ : Union[str, Any]):
'''simple docstring'''
lowerCAmelCase__ = ViTMAEModel(config=lowercase__)
model.to(lowercase__)
model.eval()
lowerCAmelCase__ = model(lowercase__)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def __snake_case ( self : Tuple , lowercase__ : List[Any] , lowercase__ : Optional[int] , lowercase__ : Any):
'''simple docstring'''
lowerCAmelCase__ = ViTMAEForPreTraining(lowercase__)
model.to(lowercase__)
model.eval()
lowerCAmelCase__ = model(lowercase__)
lowerCAmelCase__ = (self.image_size // self.patch_size) ** 2
lowerCAmelCase__ = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels))
# test greyscale images
lowerCAmelCase__ = 1
lowerCAmelCase__ = ViTMAEForPreTraining(lowercase__)
model.to(lowercase__)
model.eval()
lowerCAmelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
lowerCAmelCase__ = model(lowercase__)
lowerCAmelCase__ = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels))
def __snake_case ( self : Dict):
'''simple docstring'''
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = config_and_inputs
lowerCAmelCase__ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class a_ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
UpperCAmelCase_ = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = False
def __snake_case ( self : str):
'''simple docstring'''
lowerCAmelCase__ = ViTMAEModelTester(self)
lowerCAmelCase__ = ConfigTester(self , config_class=lowercase__ , has_text_modality=lowercase__ , hidden_size=37)
def __snake_case ( self : Union[str, Any]):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='ViTMAE does not use inputs_embeds')
def __snake_case ( self : Optional[Any]):
'''simple docstring'''
pass
def __snake_case ( self : Optional[Any]):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(lowercase__)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
lowerCAmelCase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase__ , nn.Linear))
def __snake_case ( self : List[Any]):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(lowercase__)
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] , lowercase__)
def __snake_case ( self : Dict):
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase__)
def __snake_case ( self : List[Any]):
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowercase__)
def __snake_case ( self : Optional[int] , lowercase__ : Optional[int] , lowercase__ : Union[str, Any] , lowercase__ : Dict):
'''simple docstring'''
np.random.seed(2)
lowerCAmelCase__ = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
lowerCAmelCase__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
lowerCAmelCase__ = torch.from_numpy(lowercase__)
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
lowerCAmelCase__ = pt_noise
super().check_pt_tf_models(lowercase__ , lowercase__ , lowercase__)
def __snake_case ( self : List[Any]):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(lowercase__)
model.to(lowercase__)
model.eval()
# make random mask reproducible
torch.manual_seed(2)
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(lowercase__ , lowercase__))
lowerCAmelCase__ = outputs[0].cpu().numpy()
lowerCAmelCase__ = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase__)
lowerCAmelCase__ = model_class.from_pretrained(lowercase__)
model.to(lowercase__)
# make random mask reproducible
torch.manual_seed(2)
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(lowercase__ , lowercase__))
# Make sure we don't have nans
lowerCAmelCase__ = after_outputs[0].cpu().numpy()
lowerCAmelCase__ = 0
lowerCAmelCase__ = np.amax(np.abs(out_a - out_a))
self.assertLessEqual(lowercase__ , 1e-5)
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.')
def __snake_case ( self : List[Any]):
'''simple docstring'''
pass
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.')
def __snake_case ( self : Tuple):
'''simple docstring'''
pass
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.')
def __snake_case ( self : Union[str, Any]):
'''simple docstring'''
pass
@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load')
def __snake_case ( self : int):
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
def __snake_case ( self : Optional[Any]):
'''simple docstring'''
pass
@slow
def __snake_case ( self : Tuple):
'''simple docstring'''
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = ViTMAEModel.from_pretrained(lowercase__)
self.assertIsNotNone(lowercase__)
def __lowerCamelCase ( ):
lowerCAmelCase__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __snake_case ( self : int):
'''simple docstring'''
return ViTImageProcessor.from_pretrained('facebook/vit-mae-base') if is_vision_available() else None
@slow
def __snake_case ( self : int):
'''simple docstring'''
np.random.seed(2)
lowerCAmelCase__ = ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(lowercase__)
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=lowercase__ , return_tensors='pt').to(lowercase__)
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
lowerCAmelCase__ = ViTMAEConfig()
lowerCAmelCase__ = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
lowerCAmelCase__ = np.random.uniform(size=(1, num_patches))
# forward pass
with torch.no_grad():
lowerCAmelCase__ = model(**lowercase__ , noise=torch.from_numpy(lowercase__).to(device=lowercase__))
# verify the logits
lowerCAmelCase__ = torch.Size((1, 196, 768))
self.assertEqual(outputs.logits.shape , lowercase__)
lowerCAmelCase__ = torch.tensor(
[[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]])
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(lowercase__) , atol=1e-4))
| 119 | import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
lowerCAmelCase__ = {
'vocab_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
lowerCAmelCase__ = {
'vocab_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
lowerCAmelCase__ = {
'vocab_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
),
},
}
lowerCAmelCase__ = {
'facebook/dpr-ctx_encoder-single-nq-base': 512,
'facebook/dpr-ctx_encoder-multiset-base': 512,
}
lowerCAmelCase__ = {
'facebook/dpr-question_encoder-single-nq-base': 512,
'facebook/dpr-question_encoder-multiset-base': 512,
}
lowerCAmelCase__ = {
'facebook/dpr-reader-single-nq-base': 512,
'facebook/dpr-reader-multiset-base': 512,
}
lowerCAmelCase__ = {
'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__ = {
'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__ = {
'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class a_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCAmelCase_ = VOCAB_FILES_NAMES
UpperCAmelCase_ = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class a_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCAmelCase_ = VOCAB_FILES_NAMES
UpperCAmelCase_ = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase__ = collections.namedtuple(
'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)
lowerCAmelCase__ = collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])
lowerCAmelCase__ = r'\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n ```\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n ```\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n - `\'np\'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Returns:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n '
@add_start_docstrings(SCREAMING_SNAKE_CASE )
class a_ :
'''simple docstring'''
def __call__( self : Optional[int] , lowercase__ : List[str] , lowercase__ : Optional[str] = None , lowercase__ : Optional[str] = None , lowercase__ : Union[bool, str] = False , lowercase__ : Union[bool, str] = False , lowercase__ : Optional[int] = None , lowercase__ : Optional[Union[str, TensorType]] = None , lowercase__ : Optional[bool] = None , **lowercase__ : Union[str, Any] , ):
'''simple docstring'''
if titles is None and texts is None:
return super().__call__(
lowercase__ , padding=lowercase__ , truncation=lowercase__ , max_length=lowercase__ , return_tensors=lowercase__ , return_attention_mask=lowercase__ , **lowercase__ , )
elif titles is None or texts is None:
lowerCAmelCase__ = titles if texts is None else texts
return super().__call__(
lowercase__ , lowercase__ , padding=lowercase__ , truncation=lowercase__ , max_length=lowercase__ , return_tensors=lowercase__ , return_attention_mask=lowercase__ , **lowercase__ , )
lowerCAmelCase__ = titles if not isinstance(lowercase__ , lowercase__) else [titles]
lowerCAmelCase__ = texts if not isinstance(lowercase__ , lowercase__) else [texts]
lowerCAmelCase__ = len(lowercase__)
lowerCAmelCase__ = questions if not isinstance(lowercase__ , lowercase__) else [questions] * n_passages
if len(lowercase__) != len(lowercase__):
raise ValueError(
F"""There should be as many titles than texts but got {len(lowercase__)} titles and {len(lowercase__)} texts.""")
lowerCAmelCase__ = super().__call__(lowercase__ , lowercase__ , padding=lowercase__ , truncation=lowercase__)['input_ids']
lowerCAmelCase__ = super().__call__(lowercase__ , add_special_tokens=lowercase__ , padding=lowercase__ , truncation=lowercase__)['input_ids']
lowerCAmelCase__ = {
'input_ids': [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(lowercase__ , lowercase__)
]
}
if return_attention_mask is not False:
lowerCAmelCase__ = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
lowerCAmelCase__ = attention_mask
return self.pad(lowercase__ , padding=lowercase__ , max_length=lowercase__ , return_tensors=lowercase__)
def __snake_case ( self : Union[str, Any] , lowercase__ : BatchEncoding , lowercase__ : DPRReaderOutput , lowercase__ : int = 16 , lowercase__ : int = 64 , lowercase__ : int = 4 , ):
'''simple docstring'''
lowerCAmelCase__ = reader_input['input_ids']
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = reader_output[:3]
lowerCAmelCase__ = len(lowercase__)
lowerCAmelCase__ = sorted(range(lowercase__) , reverse=lowercase__ , key=relevance_logits.__getitem__)
lowerCAmelCase__ = []
for doc_id in sorted_docs:
lowerCAmelCase__ = list(input_ids[doc_id])
# assuming question & title information is at the beginning of the sequence
lowerCAmelCase__ = sequence_ids.index(self.sep_token_id , 2) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
lowerCAmelCase__ = sequence_ids.index(self.pad_token_id)
else:
lowerCAmelCase__ = len(lowercase__)
lowerCAmelCase__ = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=lowercase__ , top_spans=lowercase__ , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=lowercase__ , start_index=lowercase__ , end_index=lowercase__ , text=self.decode(sequence_ids[start_index : end_index + 1]) , ))
if len(lowercase__) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def __snake_case ( self : Optional[int] , lowercase__ : List[int] , lowercase__ : List[int] , lowercase__ : int , lowercase__ : int , ):
'''simple docstring'''
lowerCAmelCase__ = []
for start_index, start_score in enumerate(lowercase__):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
scores.append(((start_index, start_index + answer_length), start_score + end_score))
lowerCAmelCase__ = sorted(lowercase__ , key=lambda lowercase__: x[1] , reverse=lowercase__)
lowerCAmelCase__ = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""")
lowerCAmelCase__ = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(F"""Span is too long: {length} > {max_answer_length}""")
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals):
continue
chosen_span_intervals.append((start_index, end_index))
if len(lowercase__) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(SCREAMING_SNAKE_CASE )
class a_ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCAmelCase_ = VOCAB_FILES_NAMES
UpperCAmelCase_ = READER_PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ = READER_PRETRAINED_INIT_CONFIGURATION
UpperCAmelCase_ = ['input_ids', 'attention_mask']
| 119 | 1 |
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : List[str] , lowercase : list[int] ):
'''simple docstring'''
_snake_case = len(lowercase )
_snake_case = [0] * len_array
if len_array > 0:
_snake_case = array[0]
for i in range(1 , lowercase ):
_snake_case = self.prefix_sum[i - 1] + array[i]
def A ( self : Optional[Any] , lowercase : int , lowercase : int ):
'''simple docstring'''
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def A ( self : Union[str, Any] , lowercase : int ):
'''simple docstring'''
_snake_case = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(lowercase )
return False
if __name__ == "__main__":
import doctest
doctest.testmod() | 282 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def A ( self : Optional[int] ):
'''simple docstring'''
_snake_case = 'hf-internal-testing/tiny-random-t5'
_snake_case = AutoTokenizer.from_pretrained(lowercase )
_snake_case = AutoModelForSeqaSeqLM.from_pretrained(lowercase )
_snake_case = tokenizer('This is me' , return_tensors='pt' )
_snake_case = model.to_bettertransformer()
self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
_snake_case = model.generate(**lowercase )
_snake_case = model.reverse_bettertransformer()
self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase )
_snake_case = AutoModelForSeqaSeqLM.from_pretrained(lowercase )
self.assertFalse(
any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
_snake_case = model_reloaded.generate(**lowercase )
self.assertTrue(torch.allclose(lowercase , lowercase ) )
def A ( self : List[Any] ):
'''simple docstring'''
_snake_case = 'hf-internal-testing/tiny-random-t5'
_snake_case = AutoModelForSeqaSeqLM.from_pretrained(lowercase )
_snake_case = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(lowercase ):
model.save_pretrained(lowercase )
_snake_case = model.reverse_bettertransformer()
model.save_pretrained(lowercase ) | 282 | 1 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def snake_case (A_ :int , A_ :int , A_ :Any , A_ :str=5 ):
assert masked_input.count('<mask>' ) == 1
a : int = torch.tensor(tokenizer.encode(A_ , add_special_tokens=A_ ) ).unsqueeze(0 ) # Batch size 1
a : Optional[int] = model(A_ )[0] # The last hidden-state is the first element of the output tuple
a : Optional[Any] = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
a : int = logits[0, masked_index, :]
a : Dict = logits.softmax(dim=0 )
a : List[Any] = prob.topk(k=A_ , dim=0 )
a : Dict = ' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(A_ ) )] )
a : Dict = tokenizer.mask_token
a : Optional[Any] = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
a : Optional[int] = predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(A_ ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(A_ ) , A_ ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(A_ , A_ ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
_UpperCamelCase : Any = CamembertTokenizer.from_pretrained('camembert-base')
_UpperCamelCase : Union[str, Any] = CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()
_UpperCamelCase : int = 'Le camembert est <mask> :)'
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 358 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def snake_case (A_ :Dict ):
'''simple docstring'''
a : str = []
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
f'''stage{idx}.patch_embed.proj.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
f'''stage{idx}.patch_embed.proj.bias''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
f'''stage{idx}.patch_embed.norm.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
f'''stage{idx}.patch_embed.norm.bias''',
) )
return embed
def snake_case (A_ :Any , A_ :List[Any] ):
'''simple docstring'''
a : Union[str, Any] = []
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
) )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''') )
return attention_weights
def snake_case (A_ :Dict ):
'''simple docstring'''
a : int = []
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token') )
return token
def snake_case ():
'''simple docstring'''
a : int = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def snake_case (A_ :int , A_ :Optional[int] , A_ :Dict , A_ :Dict ):
'''simple docstring'''
a : Optional[Any] = 'imagenet-1k-id2label.json'
a : Dict = 1_0_0_0
a : Tuple = 'huggingface/label-files'
a : List[Any] = num_labels
a : List[str] = json.load(open(cached_download(hf_hub_url(A_ , A_ , repo_type='dataset' ) ) , 'r' ) )
a : int = {int(A_ ): v for k, v in idalabel.items()}
a : str = idalabel
a : Optional[int] = {v: k for k, v in idalabel.items()}
a : Tuple = CvtConfig(num_labels=A_ , idalabel=A_ , labelaid=A_ )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' , 1 )[-1][4:6] == "13":
a : int = [1, 2, 1_0]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' , 1 )[-1][4:6] == "21":
a : List[Any] = [1, 4, 1_6]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
a : Optional[int] = [2, 2, 2_0]
a : Any = [3, 1_2, 1_6]
a : str = [1_9_2, 7_6_8, 1_0_2_4]
a : List[Any] = CvtForImageClassification(A_ )
a : Optional[int] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
a : Union[str, Any] = image_size
a : Optional[Any] = torch.load(A_ , map_location=torch.device('cpu' ) )
a : int = OrderedDict()
a : Any = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
a : Dict = list_of_state_dict + cls_token(A_ )
a : Any = list_of_state_dict + embeddings(A_ )
for cnt in range(config.depth[idx] ):
a : Dict = list_of_state_dict + attention(A_ , A_ )
a : Any = list_of_state_dict + final()
for gg in list_of_state_dict:
print(A_ )
for i in range(len(A_ ) ):
a : List[Any] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(A_ )
model.save_pretrained(A_ )
image_processor.save_pretrained(A_ )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
_UpperCamelCase : Any = argparse.ArgumentParser()
parser.add_argument(
'--cvt_model',
default='cvt-w24',
type=str,
help='Name of the cvt model you\'d like to convert.',
)
parser.add_argument(
'--image_size',
default=384,
type=int,
help='Input Image Size',
)
parser.add_argument(
'--cvt_file_name',
default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
type=str,
help='Input Image Size',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
_UpperCamelCase : int = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 186 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
a : List[str] = logging.getLogger(__name__)
def _SCREAMING_SNAKE_CASE ( _lowercase : Union[str, Any] , _lowercase : Union[str, Any] ) ->int:
'''simple docstring'''
return (preds == labels).mean()
@dataclass
class __UpperCamelCase :
lowerCamelCase : str =field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
lowerCamelCase : Optional[str] =field(
default=a__ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
lowerCamelCase : Optional[str] =field(
default=a__ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
lowerCamelCase : Optional[str] =field(
default=a__ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
@dataclass
class __UpperCamelCase :
lowerCamelCase : str =field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} )
lowerCamelCase : str =field(metadata={"""help""": """Should contain the data files for the task."""} )
lowerCamelCase : int =field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
lowerCamelCase : bool =field(
default=a__ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def _SCREAMING_SNAKE_CASE ( ) ->str:
'''simple docstring'''
a : Any = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
a, a, a : Union[str, Any] = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
" --overwrite_output_dir to overcome." )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , _lowercase )
# Set seed
set_seed(training_args.seed )
try:
a : Union[str, Any] = processors[data_args.task_name]()
a : Any = processor.get_labels()
a : Optional[Any] = len(_lowercase )
except KeyError:
raise ValueError("Task not found: %s" % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a : List[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_lowercase , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
a : Optional[int] = 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 , )
a : str = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=_lowercase , cache_dir=model_args.cache_dir , )
# Get datasets
a : str = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_lowercase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
a : Any = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_lowercase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(_lowercase : EvalPrediction ) -> Dict:
a : int = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(_lowercase , p.label_ids )}
# Data collator
a : List[Any] = DataCollatorWithPadding(_lowercase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
a : Dict = Trainer(
model=_lowercase , args=_lowercase , train_dataset=_lowercase , eval_dataset=_lowercase , compute_metrics=_lowercase , data_collator=_lowercase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
a : Dict = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
a : str = trainer.evaluate()
a : List[str] = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_master():
with open(_lowercase , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , _lowercase , _lowercase )
writer.write("%s = %s\n" % (key, value) )
results.update(_lowercase )
return results
def _SCREAMING_SNAKE_CASE ( _lowercase : Union[str, Any] ) ->Any:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 105 |
import random
def A ( a_ ,a_ ,a_ = False ) -> dict:
__UpperCamelCase : dict ={i: [] for i in range(a_ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_graph(a_ )
# if probability is lower or equal than 0, then return a graph without edges
if probability <= 0:
return graph
# for each couple of nodes, add an edge from u to v
# if the number randomly generated is greater than probability probability
for i in range(a_ ):
for j in range(i + 1 ,a_ ):
if random.random() < probability:
graph[i].append(a_ )
if not directed:
# if the graph is undirected, add an edge in from j to i, either
graph[j].append(a_ )
return graph
def A ( a_ ) -> dict:
return {
i: [j for j in range(a_ ) if i != j] for i in range(a_ )
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 71 | 0 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase_ ( A , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase_ = None
lowerCamelCase_ = BloomTokenizerFast
lowerCamelCase_ = BloomTokenizerFast
lowerCamelCase_ = True
lowerCamelCase_ = False
lowerCamelCase_ = '''tokenizer_file'''
lowerCamelCase_ = {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}
def lowerCAmelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
super().setUp()
_SCREAMING_SNAKE_CASE = BloomTokenizerFast.from_pretrained("bigscience/tokenizer" )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCAmelCase_ ( self : str , **__lowerCamelCase : Tuple ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
_SCREAMING_SNAKE_CASE = [[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]
_SCREAMING_SNAKE_CASE = tokenizer.batch_encode_plus(__lowerCamelCase )["input_ids"]
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def lowerCAmelCase_ ( self : Optional[Any] , __lowerCamelCase : int=6 ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
_SCREAMING_SNAKE_CASE = "This is a simple input"
_SCREAMING_SNAKE_CASE = ["This is a simple input 1", "This is a simple input 2"]
_SCREAMING_SNAKE_CASE = ("This is a simple input", "This is a pair")
_SCREAMING_SNAKE_CASE = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
try:
tokenizer_r.encode(__lowerCamelCase , max_length=__lowerCamelCase )
tokenizer_r.encode_plus(__lowerCamelCase , max_length=__lowerCamelCase )
tokenizer_r.batch_encode_plus(__lowerCamelCase , max_length=__lowerCamelCase )
tokenizer_r.encode(__lowerCamelCase , max_length=__lowerCamelCase )
tokenizer_r.batch_encode_plus(__lowerCamelCase , max_length=__lowerCamelCase )
except ValueError:
self.fail("Bloom Tokenizer should be able to deal with padding" )
_SCREAMING_SNAKE_CASE = None # Hotfixing padding = None
self.assertRaises(__lowerCamelCase , tokenizer_r.encode , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Simple input
self.assertRaises(__lowerCamelCase , tokenizer_r.encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Simple input
self.assertRaises(
__lowerCamelCase , tokenizer_r.batch_encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" , )
# Pair input
self.assertRaises(__lowerCamelCase , tokenizer_r.encode , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Pair input
self.assertRaises(__lowerCamelCase , tokenizer_r.encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Pair input
self.assertRaises(
__lowerCamelCase , tokenizer_r.batch_encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" , )
def lowerCAmelCase_ ( self : List[Any] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = load_dataset("xnli" , "all_languages" , split="test" , streaming=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = next(iter(__lowerCamelCase ) )["premise"] # pick up one data
_SCREAMING_SNAKE_CASE = list(sample_data.values() )
_SCREAMING_SNAKE_CASE = list(map(tokenizer.encode , __lowerCamelCase ) )
_SCREAMING_SNAKE_CASE = [tokenizer.decode(__lowerCamelCase , clean_up_tokenization_spaces=__lowerCamelCase ) for x in output_tokens]
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def lowerCAmelCase_ ( self : str ):
"""simple docstring"""
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 )
| 370 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
'tokenization_ctrl': ['CTRLTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
'CTRLForSequenceClassification',
'CTRLLMHeadModel',
'CTRLModel',
'CTRLPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFCTRLForSequenceClassification',
'TFCTRLLMHeadModel',
'TFCTRLModel',
'TFCTRLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 111 | 0 |
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE = 3 , _SCREAMING_SNAKE_CASE = 7 , _SCREAMING_SNAKE_CASE = 1_000_000 ):
"""simple docstring"""
UpperCamelCase = 0
UpperCamelCase = 1
for current_denominator in range(1 , limit + 1 ):
UpperCamelCase = current_denominator * numerator // denominator
if current_denominator % denominator == 0:
current_numerator -= 1
if current_numerator * max_denominator > current_denominator * max_numerator:
UpperCamelCase = current_numerator
UpperCamelCase = current_denominator
return max_numerator
if __name__ == "__main__":
print(solution(numerator=3, denominator=7, limit=1_000_000))
| 153 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class _lowerCamelCase ( _lowercase ):
def __init__(self , *__a , **__a ) -> None:
warnings.warn(
"The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use PerceiverImageProcessor instead." , __a , )
super().__init__(*__a , **__a )
| 153 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCAmelCase__ : int | float | str ) ->tuple[int, int]:
try:
A__ : Dict = float(UpperCAmelCase__ )
except ValueError:
raise ValueError("""Please enter a valid number""" )
A__ : int = decimal - int(UpperCAmelCase__ )
if fractional_part == 0:
return int(UpperCAmelCase__ ), 1
else:
A__ : int = len(str(UpperCAmelCase__ ).split(""".""" )[1] )
A__ : Optional[Any] = int(decimal * (1_0**number_of_frac_digits) )
A__ : Any = 1_0**number_of_frac_digits
A__ : Union[str, Any] = denominator, numerator
while True:
A__ : List[str] = dividend % divisor
if remainder == 0:
break
A__ : Tuple = divisor, remainder
A__ : Optional[int] = numerator / divisor, denominator / divisor
return int(UpperCAmelCase__ ), int(UpperCAmelCase__ )
if __name__ == "__main__":
print(F'{decimal_to_fraction(2) = }')
print(F'{decimal_to_fraction(89.0) = }')
print(F'{decimal_to_fraction("67") = }')
print(F'{decimal_to_fraction("45.0") = }')
print(F'{decimal_to_fraction(1.5) = }')
print(F'{decimal_to_fraction("6.25") = }')
print(F'{decimal_to_fraction("78td") = }')
| 356 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A_ = logging.get_logger(__name__)
A_ = {
'''microsoft/table-transformer-detection''': (
'''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
),
}
class __SCREAMING_SNAKE_CASE ( UpperCamelCase ):
snake_case_ = 'table-transformer'
snake_case_ = ['past_key_values']
snake_case_ = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Dict , snake_case : int=True , snake_case : Dict=None , snake_case : Union[str, Any]=3 , snake_case : Dict=100 , snake_case : Tuple=6 , snake_case : Optional[int]=2048 , snake_case : int=8 , snake_case : Dict=6 , snake_case : Any=2048 , snake_case : str=8 , snake_case : Union[str, Any]=0.0 , snake_case : List[str]=0.0 , snake_case : List[str]=True , snake_case : Any="relu" , snake_case : str=256 , snake_case : int=0.1 , snake_case : Dict=0.0 , snake_case : str=0.0 , snake_case : Union[str, Any]=0.02 , snake_case : Union[str, Any]=1.0 , snake_case : Optional[Any]=False , snake_case : int="sine" , snake_case : Optional[Any]="resnet50" , snake_case : Optional[int]=True , snake_case : Any=False , snake_case : int=1 , snake_case : Tuple=5 , snake_case : Optional[int]=2 , snake_case : Tuple=1 , snake_case : Optional[Any]=1 , snake_case : Optional[Any]=5 , snake_case : Dict=2 , snake_case : Any=0.1 , **snake_case : Any , ):
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
A__ : Optional[Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(snake_case , snake_case ):
A__ : Optional[int] = backbone_config.get("""model_type""" )
A__ : Optional[int] = CONFIG_MAPPING[backbone_model_type]
A__ : List[str] = config_class.from_dict(snake_case )
# set timm attributes to None
A__ , A__ , A__ : str = None, None, None
A__ : Tuple = use_timm_backbone
A__ : str = backbone_config
A__ : str = num_channels
A__ : List[Any] = num_queries
A__ : Optional[Any] = d_model
A__ : Tuple = encoder_ffn_dim
A__ : Union[str, Any] = encoder_layers
A__ : List[Any] = encoder_attention_heads
A__ : Optional[int] = decoder_ffn_dim
A__ : Any = decoder_layers
A__ : int = decoder_attention_heads
A__ : Any = dropout
A__ : Dict = attention_dropout
A__ : Dict = activation_dropout
A__ : Tuple = activation_function
A__ : List[str] = init_std
A__ : List[str] = init_xavier_std
A__ : Any = encoder_layerdrop
A__ : Optional[Any] = decoder_layerdrop
A__ : Union[str, Any] = encoder_layers
A__ : Dict = auxiliary_loss
A__ : List[Any] = position_embedding_type
A__ : Optional[Any] = backbone
A__ : str = use_pretrained_backbone
A__ : Union[str, Any] = dilation
# Hungarian matcher
A__ : Tuple = class_cost
A__ : Optional[Any] = bbox_cost
A__ : Dict = giou_cost
# Loss coefficients
A__ : Any = mask_loss_coefficient
A__ : str = dice_loss_coefficient
A__ : str = bbox_loss_coefficient
A__ : Union[str, Any] = giou_loss_coefficient
A__ : List[str] = eos_coefficient
super().__init__(is_encoder_decoder=snake_case , **snake_case )
@property
def _UpperCamelCase ( self : List[str] ):
'''simple docstring'''
return self.encoder_attention_heads
@property
def _UpperCamelCase ( self : Dict ):
'''simple docstring'''
return self.d_model
class __SCREAMING_SNAKE_CASE ( UpperCamelCase ):
snake_case_ = version.parse('1.11' )
@property
def _UpperCamelCase ( self : Any ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _UpperCamelCase ( self : Optional[int] ):
'''simple docstring'''
return 1e-5
@property
def _UpperCamelCase ( self : List[str] ):
'''simple docstring'''
return 12
| 296 | 0 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Any = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
__UpperCamelCase :Tuple = parser.add_subparsers(help='''diffusers-cli command helpers''' )
# Register commands
EnvironmentCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
# Let's go
__UpperCamelCase :Tuple = parser.parse_args()
if not hasattr(_SCREAMING_SNAKE_CASE , '''func''' ):
parser.print_help()
exit(1 )
# Run
__UpperCamelCase :List[str] = args.func(_SCREAMING_SNAKE_CASE )
service.run()
if __name__ == "__main__":
main()
| 43 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__A : int = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : int = 1_6000 ):
'''simple docstring'''
_UpperCAmelCase = int(round(sample_rate * max_length ) )
if len(_SCREAMING_SNAKE_CASE ) <= sample_length:
return wav
_UpperCAmelCase = randint(0 , len(_SCREAMING_SNAKE_CASE ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(default=lowerCAmelCase , metadata={"""help""": """Name of a dataset from the datasets package"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the training audio paths and labels."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the validation audio paths and labels."""})
UpperCamelCase__ = field(
default="""train""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
UpperCamelCase__ = field(
default="""validation""" , metadata={
"""help""": (
"""The name of the training data set split to use (via the datasets library). Defaults to 'validation'"""
)
} , )
UpperCamelCase__ = field(
default="""audio""" , metadata={"""help""": """The name of the dataset column containing the audio data. Defaults to 'audio'"""} , )
UpperCamelCase__ = field(
default="""label""" , metadata={"""help""": """The name of the dataset column containing the labels. Defaults to 'label'"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
UpperCamelCase__ = field(
default=20 , metadata={"""help""": """Audio clips will be randomly cut to this length during training if the value is set."""} , )
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = field(
default="""facebook/wav2vec2-base""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from the Hub"""})
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=lowerCAmelCase , metadata={"""help""": """Name or path of preprocessor config."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature encoder layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to generate an attention mask in the feature extractor."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""})
UpperCamelCase__ = field(
default=lowerCAmelCase , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def lowercase__ ( self : Optional[Any] )->int:
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''will be removed in a future version. Use `--freeze_feature_encoder`'''
'''instead. Setting `freeze_feature_encoder==True`.''' , __UpperCamelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''should not be used in combination with `--freeze_feature_encoder`.'''
'''Only make use of `--freeze_feature_encoder`.''' )
def lowercase ( ):
'''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_audio_classification''' , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 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(_SCREAMING_SNAKE_CASE )
transformers.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} '
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
_UpperCAmelCase = 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 train from scratch.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset and prepare it for the audio classification task.
_UpperCAmelCase = DatasetDict()
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--audio_column_name` to the correct audio column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--label_column_name` to the correct text column - one of '''
f'{", ".join(raw_datasets["train"].column_names )}.' )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
_UpperCAmelCase = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
_UpperCAmelCase = feature_extractor.model_input_names[0]
def train_transforms(_SCREAMING_SNAKE_CASE : Tuple ):
_UpperCAmelCase = []
for audio in batch[data_args.audio_column_name]:
_UpperCAmelCase = random_subsample(
audio['''array'''] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(_SCREAMING_SNAKE_CASE : Optional[int] ):
_UpperCAmelCase = [audio['''array'''] for audio in batch[data_args.audio_column_name]]
_UpperCAmelCase = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
_UpperCAmelCase = {model_input_name: inputs.get(_SCREAMING_SNAKE_CASE )}
_UpperCAmelCase = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_UpperCAmelCase = raw_datasets['''train'''].features[data_args.label_column_name].names
_UpperCAmelCase , _UpperCAmelCase = {}, {}
for i, label in enumerate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = str(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = label
# Load the accuracy metric from the datasets package
_UpperCAmelCase = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(_SCREAMING_SNAKE_CASE : List[str] ):
_UpperCAmelCase = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=_SCREAMING_SNAKE_CASE , references=eval_pred.label_ids )
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(_SCREAMING_SNAKE_CASE ) , labelaid=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , finetuning_task='''audio-classification''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=_SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
_UpperCAmelCase = (
raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_UpperCAmelCase = (
raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(_SCREAMING_SNAKE_CASE , output_all_columns=_SCREAMING_SNAKE_CASE )
# Initialize our trainer
_UpperCAmelCase = Trainer(
model=_SCREAMING_SNAKE_CASE , args=_SCREAMING_SNAKE_CASE , train_dataset=raw_datasets['''train'''] if training_args.do_train else None , eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None , compute_metrics=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , )
# 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=_SCREAMING_SNAKE_CASE )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_UpperCAmelCase = trainer.evaluate()
trainer.log_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
trainer.save_metrics('''eval''' , _SCREAMING_SNAKE_CASE )
# Write model card and (optionally) push to hub
_UpperCAmelCase = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''audio-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''audio-classification'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_SCREAMING_SNAKE_CASE )
else:
trainer.create_model_card(**_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 260 | 0 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
import pytest
import requests
from packaging import version
from datasets import config
if config.PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase=False ) -> Union[str, Any]:
"""simple docstring"""
try:
A : Dict = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
A : List[Any] = default
else:
# KEY is set, convert it to True or False.
try:
A : int = strtobool(_lowerCAmelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f'''If set, {key} must be yes or no.''' )
return _value
SCREAMING_SNAKE_CASE_:int = parse_flag_from_env("""RUN_SLOW""", default=False)
SCREAMING_SNAKE_CASE_:Optional[int] = parse_flag_from_env("""RUN_REMOTE""", default=False)
SCREAMING_SNAKE_CASE_:Optional[Any] = parse_flag_from_env("""RUN_LOCAL""", default=True)
SCREAMING_SNAKE_CASE_:Optional[Any] = parse_flag_from_env("""RUN_PACKAGED""", default=True)
# Compression
SCREAMING_SNAKE_CASE_:Union[str, Any] = pytest.mark.skipif(not config.LZ4_AVAILABLE, reason="""test requires lz4""")
SCREAMING_SNAKE_CASE_:Optional[int] = pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason="""test requires py7zr""")
SCREAMING_SNAKE_CASE_:List[Any] = pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason="""test requires zstandard""")
# Audio
SCREAMING_SNAKE_CASE_:List[Any] = pytest.mark.skipif(
# On Windows and OS X, soundfile installs sndfile
find_spec("""soundfile""") is None or version.parse(importlib_metadata.version("""soundfile""")) < version.parse("""0.12.0"""),
reason="""test requires sndfile>=0.12.1: 'pip install \"soundfile>=0.12.1\"'; """,
)
# Beam
SCREAMING_SNAKE_CASE_:List[Any] = pytest.mark.skipif(
not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse("""0.3.2"""),
reason="""test requires apache-beam and a compatible dill version""",
)
# Dill-cloudpickle compatibility
SCREAMING_SNAKE_CASE_:List[Any] = pytest.mark.skipif(
config.DILL_VERSION <= version.parse("""0.3.2"""),
reason="""test requires dill>0.3.2 for cloudpickle compatibility""",
)
# Windows
SCREAMING_SNAKE_CASE_:Dict = pytest.mark.skipif(
sys.platform == """win32""",
reason="""test should not be run on Windows""",
)
def __UpperCamelCase ( _lowerCAmelCase ) -> str:
"""simple docstring"""
try:
import faiss # noqa
except ImportError:
A : List[str] = unittest.skip("""test requires faiss""" )(_lowerCAmelCase )
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> Tuple:
"""simple docstring"""
try:
import regex # noqa
except ImportError:
A : str = unittest.skip("""test requires regex""" )(_lowerCAmelCase )
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> List[str]:
"""simple docstring"""
try:
import elasticsearch # noqa
except ImportError:
A : int = unittest.skip("""test requires elasticsearch""" )(_lowerCAmelCase )
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> Optional[int]:
"""simple docstring"""
try:
import sqlalchemy # noqa
except ImportError:
A : str = unittest.skip("""test requires sqlalchemy""" )(_lowerCAmelCase )
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> List[Any]:
"""simple docstring"""
if not config.TORCH_AVAILABLE:
A : Union[str, Any] = unittest.skip("""test requires PyTorch""" )(_lowerCAmelCase )
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> Any:
"""simple docstring"""
if not config.TF_AVAILABLE:
A : str = unittest.skip("""test requires TensorFlow""" )(_lowerCAmelCase )
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> List[str]:
"""simple docstring"""
if not config.JAX_AVAILABLE:
A : Optional[int] = unittest.skip("""test requires JAX""" )(_lowerCAmelCase )
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> int:
"""simple docstring"""
if not config.PIL_AVAILABLE:
A : Union[str, Any] = unittest.skip("""test requires Pillow""" )(_lowerCAmelCase )
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> Any:
"""simple docstring"""
try:
import transformers # noqa F401
except ImportError:
return unittest.skip("""test requires transformers""" )(_lowerCAmelCase )
else:
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> List[str]:
"""simple docstring"""
try:
import tiktoken # noqa F401
except ImportError:
return unittest.skip("""test requires tiktoken""" )(_lowerCAmelCase )
else:
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> int:
"""simple docstring"""
try:
import spacy # noqa F401
except ImportError:
return unittest.skip("""test requires spacy""" )(_lowerCAmelCase )
else:
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> List[Any]:
"""simple docstring"""
def _require_spacy_model(_lowerCAmelCase ):
try:
import spacy # noqa F401
spacy.load(_lowerCAmelCase )
except ImportError:
return unittest.skip("""test requires spacy""" )(_lowerCAmelCase )
except OSError:
return unittest.skip("""test requires spacy model '{}'""".format(_lowerCAmelCase ) )(_lowerCAmelCase )
else:
return test_case
return _require_spacy_model
def __UpperCamelCase ( _lowerCAmelCase ) -> str:
"""simple docstring"""
try:
import pyspark # noqa F401
except ImportError:
return unittest.skip("""test requires pyspark""" )(_lowerCAmelCase )
else:
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> Dict:
"""simple docstring"""
try:
import joblibspark # noqa F401
except ImportError:
return unittest.skip("""test requires joblibspark""" )(_lowerCAmelCase )
else:
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
if not _run_slow_tests or _run_slow_tests == 0:
A : Tuple = unittest.skip("""test is slow""" )(_lowerCAmelCase )
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> List[Any]:
"""simple docstring"""
if not _run_local_tests or _run_local_tests == 0:
A : str = unittest.skip("""test is local""" )(_lowerCAmelCase )
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> int:
"""simple docstring"""
if not _run_packaged_tests or _run_packaged_tests == 0:
A : int = unittest.skip("""test is packaged""" )(_lowerCAmelCase )
return test_case
def __UpperCamelCase ( _lowerCAmelCase ) -> List[str]:
"""simple docstring"""
if not _run_remote_tests or _run_remote_tests == 0:
A : List[str] = unittest.skip("""test requires remote""" )(_lowerCAmelCase )
return test_case
def __UpperCamelCase ( *_lowerCAmelCase ) -> Any:
"""simple docstring"""
def decorate(cls ):
for name, fn in cls.__dict__.items():
if callable(_lowerCAmelCase ) and name.startswith("""test""" ):
for decorator in decorators:
A : Any = decorator(_lowerCAmelCase )
setattr(cls , _lowerCAmelCase , _lowerCAmelCase )
return cls
return decorate
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
pass
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : List[str] = 0
__lowerCamelCase : Tuple = 1
__lowerCamelCase : List[Any] = 2
@contextmanager
def __UpperCamelCase ( _lowerCAmelCase=OfflineSimulationMode.CONNECTION_FAILS , _lowerCAmelCase=1e-16 ) -> Any:
"""simple docstring"""
A : List[Any] = requests.Session().request
def timeout_request(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase ):
# Change the url to an invalid url so that the connection hangs
A : Optional[Any] = """https://10.255.255.1"""
if kwargs.get("""timeout""" ) is None:
raise RequestWouldHangIndefinitelyError(
f'''Tried a call to {url} in offline mode with no timeout set. Please set a timeout.''' )
A : List[str] = timeout
try:
return online_request(_lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase )
except Exception as e:
# The following changes in the error are just here to make the offline timeout error prettier
A : int = url
A : Union[str, Any] = e.args[0]
A : Union[str, Any] = (max_retry_error.args[0].replace("""10.255.255.1""" , f'''OfflineMock[{url}]''' ),)
A : List[Any] = (max_retry_error,)
raise
def raise_connection_error(_lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase ):
raise requests.ConnectionError("""Offline mode is enabled.""" , request=_lowerCAmelCase )
if mode is OfflineSimulationMode.CONNECTION_FAILS:
with patch("""requests.Session.send""" , _lowerCAmelCase ):
yield
elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
# inspired from https://stackoverflow.com/a/904609
with patch("""requests.Session.request""" , _lowerCAmelCase ):
yield
elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
with patch("""datasets.config.HF_DATASETS_OFFLINE""" , _lowerCAmelCase ):
yield
else:
raise ValueError("""Please use a value from the OfflineSimulationMode enum.""" )
@contextmanager
def __UpperCamelCase ( *_lowerCAmelCase , **_lowerCAmelCase ) -> List[Any]:
"""simple docstring"""
A : str = str(Path().resolve() )
with tempfile.TemporaryDirectory(*_lowerCAmelCase , **_lowerCAmelCase ) as tmp_dir:
try:
os.chdir(_lowerCAmelCase )
yield
finally:
os.chdir(_lowerCAmelCase )
@contextmanager
def __UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
import gc
gc.collect()
A : Any = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."
@contextmanager
def __UpperCamelCase ( ) -> str:
"""simple docstring"""
import gc
gc.collect()
A : Optional[int] = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
return deepcopy(_lowerCAmelCase ).integers(0 , 100 , 10 ).tolist() == deepcopy(_lowerCAmelCase ).integers(0 , 100 , 10 ).tolist()
def __UpperCamelCase ( _lowerCAmelCase ) -> int:
"""simple docstring"""
import decorator
from requests.exceptions import HTTPError
def _wrapper(_lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase ):
try:
return func(*_lowerCAmelCase , **_lowerCAmelCase )
except HTTPError as err:
if str(_lowerCAmelCase ).startswith("""500""" ) or str(_lowerCAmelCase ).startswith("""502""" ):
pytest.xfail(str(_lowerCAmelCase ) )
raise err
return decorator.decorator(_wrapper , _lowerCAmelCase )
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__ ):
A : List[str] = returncode
A : Dict = stdout
A : int = stderr
async def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Tuple:
"""simple docstring"""
while True:
A : Optional[int] = await stream.readline()
if line:
callback(_lowerCAmelCase )
else:
break
async def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=False , _lowerCAmelCase=False ) -> _RunOutput:
"""simple docstring"""
if echo:
print("""\nRunning: """ , """ """.join(_lowerCAmelCase ) )
A : Optional[int] = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_lowerCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_lowerCAmelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
A : List[str] = []
A : Any = []
def tee(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase="" ):
A : Dict = line.decode("""utf-8""" ).rstrip()
sink.append(_lowerCAmelCase )
if not quiet:
print(_lowerCAmelCase , _lowerCAmelCase , file=_lowerCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
_read_stream(p.stdout , lambda _lowerCAmelCase : tee(_lowerCAmelCase , _lowerCAmelCase , sys.stdout , label="""stdout:""" ) ),
_read_stream(p.stderr , lambda _lowerCAmelCase : tee(_lowerCAmelCase , _lowerCAmelCase , sys.stderr , label="""stderr:""" ) ),
] , timeout=_lowerCAmelCase , )
return _RunOutput(await p.wait() , _lowerCAmelCase , _lowerCAmelCase )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=180 , _lowerCAmelCase=False , _lowerCAmelCase=True ) -> _RunOutput:
"""simple docstring"""
A : Union[str, Any] = asyncio.get_event_loop()
A : int = loop.run_until_complete(
_stream_subprocess(_lowerCAmelCase , env=_lowerCAmelCase , stdin=_lowerCAmelCase , timeout=_lowerCAmelCase , quiet=_lowerCAmelCase , echo=_lowerCAmelCase ) )
A : Tuple = """ """.join(_lowerCAmelCase )
if result.returncode > 0:
A : Optional[Any] = """\n""".join(result.stderr )
raise RuntimeError(
f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
f'''The combined stderr from workers follows:\n{stderr}''' )
# check that the subprocess actually did run and produced some output, should the test rely on
# the remote side to do the testing
if not result.stdout and not result.stderr:
raise RuntimeError(f'''\'{cmd_str}\' produced no output.''' )
return result
def __UpperCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
A : Union[str, Any] = os.environ.get("""PYTEST_XDIST_WORKER""" , """gw0""" )
A : str = re.sub(R"""^gw""" , """""" , _lowerCAmelCase , 0 , re.M )
return int(_lowerCAmelCase )
def __UpperCamelCase ( ) -> int:
"""simple docstring"""
A : Dict = 2_9500
A : Dict = pytest_xdist_worker_id()
return port + uniq_delta
| 115 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __UpperCamelCase ( ) -> Dict:
"""simple docstring"""
A : str = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
A : int = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(_lowerCAmelCase )
# Let's go
A : str = parser.parse_args()
if not hasattr(_lowerCAmelCase , """func""" ):
parser.print_help()
exit(1 )
# Run
A : Any = args.func(_lowerCAmelCase )
service.run()
if __name__ == "__main__":
main()
| 115 | 1 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> np.ndarray:
lowercase__ : Optional[int] = int(np.ceil((x_end - xa) / step_size ) )
lowercase__ : Optional[int] = np.zeros((n + 1,) )
lowercase__ : List[Any] = ya
lowercase__ : Optional[Any] = xa
for k in range(__lowerCamelCase ):
lowercase__ : str = y[k] + step_size * ode_func(__lowerCamelCase , y[k] )
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 16 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowercase ( A__ ):
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = False , UpperCamelCase_ = False , UpperCamelCase_ = None , UpperCamelCase_ = None , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(
features=UpperCamelCase_ , cache_dir=UpperCamelCase_ , keep_in_memory=UpperCamelCase_ , streaming=UpperCamelCase_ , num_proc=UpperCamelCase_ , **UpperCamelCase_ , )
UpperCamelCase__ :Any = Generator(
cache_dir=UpperCamelCase_ , features=UpperCamelCase_ , generator=UpperCamelCase_ , gen_kwargs=UpperCamelCase_ , **UpperCamelCase_ , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
if self.streaming:
UpperCamelCase__ :Optional[Any] = self.builder.as_streaming_dataset(split='''train''' )
# Build regular (map-style) dataset
else:
UpperCamelCase__ :Optional[int] = None
UpperCamelCase__ :int = None
UpperCamelCase__ :Any = None
UpperCamelCase__ :Any = None
self.builder.download_and_prepare(
download_config=UpperCamelCase_ , download_mode=UpperCamelCase_ , verification_mode=UpperCamelCase_ , base_path=UpperCamelCase_ , num_proc=self.num_proc , )
UpperCamelCase__ :List[Any] = self.builder.as_dataset(
split='''train''' , verification_mode=UpperCamelCase_ , in_memory=self.keep_in_memory )
return dataset | 97 | 0 |
'''simple docstring'''
from math import factorial
def lowerCamelCase__ ( A : int = 20 ):
'''simple docstring'''
UpperCAmelCase = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCAmelCase = n // 2
return int(factorial(A ) / (factorial(A ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(20))
else:
try:
_lowercase : List[str] = int(sys.argv[1])
print(solution(n))
except ValueError:
print("""Invalid entry - please enter a number.""")
| 91 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : List[Any] = ["image_processor", "tokenizer"]
__magic_name__ : Tuple = "ViTImageProcessor"
__magic_name__ : int = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__( self : List[str] , lowerCAmelCase : Tuple=None , lowerCAmelCase : List[str]=None , **lowerCAmelCase : Optional[int] )-> Tuple:
"""simple docstring"""
UpperCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , lowerCAmelCase , )
UpperCAmelCase = kwargs.pop('''feature_extractor''' )
UpperCAmelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(lowerCAmelCase , lowerCAmelCase )
def __call__( self : int , lowerCAmelCase : Dict=None , lowerCAmelCase : Optional[int]=None , lowerCAmelCase : Union[str, Any]=None , lowerCAmelCase : int=None , **lowerCAmelCase : Tuple )-> Optional[int]:
"""simple docstring"""
if text is None and visual_prompt is None and images is None:
raise ValueError('''You have to specify either text, visual prompt or images.''' )
if text is not None and visual_prompt is not None:
raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''' )
if text is not None:
UpperCAmelCase = self.tokenizer(lowerCAmelCase , return_tensors=lowerCAmelCase , **lowerCAmelCase )
if visual_prompt is not None:
UpperCAmelCase = self.image_processor(lowerCAmelCase , return_tensors=lowerCAmelCase , **lowerCAmelCase )
if images is not None:
UpperCAmelCase = self.image_processor(lowerCAmelCase , return_tensors=lowerCAmelCase , **lowerCAmelCase )
if visual_prompt is not None and images is not None:
UpperCAmelCase = {
'''pixel_values''': image_features.pixel_values,
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
UpperCAmelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
UpperCAmelCase = {
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**lowerCAmelCase ) , tensor_type=lowerCAmelCase )
def a__( self : Optional[int] , *lowerCAmelCase : List[str] , **lowerCAmelCase : Dict )-> Tuple:
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase , **lowerCAmelCase )
def a__( self : List[Any] , *lowerCAmelCase : str , **lowerCAmelCase : List[Any] )-> Optional[Any]:
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase , **lowerCAmelCase )
@property
def a__( self : Any )-> Optional[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
@property
def a__( self : str )-> List[Any]:
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , lowerCAmelCase , )
return self.image_processor
| 91 | 1 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __lowerCamelCase : str = "" ):
snake_case : Dict = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
snake_case : Union[str, Any] = BeautifulSoup(requests.get(__lowerCamelCase ).text , "html.parser" )
snake_case : str = soup.find_all("td" , attrs="titleColumn" )
snake_case : Union[str, Any] = soup.find_all("td" , class_="ratingColumn imdbRating" )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(__lowerCamelCase , __lowerCamelCase )
}
def UpperCamelCase ( __lowerCamelCase : str = "IMDb_Top_250_Movies.csv" ):
snake_case : List[str] = get_imdb_top_aaa_movies()
with open(__lowerCamelCase , "w" , newline="" ) as out_file:
snake_case : Union[str, Any] = csv.writer(__lowerCamelCase )
writer.writerow(["Movie title", "IMDb rating"] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 59 |
from __future__ import annotations
from collections.abc import Iterator
class __lowerCAmelCase :
def __init__( self :Optional[Any] , __magic_name__ :int ):
'''simple docstring'''
a = value
a = None
a = None
class __lowerCAmelCase :
def __init__( self :str , __magic_name__ :Node ):
'''simple docstring'''
a = tree
def lowerCamelCase__ ( self :str , __magic_name__ :Node | None ):
'''simple docstring'''
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self :Tuple ):
'''simple docstring'''
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 228 | 0 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_decord,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class lowerCamelCase ( unittest.TestCase ):
UpperCAmelCase__ : List[str] = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def UpperCAmelCase(self : Optional[Any] , _A : Tuple , _A : Optional[Any] , _A : Tuple ) -> Any:
snake_case = hf_hub_download(
repo_id="nateraw/video-demo" , filename="archery.mp4" , repo_type="dataset" )
snake_case = VideoClassificationPipeline(model=_A , image_processor=_A , top_k=2 )
snake_case = [
example_video_filepath,
"https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
]
return video_classifier, examples
def UpperCAmelCase(self : List[Any] , _A : Optional[Any] , _A : Tuple ) -> str:
for example in examples:
snake_case = video_classifier(_A )
self.assertEqual(
_A , [
{"score": ANY(_A ), "label": ANY(_A )},
{"score": ANY(_A ), "label": ANY(_A )},
] , )
@require_torch
def UpperCAmelCase(self : Any ) -> Dict:
snake_case = "hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
snake_case = VideoMAEFeatureExtractor(
size={"shortest_edge": 1_0} , crop_size={"height": 1_0, "width": 1_0} )
snake_case = pipeline(
"video-classification" , model=_A , feature_extractor=_A , frame_sampling_rate=4 )
snake_case = hf_hub_download(repo_id="nateraw/video-demo" , filename="archery.mp4" , repo_type="dataset" )
snake_case = video_classifier(_A , top_k=2 )
self.assertEqual(
nested_simplify(_A , decimals=4 ) , [{"score": 0.51_99, "label": "LABEL_0"}, {"score": 0.48_01, "label": "LABEL_1"}] , )
snake_case = video_classifier(
[
video_file_path,
video_file_path,
] , top_k=2 , )
self.assertEqual(
nested_simplify(_A , decimals=4 ) , [
[{"score": 0.51_99, "label": "LABEL_0"}, {"score": 0.48_01, "label": "LABEL_1"}],
[{"score": 0.51_99, "label": "LABEL_0"}, {"score": 0.48_01, "label": "LABEL_1"}],
] , )
@require_tf
def UpperCAmelCase(self : Union[str, Any] ) -> int:
pass
| 137 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 137 | 1 |
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : int ):
lowerCamelCase_ = [[0 for _ in range(UpperCAmelCase_ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowerCamelCase_ = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelCase_ ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
a_ : Optional[int] = int(input("""Enter a number: """).strip())
print(partition(n))
except ValueError:
print("""Please enter a number.""")
else:
try:
a_ : str = int(sys.argv[1])
print(partition(n))
except ValueError:
print("""Please pass a number.""")
| 55 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
A_ : Tuple = logging.get_logger(__name__)
class A_ ( _a ):
'''simple docstring'''
a__ = "linear"
a__ = "cosine"
a__ = "cosine_with_restarts"
a__ = "polynomial"
a__ = "constant"
a__ = "constant_with_warmup"
a__ = "piecewise_constant"
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = -1 ) -> Tuple:
'''simple docstring'''
return LambdaLR(SCREAMING_SNAKE_CASE , lambda SCREAMING_SNAKE_CASE : 1 , last_epoch=SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = -1 ) -> Union[str, Any]:
'''simple docstring'''
def lr_lambda(SCREAMING_SNAKE_CASE ):
if current_step < num_warmup_steps:
return float(SCREAMING_SNAKE_CASE ) / float(max(1.0 , SCREAMING_SNAKE_CASE ) )
return 1.0
return LambdaLR(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = -1 ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase = {}
__UpperCAmelCase = step_rules.split(''',''' )
for rule_str in rule_list[:-1]:
__UpperCAmelCase , __UpperCAmelCase = rule_str.split(''':''' )
__UpperCAmelCase = int(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = float(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = value
__UpperCAmelCase = float(rule_list[-1] )
def create_rules_function(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
def rule_func(SCREAMING_SNAKE_CASE ) -> float:
__UpperCAmelCase = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(SCREAMING_SNAKE_CASE ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
__UpperCAmelCase = create_rules_function(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return LambdaLR(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=-1 ) -> Optional[Any]:
'''simple docstring'''
def lr_lambda(SCREAMING_SNAKE_CASE ):
if current_step < num_warmup_steps:
return float(SCREAMING_SNAKE_CASE ) / float(max(1 , SCREAMING_SNAKE_CASE ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0.5 , SCREAMING_SNAKE_CASE = -1 ) -> int:
'''simple docstring'''
def lr_lambda(SCREAMING_SNAKE_CASE ):
if current_step < num_warmup_steps:
return float(SCREAMING_SNAKE_CASE ) / float(max(1 , SCREAMING_SNAKE_CASE ) )
__UpperCAmelCase = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(SCREAMING_SNAKE_CASE ) * 2.0 * progress )) )
return LambdaLR(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = -1 ) -> Dict:
'''simple docstring'''
def lr_lambda(SCREAMING_SNAKE_CASE ):
if current_step < num_warmup_steps:
return float(SCREAMING_SNAKE_CASE ) / float(max(1 , SCREAMING_SNAKE_CASE ) )
__UpperCAmelCase = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(SCREAMING_SNAKE_CASE ) * progress) % 1.0) )) )
return LambdaLR(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=1e-7 , SCREAMING_SNAKE_CASE=1.0 , SCREAMING_SNAKE_CASE=-1 ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase = optimizer.defaults['''lr''']
if not (lr_init > lr_end):
raise ValueError(f'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})''' )
def lr_lambda(SCREAMING_SNAKE_CASE ):
if current_step < num_warmup_steps:
return float(SCREAMING_SNAKE_CASE ) / float(max(1 , SCREAMING_SNAKE_CASE ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
__UpperCAmelCase = lr_init - lr_end
__UpperCAmelCase = num_training_steps - num_warmup_steps
__UpperCAmelCase = 1 - (current_step - num_warmup_steps) / decay_steps
__UpperCAmelCase = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1.0 , SCREAMING_SNAKE_CASE = -1 , ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase = SchedulerType(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(SCREAMING_SNAKE_CASE , step_rules=SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(f'''{name} requires `num_warmup_steps`, please provide that argument.''' )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(SCREAMING_SNAKE_CASE , num_warmup_steps=SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(f'''{name} requires `num_training_steps`, please provide that argument.''' )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
SCREAMING_SNAKE_CASE , num_warmup_steps=SCREAMING_SNAKE_CASE , num_training_steps=SCREAMING_SNAKE_CASE , num_cycles=SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
SCREAMING_SNAKE_CASE , num_warmup_steps=SCREAMING_SNAKE_CASE , num_training_steps=SCREAMING_SNAKE_CASE , power=SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE , )
return schedule_func(
SCREAMING_SNAKE_CASE , num_warmup_steps=SCREAMING_SNAKE_CASE , num_training_steps=SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE )
| 333 | 0 |
'''simple docstring'''
from __future__ import annotations
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : int , _lowerCAmelCase : list[list[int]] ):
A = TypeError(
"""Matrices must be formed from a list of zero or more lists containing at """
"""least one and the same number of values, each of which must be of type """
"""int or float.""" )
if len(_lowerCAmelCase ) != 0:
A = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(_lowerCAmelCase ) != cols:
raise error
for value in row:
if not isinstance(_lowerCAmelCase , (int, float) ):
raise error
A = rows
else:
A = []
def A (self : Optional[Any] ):
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def A (self : List[Any] ):
return len(self.rows )
@property
def A (self : Union[str, Any] ):
return len(self.rows[0] )
@property
def A (self : List[str] ):
return (self.num_rows, self.num_columns)
@property
def A (self : Union[str, Any] ):
return self.order[0] == self.order[1]
def A (self : str ):
A = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(_lowerCAmelCase )
def A (self : str ):
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def A (self : Any ):
return bool(self.determinant() )
def A (self : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : int ):
A = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(_lowerCAmelCase ).determinant()
def A (self : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : int ):
if (row + column) % 2 == 0:
return self.get_minor(_lowerCAmelCase , _lowerCAmelCase )
return -1 * self.get_minor(_lowerCAmelCase , _lowerCAmelCase )
def A (self : Any ):
return Matrix(
[
[self.get_minor(_lowerCAmelCase , _lowerCAmelCase ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def A (self : Optional[int] ):
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def A (self : int ):
A = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(_lowerCAmelCase )
def A (self : Any ):
A = self.determinant()
if not determinant:
raise TypeError("""Only matrices with a non-zero determinant have an inverse""" )
return self.adjugate() * (1 / determinant)
def __repr__(self : str ):
return str(self.rows )
def __str__(self : List[Any] ):
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
"""[""" + """. """.join([str(_lowerCAmelCase ) for value in row] ) + """.]"""
for row in self.rows
] )
+ "]"
)
def A (self : Union[str, Any] , _lowerCAmelCase : list[int] , _lowerCAmelCase : int | None = None ):
A = TypeError("""Row must be a list containing all ints and/or floats""" )
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise type_error
for value in row:
if not isinstance(_lowerCAmelCase , (int, float) ):
raise type_error
if len(_lowerCAmelCase ) != self.num_columns:
raise ValueError(
"""Row must be equal in length to the other rows in the matrix""" )
if position is None:
self.rows.append(_lowerCAmelCase )
else:
A = self.rows[0:position] + [row] + self.rows[position:]
def A (self : Optional[Any] , _lowerCAmelCase : list[int] , _lowerCAmelCase : int | None = None ):
A = TypeError(
"""Column must be a list containing all ints and/or floats""" )
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise type_error
for value in column:
if not isinstance(_lowerCAmelCase , (int, float) ):
raise type_error
if len(_lowerCAmelCase ) != self.num_rows:
raise ValueError(
"""Column must be equal in length to the other columns in the matrix""" )
if position is None:
A = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
A = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__(self : List[Any] , _lowerCAmelCase : object ):
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return NotImplemented
return self.rows == other.rows
def __ne__(self : Tuple , _lowerCAmelCase : object ):
return not self == other
def __neg__(self : Any ):
return self * -1
def __add__(self : Optional[Any] , _lowerCAmelCase : Matrix ):
if self.order != other.order:
raise ValueError("""Addition requires matrices of the same order""" )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__(self : List[str] , _lowerCAmelCase : Matrix ):
if self.order != other.order:
raise ValueError("""Subtraction requires matrices of the same order""" )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__(self : Optional[int] , _lowerCAmelCase : Matrix | int | float ):
if isinstance(_lowerCAmelCase , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
if self.num_columns != other.num_rows:
raise ValueError(
"""The number of columns in the first matrix must """
"""be equal to the number of rows in the second""" )
return Matrix(
[
[Matrix.dot_product(_lowerCAmelCase , _lowerCAmelCase ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
"""A Matrix can only be multiplied by an int, float, or another matrix""" )
def __pow__(self : Union[str, Any] , _lowerCAmelCase : int ):
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""A Matrix can only be raised to the power of an int""" )
if not self.is_square:
raise ValueError("""Only square matrices can be raised to a power""" )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
"""Only invertable matrices can be raised to a negative power""" )
A = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def A (cls : List[str] , _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] ):
return sum(row[i] * column[i] for i in range(len(_lowerCAmelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : Tuple , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : Dict ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Optional[int] , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : Any ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : List[str] , *_lowerCAmelCase : Dict , **_lowerCAmelCase : str ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class __UpperCAmelCase ( metaclass=A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : List[str] , *_lowerCAmelCase : Dict , **_lowerCAmelCase : int ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : List[Any] , *_lowerCAmelCase : str , **_lowerCAmelCase : str ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : List[str] , *_lowerCAmelCase : Optional[int] , **_lowerCAmelCase : List[Any] ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class __UpperCAmelCase ( metaclass=A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : Union[str, Any] , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : int ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Any , *_lowerCAmelCase : str , **_lowerCAmelCase : Union[str, Any] ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : List[Any] , *_lowerCAmelCase : Dict , **_lowerCAmelCase : Union[str, Any] ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class __UpperCAmelCase ( metaclass=A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : List[str] , *_lowerCAmelCase : Dict , **_lowerCAmelCase : Any ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Optional[int] , *_lowerCAmelCase : Dict , **_lowerCAmelCase : Dict ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Union[str, Any] , *_lowerCAmelCase : str , **_lowerCAmelCase : List[str] ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class __UpperCAmelCase ( metaclass=A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : Union[str, Any] , *_lowerCAmelCase : Any , **_lowerCAmelCase : str ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Optional[Any] , *_lowerCAmelCase : int , **_lowerCAmelCase : Any ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Dict , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : int ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class __UpperCAmelCase ( metaclass=A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__(self : Dict , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : Optional[int] ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Dict , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : Any ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A (cls : Optional[Any] , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : Tuple ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
| 337 | 1 |
from collections import deque
from math import floor
from random import random
from time import time
class lowerCAmelCase_ :
def __init__( self ) -> int:
UpperCamelCase : Dict = {}
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=1 ) -> Union[str, Any]:
if self.graph.get(SCREAMING_SNAKE_CASE_ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
UpperCamelCase : Optional[Any] = [[w, v]]
if not self.graph.get(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = []
def snake_case_ ( self ) -> List[Any]:
return list(self.graph )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Tuple:
if self.graph.get(SCREAMING_SNAKE_CASE_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_=-2, SCREAMING_SNAKE_CASE_=-1 ) -> Optional[Any]:
if s == d:
return []
UpperCamelCase : Optional[Any] = []
UpperCamelCase : int = []
if s == -2:
UpperCamelCase : List[Any] = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE_ )
visited.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase : Tuple = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(SCREAMING_SNAKE_CASE_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
UpperCamelCase : Dict = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(SCREAMING_SNAKE_CASE_ ) != 0:
UpperCamelCase : Optional[int] = stack[len(SCREAMING_SNAKE_CASE_ ) - 1]
else:
UpperCamelCase : Optional[Any] = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return visited
def snake_case_ ( self, SCREAMING_SNAKE_CASE_=-1 ) -> str:
if c == -1:
UpperCamelCase : List[Any] = floor(random() * 1_0000 ) + 10
for i in range(SCREAMING_SNAKE_CASE_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
UpperCamelCase : Tuple = floor(random() * c ) + 1
if n != i:
self.add_pair(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, 1 )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_=-2 ) -> int:
UpperCamelCase : int = deque()
UpperCamelCase : str = []
if s == -2:
UpperCamelCase : Optional[Any] = list(self.graph )[0]
d.append(SCREAMING_SNAKE_CASE_ )
visited.append(SCREAMING_SNAKE_CASE_ )
while d:
UpperCamelCase : int = 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 snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Any:
UpperCamelCase : List[Any] = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Dict:
return len(self.graph[u] )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_=-2 ) -> Any:
UpperCamelCase : List[str] = []
UpperCamelCase : Dict = []
if s == -2:
UpperCamelCase : Optional[Any] = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE_ )
visited.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = s
UpperCamelCase : List[Any] = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase : Optional[int] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
UpperCamelCase : Optional[Any] = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(SCREAMING_SNAKE_CASE_ ) != 0:
UpperCamelCase : List[str] = stack[len(SCREAMING_SNAKE_CASE_ ) - 1]
else:
UpperCamelCase : List[str] = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return sorted_nodes
def snake_case_ ( self ) -> Optional[int]:
UpperCamelCase : Dict = []
UpperCamelCase : Tuple = []
UpperCamelCase : Optional[int] = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE_ )
visited.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = -2
UpperCamelCase : str = []
UpperCamelCase : Union[str, Any] = s
UpperCamelCase : List[Any] = False
UpperCamelCase : List[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase : str = 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
):
UpperCamelCase : Optional[Any] = len(SCREAMING_SNAKE_CASE_ ) - 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] )
UpperCamelCase : str = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
UpperCamelCase : List[str] = True
if len(SCREAMING_SNAKE_CASE_ ) != 0:
UpperCamelCase : Dict = stack[len(SCREAMING_SNAKE_CASE_ ) - 1]
else:
UpperCamelCase : Union[str, Any] = False
indirect_parents.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = s
UpperCamelCase : str = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return list(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ) -> int:
UpperCamelCase : Union[str, Any] = []
UpperCamelCase : int = []
UpperCamelCase : int = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE_ )
visited.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = -2
UpperCamelCase : Union[str, Any] = []
UpperCamelCase : Dict = s
UpperCamelCase : int = False
UpperCamelCase : int = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase : 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
):
UpperCamelCase : List[str] = len(SCREAMING_SNAKE_CASE_ ) - 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] )
UpperCamelCase : List[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
UpperCamelCase : List[str] = True
if len(SCREAMING_SNAKE_CASE_ ) != 0:
UpperCamelCase : List[Any] = stack[len(SCREAMING_SNAKE_CASE_ ) - 1]
else:
UpperCamelCase : Dict = False
indirect_parents.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = s
UpperCamelCase : Optional[Any] = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return False
def snake_case_ ( self, SCREAMING_SNAKE_CASE_=-2, SCREAMING_SNAKE_CASE_=-1 ) -> Tuple:
UpperCamelCase : Tuple = time()
self.dfs(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = time()
return end - begin
def snake_case_ ( self, SCREAMING_SNAKE_CASE_=-2 ) -> Optional[int]:
UpperCamelCase : Union[str, Any] = time()
self.bfs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = time()
return end - begin
class lowerCAmelCase_ :
def __init__( self ) -> Union[str, Any]:
UpperCamelCase : Optional[int] = {}
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=1 ) -> Any:
# check if the u exists
if self.graph.get(SCREAMING_SNAKE_CASE_ ):
# 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
UpperCamelCase : Optional[int] = [[w, v]]
# add the other way
if self.graph.get(SCREAMING_SNAKE_CASE_ ):
# 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
UpperCamelCase : List[str] = [[w, u]]
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> List[Any]:
if self.graph.get(SCREAMING_SNAKE_CASE_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(SCREAMING_SNAKE_CASE_ )
# the other way round
if self.graph.get(SCREAMING_SNAKE_CASE_ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_=-2, SCREAMING_SNAKE_CASE_=-1 ) -> Any:
if s == d:
return []
UpperCamelCase : Any = []
UpperCamelCase : Tuple = []
if s == -2:
UpperCamelCase : Optional[Any] = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE_ )
visited.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase : Optional[int] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(SCREAMING_SNAKE_CASE_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
UpperCamelCase : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(SCREAMING_SNAKE_CASE_ ) != 0:
UpperCamelCase : int = stack[len(SCREAMING_SNAKE_CASE_ ) - 1]
else:
UpperCamelCase : List[Any] = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return visited
def snake_case_ ( self, SCREAMING_SNAKE_CASE_=-1 ) -> List[str]:
if c == -1:
UpperCamelCase : int = floor(random() * 1_0000 ) + 10
for i in range(SCREAMING_SNAKE_CASE_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
UpperCamelCase : List[str] = floor(random() * c ) + 1
if n != i:
self.add_pair(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, 1 )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_=-2 ) -> List[Any]:
UpperCamelCase : int = deque()
UpperCamelCase : str = []
if s == -2:
UpperCamelCase : Tuple = list(self.graph )[0]
d.append(SCREAMING_SNAKE_CASE_ )
visited.append(SCREAMING_SNAKE_CASE_ )
while d:
UpperCamelCase : 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 snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
return len(self.graph[u] )
def snake_case_ ( self ) -> str:
UpperCamelCase : Optional[int] = []
UpperCamelCase : List[str] = []
UpperCamelCase : int = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE_ )
visited.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = -2
UpperCamelCase : Union[str, Any] = []
UpperCamelCase : str = s
UpperCamelCase : List[Any] = False
UpperCamelCase : Optional[int] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase : 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
):
UpperCamelCase : Any = len(SCREAMING_SNAKE_CASE_ ) - 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] )
UpperCamelCase : List[str] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
UpperCamelCase : str = True
if len(SCREAMING_SNAKE_CASE_ ) != 0:
UpperCamelCase : Any = stack[len(SCREAMING_SNAKE_CASE_ ) - 1]
else:
UpperCamelCase : Any = False
indirect_parents.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = s
UpperCamelCase : str = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return list(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ) -> Any:
UpperCamelCase : Optional[int] = []
UpperCamelCase : Optional[Any] = []
UpperCamelCase : List[str] = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE_ )
visited.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = -2
UpperCamelCase : Union[str, Any] = []
UpperCamelCase : Optional[int] = s
UpperCamelCase : List[Any] = False
UpperCamelCase : List[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase : List[str] = 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
):
UpperCamelCase : List[str] = len(SCREAMING_SNAKE_CASE_ ) - 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] )
UpperCamelCase : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
UpperCamelCase : Optional[int] = True
if len(SCREAMING_SNAKE_CASE_ ) != 0:
UpperCamelCase : str = stack[len(SCREAMING_SNAKE_CASE_ ) - 1]
else:
UpperCamelCase : List[Any] = False
indirect_parents.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = s
UpperCamelCase : Union[str, Any] = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return False
def snake_case_ ( self ) -> List[str]:
return list(self.graph )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_=-2, SCREAMING_SNAKE_CASE_=-1 ) -> Union[str, Any]:
UpperCamelCase : Any = time()
self.dfs(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = time()
return end - begin
def snake_case_ ( self, SCREAMING_SNAKE_CASE_=-2 ) -> int:
UpperCamelCase : Optional[int] = time()
self.bfs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = time()
return end - begin
| 119 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''b0''': efficientnet.EfficientNetBa,
'''b1''': efficientnet.EfficientNetBa,
'''b2''': efficientnet.EfficientNetBa,
'''b3''': efficientnet.EfficientNetBa,
'''b4''': efficientnet.EfficientNetBa,
'''b5''': efficientnet.EfficientNetBa,
'''b6''': efficientnet.EfficientNetBa,
'''b7''': efficientnet.EfficientNetBa,
}
__UpperCAmelCase = {
'''b0''': {
'''hidden_dim''': 1_280,
'''width_coef''': 1.0,
'''depth_coef''': 1.0,
'''image_size''': 224,
'''dropout_rate''': 0.2,
'''dw_padding''': [],
},
'''b1''': {
'''hidden_dim''': 1_280,
'''width_coef''': 1.0,
'''depth_coef''': 1.1,
'''image_size''': 240,
'''dropout_rate''': 0.2,
'''dw_padding''': [16],
},
'''b2''': {
'''hidden_dim''': 1_408,
'''width_coef''': 1.1,
'''depth_coef''': 1.2,
'''image_size''': 260,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 8, 16],
},
'''b3''': {
'''hidden_dim''': 1_536,
'''width_coef''': 1.2,
'''depth_coef''': 1.4,
'''image_size''': 300,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 18],
},
'''b4''': {
'''hidden_dim''': 1_792,
'''width_coef''': 1.4,
'''depth_coef''': 1.8,
'''image_size''': 380,
'''dropout_rate''': 0.4,
'''dw_padding''': [6],
},
'''b5''': {
'''hidden_dim''': 2_048,
'''width_coef''': 1.6,
'''depth_coef''': 2.2,
'''image_size''': 456,
'''dropout_rate''': 0.4,
'''dw_padding''': [13, 27],
},
'''b6''': {
'''hidden_dim''': 2_304,
'''width_coef''': 1.8,
'''depth_coef''': 2.6,
'''image_size''': 528,
'''dropout_rate''': 0.5,
'''dw_padding''': [31],
},
'''b7''': {
'''hidden_dim''': 2_560,
'''width_coef''': 2.0,
'''depth_coef''': 3.1,
'''image_size''': 600,
'''dropout_rate''': 0.5,
'''dw_padding''': [18],
},
}
def UpperCamelCase ( snake_case__ : int ) -> Optional[int]:
UpperCamelCase : str = EfficientNetConfig()
UpperCamelCase : Union[str, Any] = CONFIG_MAP[model_name]['hidden_dim']
UpperCamelCase : Union[str, Any] = CONFIG_MAP[model_name]['width_coef']
UpperCamelCase : str = CONFIG_MAP[model_name]['depth_coef']
UpperCamelCase : List[str] = CONFIG_MAP[model_name]['image_size']
UpperCamelCase : List[str] = CONFIG_MAP[model_name]['dropout_rate']
UpperCamelCase : str = CONFIG_MAP[model_name]['dw_padding']
UpperCamelCase : str = 'huggingface/label-files'
UpperCamelCase : Optional[Any] = 'imagenet-1k-id2label.json'
UpperCamelCase : Optional[Any] = 1000
UpperCamelCase : Dict = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type='dataset' ) , 'r' ) )
UpperCamelCase : Tuple = {int(snake_case__ ): v for k, v in idalabel.items()}
UpperCamelCase : Optional[int] = idalabel
UpperCamelCase : Tuple = {v: k for k, v in idalabel.items()}
return config
def UpperCamelCase ( ) -> Tuple:
UpperCamelCase : Optional[Any] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
UpperCamelCase : Union[str, Any] = Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw )
return im
def UpperCamelCase ( snake_case__ : List[str] ) -> List[Any]:
UpperCamelCase : int = CONFIG_MAP[model_name]['image_size']
UpperCamelCase : List[str] = EfficientNetImageProcessor(
size={'height': size, 'width': size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47853944, 0.4732864, 0.47434163] , do_center_crop=snake_case__ , )
return preprocessor
def UpperCamelCase ( snake_case__ : Optional[int] ) -> Dict:
UpperCamelCase : int = [v.split('_' )[0].split('block' )[1] for v in original_param_names if v.startswith('block' )]
UpperCamelCase : str = sorted(set(snake_case__ ) )
UpperCamelCase : int = len(snake_case__ )
UpperCamelCase : str = {b: str(snake_case__ ) for b, i in zip(snake_case__ , range(snake_case__ ) )}
UpperCamelCase : Optional[int] = []
rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight') )
rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight') )
rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias') )
rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean') )
rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var') )
for b in block_names:
UpperCamelCase : Union[str, Any] = block_name_mapping[b]
rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""") )
rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""") )
rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""") )
rename_keys.append(
(F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""") )
rename_keys.append(
(F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""") )
rename_keys.append(
(F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""") )
rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""") )
rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""") )
rename_keys.append(
(F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""") )
rename_keys.append(
(F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""") )
rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""") )
rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""") )
rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""") )
rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""") )
rename_keys.append(
(F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""") )
rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""") )
rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""") )
rename_keys.append(
(F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""") )
rename_keys.append(
(F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""") )
rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight') )
rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight') )
rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias') )
rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean') )
rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var') )
UpperCamelCase : List[str] = {}
for item in rename_keys:
if item[0] in original_param_names:
UpperCamelCase : Dict = 'efficientnet.' + item[1]
UpperCamelCase : Dict = 'classifier.weight'
UpperCamelCase : Dict = 'classifier.bias'
return key_mapping
def UpperCamelCase ( snake_case__ : Optional[Any] , snake_case__ : List[Any] , snake_case__ : int ) -> Dict:
for key, value in tf_params.items():
if "normalization" in key:
continue
UpperCamelCase : Any = key_mapping[key]
if "_conv" in key and "kernel" in key:
UpperCamelCase : str = torch.from_numpy(snake_case__ ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
UpperCamelCase : Any = torch.from_numpy(snake_case__ ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
UpperCamelCase : str = torch.from_numpy(np.transpose(snake_case__ ) )
else:
UpperCamelCase : str = torch.from_numpy(snake_case__ )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(snake_case__ )
@torch.no_grad()
def UpperCamelCase ( snake_case__ : Optional[Any] , snake_case__ : Dict , snake_case__ : int , snake_case__ : Optional[int] ) -> Any:
UpperCamelCase : Union[str, Any] = model_classes[model_name](
include_top=snake_case__ , weights='imagenet' , input_tensor=snake_case__ , input_shape=snake_case__ , pooling=snake_case__ , classes=1000 , classifier_activation='softmax' , )
UpperCamelCase : Optional[int] = original_model.trainable_variables
UpperCamelCase : Optional[int] = original_model.non_trainable_variables
UpperCamelCase : Tuple = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
UpperCamelCase : List[Any] = param.numpy()
UpperCamelCase : List[str] = list(tf_params.keys() )
# Load HuggingFace model
UpperCamelCase : str = get_efficientnet_config(snake_case__ )
UpperCamelCase : Any = EfficientNetForImageClassification(snake_case__ ).eval()
UpperCamelCase : Tuple = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('Converting parameters...' )
UpperCamelCase : List[Any] = rename_keys(snake_case__ )
replace_params(snake_case__ , snake_case__ , snake_case__ )
# Initialize preprocessor and preprocess input image
UpperCamelCase : List[Any] = convert_image_processor(snake_case__ )
UpperCamelCase : Dict = preprocessor(images=prepare_img() , return_tensors='pt' )
# HF model inference
hf_model.eval()
with torch.no_grad():
UpperCamelCase : Optional[int] = hf_model(**snake_case__ )
UpperCamelCase : Dict = outputs.logits.detach().numpy()
# Original model inference
UpperCamelCase : Optional[int] = False
UpperCamelCase : int = CONFIG_MAP[model_name]['image_size']
UpperCamelCase : List[Any] = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
UpperCamelCase : List[Any] = image.img_to_array(snake_case__ )
UpperCamelCase : str = np.expand_dims(snake_case__ , axis=0 )
UpperCamelCase : Any = original_model.predict(snake_case__ )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(snake_case__ , snake_case__ , atol=1E-3 ), "The predicted logits are not the same."
print('Model outputs match!' )
if save_model:
# Create folder to save model
if not os.path.isdir(snake_case__ ):
os.mkdir(snake_case__ )
# Save converted model and image processor
hf_model.save_pretrained(snake_case__ )
preprocessor.save_pretrained(snake_case__ )
if push_to_hub:
# Push model and image processor to hub
print(F"""Pushing converted {model_name} to the hub...""" )
UpperCamelCase : List[str] = F"""efficientnet-{model_name}"""
preprocessor.push_to_hub(snake_case__ )
hf_model.push_to_hub(snake_case__ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''b0''',
type=str,
help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''hf_model''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
__UpperCAmelCase = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 119 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
SCREAMING_SNAKE_CASE_ : Dict = r'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n Args:\n title_sep (`str`, *optional*, defaults to `" / "`):\n Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n doc_sep (`str`, *optional*, defaults to `" // "`):\n Separator inserted between the text of the retrieved document and the original input when calling\n [`RagRetriever`].\n n_docs (`int`, *optional*, defaults to 5):\n Number of documents to retrieve.\n max_combined_length (`int`, *optional*, defaults to 300):\n Max length of contextualized input returned by [`~RagRetriever.__call__`].\n retrieval_vector_size (`int`, *optional*, defaults to 768):\n Dimensionality of the document embeddings indexed by [`RagRetriever`].\n retrieval_batch_size (`int`, *optional*, defaults to 8):\n Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n [`RagRetriever`].\n dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n using `datasets.list_datasets()`).\n dataset_split (`str`, *optional*, defaults to `"train"`)\n Which split of the `dataset` to load.\n index_name (`str`, *optional*, defaults to `"compressed"`)\n The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n `"compressed"`.\n index_path (`str`, *optional*)\n The path to the serialized faiss index on disk.\n passages_path (`str`, *optional*):\n A path to text passages compatible with the faiss index. Required if using\n [`~models.rag.retrieval_rag.LegacyIndex`]\n use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n Whether to load a "dummy" variant of the dataset specified by `dataset`.\n label_smoothing (`float`, *optional*, defaults to 0.0):\n Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n in the loss calculation. If set to 0, no label smoothing is performed.\n do_marginalize (`bool`, *optional*, defaults to `False`):\n If `True`, the logits are marginalized over all documents by making use of\n `torch.nn.functional.log_softmax`.\n reduce_loss (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n do_deduplication (`bool`, *optional*, defaults to `True`):\n Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n set to `False` if used while training with distributed backend.\n exclude_bos_score (`bool`, *optional*, defaults to `False`):\n Whether or not to disregard the BOS token when computing the loss.\n output_retrieved(`bool`, *optional*, defaults to `False`):\n If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n `context_attention_mask` are returned. See returned tensors for more detail.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models).\n forced_eos_token_id (`int`, *optional*):\n The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n `eos_token_id`.\n'
@add_start_docstrings(_lowerCamelCase )
class a ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = "rag"
UpperCAmelCase = True
def __init__( self: str , UpperCamelCase: Optional[int]=None , UpperCamelCase: Optional[Any]=True , UpperCamelCase: str=None , UpperCamelCase: Dict=None , UpperCamelCase: Any=None , UpperCamelCase: Any=None , UpperCamelCase: Union[str, Any]=None , UpperCamelCase: int=" / " , UpperCamelCase: str=" // " , UpperCamelCase: Any=5 , UpperCamelCase: Optional[Any]=3_00 , UpperCamelCase: Any=7_68 , UpperCamelCase: Tuple=8 , UpperCamelCase: Union[str, Any]="wiki_dpr" , UpperCamelCase: Union[str, Any]="train" , UpperCamelCase: Any="compressed" , UpperCamelCase: Tuple=None , UpperCamelCase: Optional[Any]=None , UpperCamelCase: int=False , UpperCamelCase: str=False , UpperCamelCase: str=0.0 , UpperCamelCase: int=True , UpperCamelCase: Tuple=False , UpperCamelCase: int=False , UpperCamelCase: Optional[int]=False , UpperCamelCase: Optional[int]=True , UpperCamelCase: List[str]=None , **UpperCamelCase: int , ):
"""simple docstring"""
super().__init__(
bos_token_id=UpperCamelCase , pad_token_id=UpperCamelCase , eos_token_id=UpperCamelCase , decoder_start_token_id=UpperCamelCase , forced_eos_token_id=UpperCamelCase , is_encoder_decoder=UpperCamelCase , prefix=UpperCamelCase , vocab_size=UpperCamelCase , **UpperCamelCase , )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
A__ = kwargs.pop("""question_encoder""" )
A__ = question_encoder_config.pop("""model_type""" )
A__ = kwargs.pop("""generator""" )
A__ = decoder_config.pop("""model_type""" )
from ..auto.configuration_auto import AutoConfig
A__ = AutoConfig.for_model(UpperCamelCase , **UpperCamelCase )
A__ = AutoConfig.for_model(UpperCamelCase , **UpperCamelCase )
A__ = reduce_loss
A__ = label_smoothing
A__ = exclude_bos_score
A__ = do_marginalize
A__ = title_sep
A__ = doc_sep
A__ = n_docs
A__ = max_combined_length
A__ = dataset
A__ = dataset_split
A__ = index_name
A__ = retrieval_vector_size
A__ = retrieval_batch_size
A__ = passages_path
A__ = index_path
A__ = use_dummy_dataset
A__ = output_retrieved
A__ = do_deduplication
A__ = use_cache
if self.forced_eos_token_id is None:
A__ = getattr(self.generator , """forced_eos_token_id""" , UpperCamelCase )
@classmethod
def UpperCamelCase ( cls: List[str] , UpperCamelCase: PretrainedConfig , UpperCamelCase: PretrainedConfig , **UpperCamelCase: Tuple ):
"""simple docstring"""
return cls(question_encoder=question_encoder_config.to_dict() , generator=generator_config.to_dict() , **UpperCamelCase )
def UpperCamelCase ( self: int ):
"""simple docstring"""
A__ = copy.deepcopy(self.__dict__ )
A__ = self.question_encoder.to_dict()
A__ = self.generator.to_dict()
A__ = self.__class__.model_type
return output
| 353 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a :
"""simple docstring"""
def __init__( self: Optional[int] , UpperCamelCase: List[str] , UpperCamelCase: Dict=13 , UpperCamelCase: Optional[Any]=30 , UpperCamelCase: Optional[Any]=2 , UpperCamelCase: List[str]=3 , UpperCamelCase: Tuple=True , UpperCamelCase: Dict=True , UpperCamelCase: Optional[int]=32 , UpperCamelCase: Tuple=5 , UpperCamelCase: Optional[Any]=4 , UpperCamelCase: Optional[Any]=37 , UpperCamelCase: Optional[Any]="gelu" , UpperCamelCase: Dict=0.1 , UpperCamelCase: Any=0.1 , UpperCamelCase: str=10 , UpperCamelCase: Any=0.02 , UpperCamelCase: List[Any]=None , UpperCamelCase: int=2 , ):
"""simple docstring"""
A__ = parent
A__ = batch_size
A__ = image_size
A__ = patch_size
A__ = num_channels
A__ = is_training
A__ = use_labels
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__ = type_sequence_label_size
A__ = initializer_range
A__ = scope
A__ = encoder_stride
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
A__ = (image_size // patch_size) ** 2
A__ = num_patches + 1
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A__ = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self: int ):
"""simple docstring"""
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCamelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def UpperCamelCase ( self: Tuple , UpperCamelCase: str , UpperCamelCase: Union[str, Any] , UpperCamelCase: Optional[int] ):
"""simple docstring"""
A__ = ViTModel(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self: Optional[int] , UpperCamelCase: Optional[Any] , UpperCamelCase: Optional[int] , UpperCamelCase: Union[str, Any] ):
"""simple docstring"""
A__ = ViTForMaskedImageModeling(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A__ = 1
A__ = ViTForMaskedImageModeling(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ = model(UpperCamelCase )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def UpperCamelCase ( self: str , UpperCamelCase: Dict , UpperCamelCase: List[Any] , UpperCamelCase: List[Any] ):
"""simple docstring"""
A__ = self.type_sequence_label_size
A__ = ViTForImageClassification(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase , labels=UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A__ = 1
A__ = ViTForImageClassification(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ = model(UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
A__ = self.prepare_config_and_inputs()
(
(
A__
) , (
A__
) , (
A__
) ,
) = config_and_inputs
A__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a ( _lowerCamelCase, _lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = (
(
ViTModel,
ViTForImageClassification,
ViTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
UpperCAmelCase = (
{"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase = True
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def UpperCamelCase ( self: Any ):
"""simple docstring"""
A__ = ViTModelTester(self )
A__ = ConfigTester(self , config_class=UpperCamelCase , has_text_modality=UpperCamelCase , hidden_size=37 )
def UpperCamelCase ( self: str ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def UpperCamelCase ( self: str ):
"""simple docstring"""
pass
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCamelCase , nn.Linear ) )
def UpperCamelCase ( self: Dict ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(UpperCamelCase )
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] , UpperCamelCase )
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase )
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase )
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase )
@slow
def UpperCamelCase ( self: Dict ):
"""simple docstring"""
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = ViTModel.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
def _snake_case ( ):
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class a ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None
@slow
def UpperCamelCase ( self: Dict ):
"""simple docstring"""
A__ = ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" ).to(UpperCamelCase )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=UpperCamelCase , return_tensors="""pt""" ).to(UpperCamelCase )
# forward pass
with torch.no_grad():
A__ = model(**UpperCamelCase )
# verify the logits
A__ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , UpperCamelCase )
A__ = torch.tensor([-0.2_744, 0.8_215, -0.0_836] ).to(UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCamelCase , atol=1e-4 ) )
@slow
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = ViTModel.from_pretrained("""facebook/dino-vits8""" ).to(UpperCamelCase )
A__ = ViTImageProcessor.from_pretrained("""facebook/dino-vits8""" , size=4_80 )
A__ = prepare_img()
A__ = image_processor(images=UpperCamelCase , return_tensors="""pt""" )
A__ = inputs.pixel_values.to(UpperCamelCase )
# forward pass
with torch.no_grad():
A__ = model(UpperCamelCase , interpolate_pos_encoding=UpperCamelCase )
# verify the logits
A__ = torch.Size((1, 36_01, 3_84) )
self.assertEqual(outputs.last_hidden_state.shape , UpperCamelCase )
A__ = torch.tensor(
[[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]] ).to(UpperCamelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCamelCase , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = ViTModel.from_pretrained("""facebook/dino-vits8""" , torch_dtype=torch.floataa , device_map="""auto""" )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=UpperCamelCase , return_tensors="""pt""" )
A__ = inputs.pixel_values.to(UpperCamelCase )
# forward pass to make sure inference works in fp16
with torch.no_grad():
A__ = model(UpperCamelCase )
| 69 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a__ = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
'''RagModel''',
'''RagPreTrainedModel''',
'''RagSequenceForGeneration''',
'''RagTokenForGeneration''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
'''TFRagModel''',
'''TFRagPreTrainedModel''',
'''TFRagSequenceForGeneration''',
'''TFRagTokenForGeneration''',
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
a__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 235 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
A_ : List[Any] = current_set.copy()
for row_index, row in enumerate(SCREAMING_SNAKE_CASE ):
A_ : List[str] = row[0]
for column_index, column in enumerate(SCREAMING_SNAKE_CASE ):
if magnitude == 0:
A_ : Union[str, Any] = column
continue
A_ : Dict = column / magnitude
# Subtract to cancel term
A_ : Union[str, Any] = current_set[0]
A_ : Tuple = [first_row]
A_ : int = current_set[1::]
for row in current_set:
A_ : Tuple = []
# If first term is 0, it is already in form we want, so we preserve it
if row[0] == 0:
final_set.append(SCREAMING_SNAKE_CASE )
continue
for column_index in range(len(SCREAMING_SNAKE_CASE ) ):
temp_row.append(first_row[column_index] - row[column_index] )
final_set.append(SCREAMING_SNAKE_CASE )
# Create next recursion iteration set
if len(final_set[0] ) != 3:
A_ : Optional[Any] = final_set[0]
A_ : Any = []
A_ : str = []
for row in final_set[1::]:
current_first_column.append(row[0] )
next_iteration.append(row[1::] )
A_ : Optional[Any] = simplify(SCREAMING_SNAKE_CASE )
for i in range(len(SCREAMING_SNAKE_CASE ) ):
resultant[i].insert(0 , current_first_column[i] )
resultant.insert(0 , SCREAMING_SNAKE_CASE )
A_ : List[Any] = resultant
return final_set
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
if len(SCREAMING_SNAKE_CASE ) == 0:
raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
A_ : str = len(SCREAMING_SNAKE_CASE ) + 1
if any(len(SCREAMING_SNAKE_CASE ) != _length for item in equations ):
raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
for row in equations:
if any(not isinstance(SCREAMING_SNAKE_CASE , (int, float) ) for column in row ):
raise ValueError('''solve_simultaneous() requires lists of integers''' )
if len(SCREAMING_SNAKE_CASE ) == 1:
return [equations[0][-1] / equations[0][0]]
A_ : Dict = equations.copy()
if any(0 in row for row in data_set ):
A_ : Tuple = data_set.copy()
A_ : Optional[Any] = []
for row_index, row in enumerate(SCREAMING_SNAKE_CASE ):
if 0 not in row:
A_ : str = data_set.pop(SCREAMING_SNAKE_CASE )
break
if not full_row:
raise ValueError('''solve_simultaneous() requires at least 1 full equation''' )
data_set.insert(0 , SCREAMING_SNAKE_CASE )
A_ : int = data_set.copy()
A_ : Dict = simplify(SCREAMING_SNAKE_CASE )
A_ : Dict = simplified[::-1]
A_ : list = []
for row in simplified:
A_ : Union[str, Any] = row[-1]
if not solutions:
if row[-2] == 0:
solutions.append(0 )
continue
solutions.append(current_solution / row[-2] )
continue
A_ : Optional[Any] = row.copy()[: len(SCREAMING_SNAKE_CASE ) - 1 :]
while temp_row[0] == 0:
temp_row.pop(0 )
if len(SCREAMING_SNAKE_CASE ) == 0:
solutions.append(0 )
continue
A_ : int = temp_row[1::]
A_ : int = temp_row[::-1]
for column_index, column in enumerate(SCREAMING_SNAKE_CASE ):
current_solution -= column * solutions[column_index]
solutions.append(SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = []
for item in solutions:
final.append(float(round(SCREAMING_SNAKE_CASE , 5 ) ) )
return final[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase = [
[2, 1, 1, 1, 1, 4],
[1, 2, 1, 1, 1, 5],
[1, 1, 2, 1, 1, 6],
[1, 1, 1, 2, 1, 7],
[1, 1, 1, 1, 2, 8],
]
print(solve_simultaneous(eq))
print(solve_simultaneous([[4, 2]]))
| 186 | 0 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class lowerCamelCase ( A_ ):
UpperCAmelCase__ : int = 0
UpperCAmelCase__ : bool = False
UpperCAmelCase__ : float = 3.0
class lowerCamelCase ( unittest.TestCase ):
def UpperCAmelCase(self : int ) -> List[Any]:
# If no defaults are changed, `to_kwargs` returns an empty dict.
self.assertDictEqual(MockClass().to_kwargs() , {} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() , {"a": 2} )
self.assertDictEqual(MockClass(a=2 , b=_A ).to_kwargs() , {"a": 2, "b": True} )
self.assertDictEqual(MockClass(a=2 , c=2.25 ).to_kwargs() , {"a": 2, "c": 2.25} )
@require_cuda
def UpperCAmelCase(self : Any ) -> List[Any]:
# If no defaults are changed, `to_kwargs` returns an empty dict.
snake_case = GradScalerKwargs(init_scale=1_0_2_4 , growth_factor=2 )
AcceleratorState._reset_state()
snake_case = Accelerator(mixed_precision="fp16" , kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
snake_case = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale , 10_24.0 )
self.assertEqual(scaler._growth_factor , 2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor , 0.5 )
self.assertEqual(scaler._growth_interval , 2_0_0_0 )
self.assertEqual(scaler._enabled , _A )
@require_multi_gpu
def UpperCAmelCase(self : List[Any] ) -> Any:
snake_case = ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__ )]
execute_subprocess_async(_A , env=os.environ.copy() )
if __name__ == "__main__":
_A = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
_A = Accelerator(kwargs_handlers=[ddp_scaler])
_A = torch.nn.Linear(1_00, 2_00)
_A = accelerator.prepare(model)
# Check the values changed in kwargs
_A = ""
_A = model.bucket_bytes_cap // (10_24 * 10_24)
if observed_bucket_cap_map != 15:
error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 137 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__(self : Union[str, Any] , _A : Any , _A : Tuple=1_3 , _A : Optional[int]=7 , _A : Any=True , _A : str=True , _A : Union[str, Any]=True , _A : Optional[int]=True , _A : str=9_9 , _A : str=2_4 , _A : int=2 , _A : Optional[Any]=6 , _A : int=3_7 , _A : List[Any]="gelu" , _A : str=0.1 , _A : Dict=0.1 , _A : Dict=5_1_2 , _A : Tuple=1_6 , _A : List[str]=2 , _A : Dict=0.02 , _A : List[str]=3 , _A : Optional[Any]=None , _A : Dict=1_0_0_0 , ) -> Any:
snake_case = parent
snake_case = batch_size
snake_case = seq_length
snake_case = is_training
snake_case = use_input_mask
snake_case = use_token_type_ids
snake_case = use_labels
snake_case = 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 = type_sequence_label_size
snake_case = initializer_range
snake_case = num_labels
snake_case = scope
snake_case = range_bbox
def UpperCAmelCase(self : List[str] ) -> List[str]:
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
snake_case = bbox[i, j, 3]
snake_case = bbox[i, j, 1]
snake_case = t
if bbox[i, j, 2] < bbox[i, j, 0]:
snake_case = bbox[i, j, 2]
snake_case = bbox[i, j, 0]
snake_case = t
snake_case = None
if self.use_input_mask:
snake_case = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
snake_case = None
if self.use_token_type_ids:
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case = None
snake_case = None
if self.use_labels:
snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def UpperCAmelCase(self : Tuple ) -> Tuple:
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def UpperCAmelCase(self : List[str] , _A : Dict , _A : List[Any] , _A : Optional[Any] , _A : Dict , _A : str , _A : Optional[Any] , _A : Tuple , ) -> Dict:
snake_case = LiltModel(config=_A )
model.to(_A )
model.eval()
snake_case = model(_A , bbox=_A , attention_mask=_A , token_type_ids=_A )
snake_case = model(_A , bbox=_A , token_type_ids=_A )
snake_case = model(_A , bbox=_A )
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 : Optional[Any] , _A : Optional[int] , _A : Dict , _A : List[Any] , _A : Tuple , _A : Optional[int] , _A : Tuple , _A : Union[str, Any] , ) -> Optional[int]:
snake_case = self.num_labels
snake_case = LiltForTokenClassification(config=_A )
model.to(_A )
model.eval()
snake_case = model(
_A , bbox=_A , attention_mask=_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase(self : str , _A : List[Any] , _A : Union[str, Any] , _A : Any , _A : List[str] , _A : List[str] , _A : Optional[int] , _A : Optional[Any] , ) -> Optional[int]:
snake_case = LiltForQuestionAnswering(config=_A )
model.to(_A )
model.eval()
snake_case = model(
_A , bbox=_A , attention_mask=_A , token_type_ids=_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 : str ) -> str:
snake_case = self.prepare_config_and_inputs()
(
(
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) ,
) = config_and_inputs
snake_case = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class lowerCamelCase ( A_ , A_ , A_ , unittest.TestCase ):
UpperCAmelCase__ : Optional[int] = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCAmelCase__ : List[Any] = (
{
"feature-extraction": LiltModel,
"question-answering": LiltForQuestionAnswering,
"text-classification": LiltForSequenceClassification,
"token-classification": LiltForTokenClassification,
"zero-shot": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : Optional[int] = False
def UpperCAmelCase(self : Dict , _A : Optional[Any] , _A : Dict , _A : Union[str, Any] , _A : int , _A : Union[str, Any] ) -> int:
return True
def UpperCAmelCase(self : str ) -> Tuple:
snake_case = LiltModelTester(self )
snake_case = ConfigTester(self , config_class=_A , hidden_size=3_7 )
def UpperCAmelCase(self : Optional[int] ) -> List[str]:
self.config_tester.run_common_tests()
def UpperCAmelCase(self : Tuple ) -> Dict:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def UpperCAmelCase(self : int ) -> Union[str, Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case = type
self.model_tester.create_and_check_model(*_A )
def UpperCAmelCase(self : Optional[Any] ) -> List[Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_A )
def UpperCAmelCase(self : Optional[Any] ) -> Optional[int]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_A )
@slow
def UpperCAmelCase(self : Optional[Any] ) -> Optional[Any]:
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case = LiltModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@require_torch
@slow
class lowerCamelCase ( unittest.TestCase ):
def UpperCAmelCase(self : Tuple ) -> Optional[int]:
snake_case = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base" ).to(_A )
snake_case = torch.tensor([[1, 2]] , device=_A )
snake_case = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=_A )
# forward pass
with torch.no_grad():
snake_case = model(input_ids=_A , bbox=_A )
snake_case = torch.Size([1, 2, 7_6_8] )
snake_case = torch.tensor(
[[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]] , device=_A , )
self.assertTrue(outputs.last_hidden_state.shape , _A )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , _A , atol=1E-3 ) )
| 137 | 1 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ,snake_case__=5 ) -> Union[str, Any]:
"""simple docstring"""
assert masked_input.count("""<mask>""" ) == 1
_SCREAMING_SNAKE_CASE = torch.tensor(tokenizer.encode(SCREAMING_SNAKE_CASE__ ,add_special_tokens=SCREAMING_SNAKE_CASE__ ) ).unsqueeze(0 ) # Batch size 1
_SCREAMING_SNAKE_CASE = model(SCREAMING_SNAKE_CASE__ )[0] # The last hidden-state is the first element of the output tuple
_SCREAMING_SNAKE_CASE = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_SCREAMING_SNAKE_CASE = logits[0, masked_index, :]
_SCREAMING_SNAKE_CASE = logits.softmax(dim=0 )
_SCREAMING_SNAKE_CASE = prob.topk(k=SCREAMING_SNAKE_CASE__ ,dim=0 )
_SCREAMING_SNAKE_CASE = """ """.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(SCREAMING_SNAKE_CASE__ ) )] )
_SCREAMING_SNAKE_CASE = tokenizer.mask_token
_SCREAMING_SNAKE_CASE = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """ ) ):
_SCREAMING_SNAKE_CASE = predicted_token_bpe.replace("""\u2581""" ,""" """ )
if " {0}".format(SCREAMING_SNAKE_CASE__ ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(""" {0}""".format(SCREAMING_SNAKE_CASE__ ) ,SCREAMING_SNAKE_CASE__ ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
UpperCamelCase = CamembertTokenizer.from_pretrained('''camembert-base''')
UpperCamelCase = CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()
UpperCamelCase = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 306 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@require_flax
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase__ ( self : Any , A : Dict , A : Any ):
return f'''gaussian_noise_s={seed}_shape={'_'.join([str(A ) for s in shape] )}.npy'''
def UpperCAmelCase__ ( self : Optional[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def UpperCAmelCase__ ( self : Optional[Any] , A : Optional[int]=0 , A : Tuple=(4, 4, 64, 64) , A : Tuple=False ):
__snake_case: Dict = jnp.bfloataa if fpaa else jnp.floataa
__snake_case: str = jnp.array(load_hf_numpy(self.get_file_format(A , A ) ) , dtype=A )
return image
def UpperCAmelCase__ ( self : Union[str, Any] , A : Any=False , A : Optional[Any]="CompVis/stable-diffusion-v1-4" ):
__snake_case: List[Any] = jnp.bfloataa if fpaa else jnp.floataa
__snake_case: Union[str, Any] = """bf16""" if fpaa else None
__snake_case , __snake_case: Optional[int] = FlaxUNetaDConditionModel.from_pretrained(
A , subfolder="""unet""" , dtype=A , revision=A )
return model, params
def UpperCAmelCase__ ( self : Tuple , A : Tuple=0 , A : str=(4, 77, 768) , A : List[str]=False ):
__snake_case: Any = jnp.bfloataa if fpaa else jnp.floataa
__snake_case: Dict = jnp.array(load_hf_numpy(self.get_file_format(A , A ) ) , dtype=A )
return hidden_states
@parameterized.expand(
[
# fmt: off
[83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
[17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
[8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
[3, 1_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
# fmt: on
] )
def UpperCAmelCase__ ( self : Optional[Any] , A : Optional[Any] , A : str , A : Any ):
__snake_case , __snake_case: Union[str, Any] = self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4""" , fpaa=A )
__snake_case: Tuple = self.get_latents(A , fpaa=A )
__snake_case: int = self.get_encoder_hidden_states(A , fpaa=A )
__snake_case: List[Any] = model.apply(
{"""params""": params} , A , jnp.array(A , dtype=jnp.intaa ) , encoder_hidden_states=A , ).sample
assert sample.shape == latents.shape
__snake_case: str = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
__snake_case: Optional[int] = jnp.array(A , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
assert jnp.allclose(A , A , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
[17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
[8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
[3, 1_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
# fmt: on
] )
def UpperCAmelCase__ ( self : Optional[Any] , A : int , A : Tuple , A : List[str] ):
__snake_case , __snake_case: Union[str, Any] = self.get_unet_model(model_id="""stabilityai/stable-diffusion-2""" , fpaa=A )
__snake_case: Optional[int] = self.get_latents(A , shape=(4, 4, 96, 96) , fpaa=A )
__snake_case: str = self.get_encoder_hidden_states(A , shape=(4, 77, 1_024) , fpaa=A )
__snake_case: str = model.apply(
{"""params""": params} , A , jnp.array(A , dtype=jnp.intaa ) , encoder_hidden_states=A , ).sample
assert sample.shape == latents.shape
__snake_case: Dict = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
__snake_case: Any = jnp.array(A , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
assert jnp.allclose(A , A , atol=1E-2 )
| 111 | 0 |
from __future__ import annotations
class lowercase :
def __init__( self , A_ ) -> None:
"""simple docstring"""
UpperCamelCase = order
# a_{0} ... a_{k}
UpperCamelCase = [1.0] + [0.0] * order
# b_{0} ... b_{k}
UpperCamelCase = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
UpperCamelCase = [0.0] * self.order
# y[n-1] ... y[n-k]
UpperCamelCase = [0.0] * self.order
def __UpperCamelCase ( self , A_ , A_ ) -> None:
"""simple docstring"""
if len(__lowercase ) < self.order:
UpperCamelCase = [1.0, *a_coeffs]
if len(__lowercase ) != self.order + 1:
UpperCamelCase = (
F'''Expected a_coeffs to have {self.order + 1} elements '''
F'''for {self.order}-order filter, got {len(__lowercase )}'''
)
raise ValueError(__lowercase )
if len(__lowercase ) != self.order + 1:
UpperCamelCase = (
F'''Expected b_coeffs to have {self.order + 1} elements '''
F'''for {self.order}-order filter, got {len(__lowercase )}'''
)
raise ValueError(__lowercase )
UpperCamelCase = a_coeffs
UpperCamelCase = b_coeffs
def __UpperCamelCase ( self , A_ ) -> float:
"""simple docstring"""
UpperCamelCase = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
UpperCamelCase = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
UpperCamelCase = self.input_history[:-1]
UpperCamelCase = self.output_history[:-1]
UpperCamelCase = sample
UpperCamelCase = result
return result
| 367 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def A ( ) -> int:
'''simple docstring'''
UpperCamelCase = 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
UpperCamelCase = Image.open(requests.get(lowercase , stream=lowercase ).raw ).convert('RGB' )
return image
def A ( lowercase ) -> Any:
'''simple docstring'''
UpperCamelCase = []
# fmt: off
# vision encoder
rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding') )
rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding') )
rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight') )
rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias') )
rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight') )
rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.weight''', f'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.bias''', f'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.weight''', f'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.bias''', f'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.qkv.weight''', f'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.weight''', f'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.bias''', f'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.layernorm.weight') )
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias') )
# fmt: on
return rename_keys
def A ( lowercase , lowercase , lowercase ) -> Dict:
'''simple docstring'''
UpperCamelCase = dct.pop(lowercase )
UpperCamelCase = val
def A ( lowercase , lowercase ) -> List[str]:
'''simple docstring'''
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
UpperCamelCase = state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias''' )
UpperCamelCase = state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
UpperCamelCase = torch.cat((q_bias, torch.zeros_like(lowercase , requires_grad=lowercase ), v_bias) )
UpperCamelCase = qkv_bias
def A ( lowercase , lowercase ) -> int:
'''simple docstring'''
UpperCamelCase = 364 if 'coco' in model_name else 224
UpperCamelCase = BlipaVisionConfig(image_size=lowercase ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
UpperCamelCase = OPTConfig.from_pretrained('facebook/opt-2.7b' , eos_token_id=lowercase ).to_dict()
elif "opt-6.7b" in model_name:
UpperCamelCase = OPTConfig.from_pretrained('facebook/opt-6.7b' , eos_token_id=lowercase ).to_dict()
elif "t5-xl" in model_name:
UpperCamelCase = TaConfig.from_pretrained('google/flan-t5-xl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
UpperCamelCase = TaConfig.from_pretrained('google/flan-t5-xxl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
UpperCamelCase = BlipaConfig(vision_config=lowercase , text_config=lowercase )
return config, image_size
@torch.no_grad()
def A ( lowercase , lowercase=None , lowercase=False ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase = (
AutoTokenizer.from_pretrained('facebook/opt-2.7b' )
if 'opt' in model_name
else AutoTokenizer.from_pretrained('google/flan-t5-xl' )
)
UpperCamelCase = tokenizer('\n' , add_special_tokens=lowercase ).input_ids[0]
UpperCamelCase , UpperCamelCase = get_blipa_config(lowercase , eos_token_id=lowercase )
UpperCamelCase = BlipaForConditionalGeneration(lowercase ).eval()
UpperCamelCase = {
'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
}
UpperCamelCase , UpperCamelCase = model_name_to_original[model_name]
# load original model
print('Loading original model...' )
UpperCamelCase = 'cuda' if torch.cuda.is_available() else 'cpu'
UpperCamelCase , UpperCamelCase , UpperCamelCase = load_model_and_preprocess(
name=lowercase , model_type=lowercase , is_eval=lowercase , device=lowercase )
original_model.eval()
print('Done!' )
# update state dict keys
UpperCamelCase = original_model.state_dict()
UpperCamelCase = create_rename_keys(lowercase )
for src, dest in rename_keys:
rename_key(lowercase , lowercase , lowercase )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
UpperCamelCase = state_dict.pop(lowercase )
if key.startswith('Qformer.bert' ):
UpperCamelCase = key.replace('Qformer.bert' , 'qformer' )
if "attention.self" in key:
UpperCamelCase = key.replace('self' , 'attention' )
if "opt_proj" in key:
UpperCamelCase = key.replace('opt_proj' , 'language_projection' )
if "t5_proj" in key:
UpperCamelCase = key.replace('t5_proj' , 'language_projection' )
if key.startswith('opt' ):
UpperCamelCase = key.replace('opt' , 'language' )
if key.startswith('t5' ):
UpperCamelCase = key.replace('t5' , 'language' )
UpperCamelCase = val
# read in qv biases
read_in_q_v_bias(lowercase , lowercase )
UpperCamelCase , UpperCamelCase = hf_model.load_state_dict(lowercase , strict=lowercase )
assert len(lowercase ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
UpperCamelCase = load_demo_image()
UpperCamelCase = vis_processors['eval'](lowercase ).unsqueeze(0 ).to(lowercase )
UpperCamelCase = tokenizer(['\n'] , return_tensors='pt' ).input_ids.to(lowercase )
# create processor
UpperCamelCase = BlipImageProcessor(
size={'height': image_size, 'width': image_size} , image_mean=lowercase , image_std=lowercase )
UpperCamelCase = BlipaProcessor(image_processor=lowercase , tokenizer=lowercase )
UpperCamelCase = processor(images=lowercase , return_tensors='pt' ).pixel_values.to(lowercase )
# make sure processor creates exact same pixel values
assert torch.allclose(lowercase , lowercase )
original_model.to(lowercase )
hf_model.to(lowercase )
with torch.no_grad():
if "opt" in model_name:
UpperCamelCase = original_model({'image': original_pixel_values, 'text_input': ['']} ).logits
UpperCamelCase = hf_model(lowercase , lowercase ).logits
else:
UpperCamelCase = original_model(
{'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']} ).logits
UpperCamelCase = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -100 )
UpperCamelCase = hf_model(lowercase , lowercase , labels=lowercase ).logits
assert original_logits.shape == logits.shape
print('First values of original logits:' , original_logits[0, :3, :3] )
print('First values of HF logits:' , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
UpperCamelCase = torch.tensor(
[[-4_1.5_8_5_0, -4.4_4_4_0, -8.9_9_2_2], [-4_7.4_3_2_2, -5.9_1_4_3, -1.7_3_4_0]] , device=lowercase )
assert torch.allclose(logits[0, :3, :3] , lowercase , atol=1e-4 )
elif model_name == "blip2-flan-t5-xl-coco":
UpperCamelCase = torch.tensor(
[[-5_7.0_1_0_9, -9.8_9_6_7, -1_2.6_2_8_0], [-6_8.6_5_7_8, -1_2.7_1_9_1, -1_0.5_0_6_5]] , device=lowercase )
else:
# cast to same type
UpperCamelCase = logits.dtype
assert torch.allclose(original_logits.to(lowercase ) , lowercase , atol=1e-2 )
print('Looks ok!' )
print('Generating a caption...' )
UpperCamelCase = ''
UpperCamelCase = tokenizer(lowercase , return_tensors='pt' ).input_ids.to(lowercase )
UpperCamelCase = original_model.generate({'image': original_pixel_values} )
UpperCamelCase = hf_model.generate(
lowercase , lowercase , do_sample=lowercase , num_beams=5 , max_length=30 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print('Original generation:' , lowercase )
UpperCamelCase = input_ids.shape[1]
UpperCamelCase = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=lowercase )
UpperCamelCase = [text.strip() for text in output_text]
print('HF generation:' , lowercase )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(lowercase )
hf_model.save_pretrained(lowercase )
if push_to_hub:
processor.push_to_hub(f'''nielsr/{model_name}''' )
hf_model.push_to_hub(f'''nielsr/{model_name}''' )
if __name__ == "__main__":
_UpperCAmelCase : Optional[int] = argparse.ArgumentParser()
_UpperCAmelCase : str = [
"blip2-opt-2.7b",
"blip2-opt-6.7b",
"blip2-opt-2.7b-coco",
"blip2-opt-6.7b-coco",
"blip2-flan-t5-xl",
"blip2-flan-t5-xl-coco",
"blip2-flan-t5-xxl",
]
parser.add_argument(
"--model_name",
default="blip2-opt-2.7b",
choices=choices,
type=str,
help="Path to hf config.json of model to convert",
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub after converting",
)
_UpperCAmelCase : List[str] = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 110 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a ( lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE : str = OpenAIGPTTokenizer
SCREAMING_SNAKE_CASE : List[str] = OpenAIGPTTokenizerFast
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : int = False
def UpperCamelCase ( self : Dict ) -> List[str]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCamelCase_ = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
lowerCamelCase_ = dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
lowerCamelCase_ = ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) )
with open(self.merges_file , 'w' ) as fp:
fp.write('\n'.join(lowerCamelCase__ ) )
def UpperCamelCase ( self : Tuple , __SCREAMING_SNAKE_CASE : Dict ) -> Optional[int]:
return "lower newer", "lower newer"
def UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
lowerCamelCase_ = OpenAIGPTTokenizer(self.vocab_file , self.merges_file )
lowerCamelCase_ = 'lower'
lowerCamelCase_ = ['low', 'er</w>']
lowerCamelCase_ = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ = tokens + ['<unk>']
lowerCamelCase_ = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , lowerCamelCase__ )
def UpperCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : List[Any]=15 ) -> Tuple:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
# Simple input
lowerCamelCase_ = 'This is a simple input'
lowerCamelCase_ = ['This is a simple input 1', 'This is a simple input 2']
lowerCamelCase_ = ('This is a simple input', 'This is a pair')
lowerCamelCase_ = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(lowerCamelCase__ , tokenizer_r.encode , lowerCamelCase__ , max_length=lowerCamelCase__ , padding='max_length' )
# Simple input
self.assertRaises(lowerCamelCase__ , tokenizer_r.encode_plus , lowerCamelCase__ , max_length=lowerCamelCase__ , padding='max_length' )
# Simple input
self.assertRaises(
lowerCamelCase__ , tokenizer_r.batch_encode_plus , lowerCamelCase__ , max_length=lowerCamelCase__ , padding='max_length' , )
# Pair input
self.assertRaises(lowerCamelCase__ , tokenizer_r.encode , lowerCamelCase__ , max_length=lowerCamelCase__ , padding='max_length' )
# Pair input
self.assertRaises(lowerCamelCase__ , tokenizer_r.encode_plus , lowerCamelCase__ , max_length=lowerCamelCase__ , padding='max_length' )
# Pair input
self.assertRaises(
lowerCamelCase__ , tokenizer_r.batch_encode_plus , lowerCamelCase__ , max_length=lowerCamelCase__ , padding='max_length' , )
def UpperCamelCase ( self : str ) -> List[str]:
pass
@require_ftfy
@require_spacy
@require_tokenizers
class a ( lowerCAmelCase_ ):
pass
| 183 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
@dataclass
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Optional[float] = field(
default=0.0 , metadata={"help": "The label smoothing epsilon to apply (if not zero)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Whether to SortishSamler or not."} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "whether to use adafactor"} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Encoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Decoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(default=lowerCAmelCase_ , metadata={"help": "Dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Attention dropout probability. Goes into model.config."} )
__snake_case : Optional[str] = field(
default="linear" , metadata={"help": F"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}"} , )
| 296 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __a (metaclass=UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = ["""transformers""", """torch""", """note_seq"""]
def __init__( self , *_a , **_a ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ["""transformers""", """torch""", """note_seq"""] )
@classmethod
def _a ( cls , *_a , **_a ) -> int:
"""simple docstring"""
requires_backends(cls , ["""transformers""", """torch""", """note_seq"""] )
@classmethod
def _a ( cls , *_a , **_a ) -> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ["""transformers""", """torch""", """note_seq"""] )
| 56 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :List[str] = logging.get_logger(__name__)
a :Union[str, Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
),
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[str] = """audio-spectrogram-transformer"""
def __init__( self , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.0 , _a=0.0 , _a=0.02 , _a=1E-1_2 , _a=16 , _a=True , _a=10 , _a=10 , _a=1_024 , _a=128 , **_a , ) -> List[Any]:
"""simple docstring"""
super().__init__(**_a )
SCREAMING_SNAKE_CASE__ : Dict = hidden_size
SCREAMING_SNAKE_CASE__ : Any = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : Dict = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : List[str] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] = initializer_range
SCREAMING_SNAKE_CASE__ : Dict = layer_norm_eps
SCREAMING_SNAKE_CASE__ : List[Any] = patch_size
SCREAMING_SNAKE_CASE__ : Dict = qkv_bias
SCREAMING_SNAKE_CASE__ : Any = frequency_stride
SCREAMING_SNAKE_CASE__ : int = time_stride
SCREAMING_SNAKE_CASE__ : int = max_length
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_mel_bins
| 56 | 1 |
"""simple docstring"""
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
UpperCAmelCase : str = logging.get_logger(__name__)
def lowerCamelCase ( _UpperCamelCase : Optional[int] , _UpperCamelCase : str , _UpperCamelCase : Optional[int] ) -> int:
'''simple docstring'''
return [
int(1_0_0_0 * (box[0] / width) ),
int(1_0_0_0 * (box[1] / height) ),
int(1_0_0_0 * (box[2] / width) ),
int(1_0_0_0 * (box[3] / height) ),
]
def lowerCamelCase ( _UpperCamelCase : np.ndarray , _UpperCamelCase : Optional[str] , _UpperCamelCase : Optional[str] ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : List[Any] = to_pil_image(_UpperCamelCase )
__UpperCAmelCase ,__UpperCAmelCase : Any = pil_image.size
__UpperCAmelCase : Optional[Any] = pytesseract.image_to_data(_UpperCamelCase , lang=_UpperCamelCase , output_type="""dict""" , config=_UpperCamelCase )
__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase : List[Any] = data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]
# filter empty words and corresponding coordinates
__UpperCAmelCase : Any = [idx for idx, word in enumerate(_UpperCamelCase ) if not word.strip()]
__UpperCAmelCase : Any = [word for idx, word in enumerate(_UpperCamelCase ) if idx not in irrelevant_indices]
__UpperCAmelCase : List[str] = [coord for idx, coord in enumerate(_UpperCamelCase ) if idx not in irrelevant_indices]
__UpperCAmelCase : int = [coord for idx, coord in enumerate(_UpperCamelCase ) if idx not in irrelevant_indices]
__UpperCAmelCase : Any = [coord for idx, coord in enumerate(_UpperCamelCase ) if idx not in irrelevant_indices]
__UpperCAmelCase : List[str] = [coord for idx, coord in enumerate(_UpperCamelCase ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
__UpperCAmelCase : str = []
for x, y, w, h in zip(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
__UpperCAmelCase : Tuple = [x, y, x + w, y + h]
actual_boxes.append(_UpperCamelCase )
# finally, normalize the bounding boxes
__UpperCAmelCase : List[Any] = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) )
assert len(_UpperCamelCase ) == len(_UpperCamelCase ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = ["""pixel_values"""]
def __init__( self : List[str] , UpperCamelCase : bool = True , UpperCamelCase : Dict[str, int] = None , UpperCamelCase : PILImageResampling = PILImageResampling.BILINEAR , UpperCamelCase : bool = True , UpperCamelCase : float = 1 / 255 , UpperCamelCase : bool = True , UpperCamelCase : Union[float, Iterable[float]] = None , UpperCamelCase : Union[float, Iterable[float]] = None , UpperCamelCase : bool = True , UpperCamelCase : Optional[str] = None , UpperCamelCase : Optional[str] = "" , **UpperCamelCase : Any , ):
'''simple docstring'''
super().__init__(**UpperCamelCase )
__UpperCAmelCase : Tuple = size if size is not None else {"""height""": 224, """width""": 224}
__UpperCAmelCase : Union[str, Any] = get_size_dict(UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = do_resize
__UpperCAmelCase : Optional[Any] = size
__UpperCAmelCase : str = resample
__UpperCAmelCase : Any = do_rescale
__UpperCAmelCase : Any = rescale_value
__UpperCAmelCase : Any = do_normalize
__UpperCAmelCase : Optional[int] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__UpperCAmelCase : str = image_std if image_std is not None else IMAGENET_STANDARD_STD
__UpperCAmelCase : Dict = apply_ocr
__UpperCAmelCase : Optional[int] = ocr_lang
__UpperCAmelCase : str = tesseract_config
def lowerCamelCase__ ( self : List[Any] , UpperCamelCase : np.ndarray , UpperCamelCase : Dict[str, int] , UpperCamelCase : PILImageResampling = PILImageResampling.BILINEAR , UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase : List[str] , ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = get_size_dict(UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}''' )
__UpperCAmelCase : List[Any] = (size["""height"""], size["""width"""])
return resize(UpperCamelCase , size=UpperCamelCase , resample=UpperCamelCase , data_format=UpperCamelCase , **UpperCamelCase )
def lowerCamelCase__ ( self : str , UpperCamelCase : np.ndarray , UpperCamelCase : Union[int, float] , UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase : Optional[Any] , ):
'''simple docstring'''
return rescale(UpperCamelCase , scale=UpperCamelCase , data_format=UpperCamelCase , **UpperCamelCase )
def lowerCamelCase__ ( self : Tuple , UpperCamelCase : np.ndarray , UpperCamelCase : Union[float, Iterable[float]] , UpperCamelCase : Union[float, Iterable[float]] , UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase : Tuple , ):
'''simple docstring'''
return normalize(UpperCamelCase , mean=UpperCamelCase , std=UpperCamelCase , data_format=UpperCamelCase , **UpperCamelCase )
def lowerCamelCase__ ( self : Optional[int] , UpperCamelCase : ImageInput , UpperCamelCase : bool = None , UpperCamelCase : Dict[str, int] = None , UpperCamelCase : Tuple=None , UpperCamelCase : bool = None , UpperCamelCase : float = None , UpperCamelCase : bool = None , UpperCamelCase : Union[float, Iterable[float]] = None , UpperCamelCase : Union[float, Iterable[float]] = None , UpperCamelCase : bool = None , UpperCamelCase : Optional[str] = None , UpperCamelCase : Optional[str] = None , UpperCamelCase : Optional[Union[str, TensorType]] = None , UpperCamelCase : ChannelDimension = ChannelDimension.FIRST , **UpperCamelCase : Union[str, Any] , ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = do_resize if do_resize is not None else self.do_resize
__UpperCAmelCase : Union[str, Any] = size if size is not None else self.size
__UpperCAmelCase : Optional[int] = get_size_dict(UpperCamelCase )
__UpperCAmelCase : List[str] = resample if resample is not None else self.resample
__UpperCAmelCase : str = do_rescale if do_rescale is not None else self.do_rescale
__UpperCAmelCase : Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
__UpperCAmelCase : List[str] = do_normalize if do_normalize is not None else self.do_normalize
__UpperCAmelCase : List[str] = image_mean if image_mean is not None else self.image_mean
__UpperCAmelCase : Any = image_std if image_std is not None else self.image_std
__UpperCAmelCase : List[Any] = apply_ocr if apply_ocr is not None else self.apply_ocr
__UpperCAmelCase : Any = ocr_lang if ocr_lang is not None else self.ocr_lang
__UpperCAmelCase : Dict = tesseract_config if tesseract_config is not None else self.tesseract_config
__UpperCAmelCase : List[str] = make_list_of_images(UpperCamelCase )
if not valid_images(UpperCamelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""If do_normalize is True, image_mean and image_std must be specified.""" )
# All transformations expect numpy arrays.
__UpperCAmelCase : Dict = [to_numpy_array(UpperCamelCase ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , """pytesseract""" )
__UpperCAmelCase : Tuple = []
__UpperCAmelCase : Any = []
for image in images:
__UpperCAmelCase ,__UpperCAmelCase : Union[str, Any] = apply_tesseract(UpperCamelCase , UpperCamelCase , UpperCamelCase )
words_batch.append(UpperCamelCase )
boxes_batch.append(UpperCamelCase )
if do_resize:
__UpperCAmelCase : Optional[Any] = [self.resize(image=UpperCamelCase , size=UpperCamelCase , resample=UpperCamelCase ) for image in images]
if do_rescale:
__UpperCAmelCase : List[str] = [self.rescale(image=UpperCamelCase , scale=UpperCamelCase ) for image in images]
if do_normalize:
__UpperCAmelCase : str = [self.normalize(image=UpperCamelCase , mean=UpperCamelCase , std=UpperCamelCase ) for image in images]
__UpperCAmelCase : Tuple = [to_channel_dimension_format(UpperCamelCase , UpperCamelCase ) for image in images]
__UpperCAmelCase : Union[str, Any] = BatchFeature(data={"""pixel_values""": images} , tensor_type=UpperCamelCase )
if apply_ocr:
__UpperCAmelCase : Any = words_batch
__UpperCAmelCase : Optional[int] = boxes_batch
return data
| 115 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowerCamelCase__ ( ctypes.Structure ):
"""simple docstring"""
__a = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def lowerCamelCase ( ) -> Optional[int]:
'''simple docstring'''
if os.name == "nt":
__UpperCAmelCase : Dict = CursorInfo()
__UpperCAmelCase : Any = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase , ctypes.byref(_UpperCamelCase ) )
__UpperCAmelCase : Tuple = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase , ctypes.byref(_UpperCamelCase ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowerCamelCase ( ) -> Optional[int]:
'''simple docstring'''
if os.name == "nt":
__UpperCAmelCase : str = CursorInfo()
__UpperCAmelCase : int = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase , ctypes.byref(_UpperCamelCase ) )
__UpperCAmelCase : Union[str, Any] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase , ctypes.byref(_UpperCamelCase ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase ( ) -> str:
'''simple docstring'''
try:
hide_cursor()
yield
finally:
show_cursor()
| 115 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : List[Any] = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Optional[Any] = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
lowerCamelCase : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 306 |
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
if len(lowerCAmelCase_ ) != len(lowerCAmelCase_ ):
raise ValueError("""String lengths must match!""" )
__lowercase : str = 0
for chara, chara in zip(lowerCAmelCase_ , lowerCAmelCase_ ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod() | 306 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
"""speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
# See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "mctct"
def __init__( self : Union[str, Any] , lowercase_ : str=8065 , lowercase_ : Optional[Any]=1536 , lowercase_ : str=36 , lowercase_ : List[str]=6144 , lowercase_ : Optional[Any]=4 , lowercase_ : Optional[Any]=384 , lowercase_ : Tuple=920 , lowercase_ : Any=1e-5 , lowercase_ : Optional[Any]=0.3 , lowercase_ : Any="relu" , lowercase_ : Any=0.02 , lowercase_ : Dict=0.3 , lowercase_ : int=0.3 , lowercase_ : Union[str, Any]=1 , lowercase_ : Union[str, Any]=0 , lowercase_ : Union[str, Any]=2 , lowercase_ : Union[str, Any]=1 , lowercase_ : List[str]=0.3 , lowercase_ : Optional[int]=1 , lowercase_ : Dict=(7,) , lowercase_ : Union[str, Any]=(3,) , lowercase_ : Tuple=80 , lowercase_ : Union[str, Any]=1 , lowercase_ : Any=None , lowercase_ : Any="sum" , lowercase_ : List[Any]=False , **lowercase_ : Any , ):
'''simple docstring'''
super().__init__(**lowercase_ , pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_)
SCREAMING_SNAKE_CASE_ : str = vocab_size
SCREAMING_SNAKE_CASE_ : Optional[int] = hidden_size
SCREAMING_SNAKE_CASE_ : int = num_hidden_layers
SCREAMING_SNAKE_CASE_ : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE_ : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE_ : Any = attention_head_dim
SCREAMING_SNAKE_CASE_ : int = max_position_embeddings
SCREAMING_SNAKE_CASE_ : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE_ : Union[str, Any] = layerdrop
SCREAMING_SNAKE_CASE_ : str = hidden_act
SCREAMING_SNAKE_CASE_ : List[Any] = initializer_range
SCREAMING_SNAKE_CASE_ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : Tuple = pad_token_id
SCREAMING_SNAKE_CASE_ : Tuple = bos_token_id
SCREAMING_SNAKE_CASE_ : int = eos_token_id
SCREAMING_SNAKE_CASE_ : Optional[Any] = conv_glu_dim
SCREAMING_SNAKE_CASE_ : List[str] = conv_dropout
SCREAMING_SNAKE_CASE_ : Optional[Any] = num_conv_layers
SCREAMING_SNAKE_CASE_ : Tuple = input_feat_per_channel
SCREAMING_SNAKE_CASE_ : Optional[int] = input_channels
SCREAMING_SNAKE_CASE_ : List[str] = conv_channels
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ctc_loss_reduction
SCREAMING_SNAKE_CASE_ : str = ctc_zero_infinity
# prevents config testing fail with exporting to json
SCREAMING_SNAKE_CASE_ : Optional[Any] = list(lowercase_)
SCREAMING_SNAKE_CASE_ : Tuple = list(lowercase_)
if len(self.conv_kernel) != self.num_conv_layers:
raise ValueError(
'''Configuration for convolutional module is incorrect. '''
'''It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '''
F'but is `len(config.conv_kernel) = {len(self.conv_kernel)}`, '
F'`config.num_conv_layers = {self.num_conv_layers}`.')
| 91 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCAmelCase_ : Optional[Any] = datasets.logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = """\
@InProceedings{moosavi2019minimum,
author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
year = {2019},
booktitle = {Proceedings of the 57th Annual Meeting of
the Association for Computational Linguistics (Volume 1: Long Papers)},
publisher = {Association for Computational Linguistics},
address = {Florence, Italy},
}
@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}
@INPROCEEDINGS{Bagga98algorithmsfor,
author = {Amit Bagga and Breck Baldwin},
title = {Algorithms for Scoring Coreference Chains},
booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
year = {1998},
pages = {563--566}
}
@INPROCEEDINGS{Luo05oncoreference,
author = {Xiaoqiang Luo},
title = {On coreference resolution performance metrics},
booktitle = {In Proc. of HLT/EMNLP},
year = {2005},
pages = {25--32},
publisher = {URL}
}
@inproceedings{moosavi-strube-2016-coreference,
title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",
author = \"Moosavi, Nafise Sadat and
Strube, Michael\",
booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",
month = aug,
year = \"2016\",
address = \"Berlin, Germany\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/P16-1060\",
doi = \"10.18653/v1/P16-1060\",
pages = \"632--642\",
}
"""
UpperCAmelCase_ : Tuple = """\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].
This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column Type Description
1 Document ID This is a variation on the document filename
2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3 Word number
4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5 Part-of-Speech
6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.
7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"
8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.
9 Word sense This is the word sense of the word in Column 3.
10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11 Named Entities These columns identifies the spans representing various named entities.
12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N Coreference Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html
Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md
CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
"""
UpperCAmelCase_ : Union[str, Any] = """
Calculates coreference evaluation metrics.
Args:
predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
Each prediction is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
Each reference is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
keep_singletons: After extracting all mentions of key or system files,
mentions whose corresponding coreference chain is of size one,
are considered as singletons. The default evaluation mode will include
singletons in evaluations if they are included in the key or the system files.
By setting 'keep_singletons=False', all singletons in the key and system files
will be excluded from the evaluation.
NP_only: Most of the recent coreference resolvers only resolve NP mentions and
leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.
min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.
Minimum spans are determined using the MINA algorithm.
Returns:
'mentions': mentions
'muc': MUC metric [Vilain et al, 1995]
'bcub': B-cubed [Bagga and Baldwin, 1998]
'ceafe': CEAFe [Luo et al., 2005]
'lea': LEA [Moosavi and Strube, 2016]
'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)
Examples:
>>> coval = datasets.load_metric('coval')
>>> words = ['bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -',
... 'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)',
... 'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)',
... 'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -',
... 'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -',
... 'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -']
>>> references = [words]
>>> predictions = [words]
>>> results = coval.compute(predictions=predictions, references=references)
>>> print(results) # doctest:+ELLIPSIS
{'mentions/recall': 1.0,[...] 'conll_score': 100.0}
"""
def _A (__a , __a , __a=False , __a=False , __a=True , __a=False , __a="dummy_doc" ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = {doc: key_lines}
SCREAMING_SNAKE_CASE_ : List[str] = {doc: sys_lines}
SCREAMING_SNAKE_CASE_ : Dict = {}
SCREAMING_SNAKE_CASE_ : Dict = 0
SCREAMING_SNAKE_CASE_ : List[str] = 0
SCREAMING_SNAKE_CASE_ : Tuple = 0
SCREAMING_SNAKE_CASE_ : int = 0
SCREAMING_SNAKE_CASE_ : List[str] = 0
SCREAMING_SNAKE_CASE_ : Any = 0
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = reader.get_doc_mentions(__a , key_doc_lines[doc] , __a )
key_singletons_num += singletons_num
if NP_only or min_span:
SCREAMING_SNAKE_CASE_ : Optional[Any] = reader.set_annotated_parse_trees(__a , key_doc_lines[doc] , __a , __a )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = reader.get_doc_mentions(__a , sys_doc_lines[doc] , __a )
sys_singletons_num += singletons_num
if NP_only or min_span:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = reader.set_annotated_parse_trees(__a , key_doc_lines[doc] , __a , __a )
if remove_nested:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[str] = reader.remove_nested_coref_mentions(__a , __a )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[str] = reader.remove_nested_coref_mentions(__a , __a )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
SCREAMING_SNAKE_CASE_ : Optional[Any] = reader.get_mention_assignments(__a , __a )
SCREAMING_SNAKE_CASE_ : Optional[Any] = reader.get_mention_assignments(__a , __a )
SCREAMING_SNAKE_CASE_ : Optional[Any] = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
'''Number of removed nested coreferring mentions in the key '''
f'annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}' )
logger.info(
'''Number of resulting singleton clusters in the key '''
f'annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}' )
if not keep_singletons:
logger.info(
f'{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '
'''files, respectively''' )
return doc_coref_infos
def _A (__a , __a , __a , __a , __a , __a , __a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = get_coref_infos(__a , __a , __a , __a , __a , __a )
SCREAMING_SNAKE_CASE_ : str = {}
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 0
SCREAMING_SNAKE_CASE_ : str = 0
for name, metric in metrics:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = evaluator.evaluate_documents(__a , __a , beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({f'{name}/recall': recall, f'{name}/precision': precision, f'{name}/f1': fa} )
logger.info(
name.ljust(10 ) , f'Recall: {recall * 1_00:.2f}' , f' Precision: {precision * 1_00:.2f}' , f' F1: {fa * 1_00:.2f}' , )
if conll_subparts_num == 3:
SCREAMING_SNAKE_CASE_ : Tuple = (conll / 3) * 1_00
logger.info(f'CoNLL score: {conll:.2f}' )
output_scores.update({'''conll_score''': conll} )
return output_scores
def _A (__a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = False
for line in key_lines:
if not line.startswith('''#''' ):
if len(line.split() ) > 6:
SCREAMING_SNAKE_CASE_ : Any = line.split()[5]
if not parse_col == "-":
SCREAMING_SNAKE_CASE_ : Any = True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase__ ( datasets.Metric ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''')),
'''references''': datasets.Sequence(datasets.Value('''string''')),
}) , codebase_urls=['''https://github.com/ns-moosavi/coval'''] , reference_urls=[
'''https://github.com/ns-moosavi/coval''',
'''https://www.aclweb.org/anthology/P16-1060''',
'''http://www.conll.cemantix.org/2012/data.html''',
] , )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Dict=True , lowercase_ : Optional[Any]=False , lowercase_ : Optional[Any]=False , lowercase_ : Dict=False):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = [
('''mentions''', evaluator.mentions),
('''muc''', evaluator.muc),
('''bcub''', evaluator.b_cubed),
('''ceafe''', evaluator.ceafe),
('''lea''', evaluator.lea),
]
if min_span:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = util.check_gold_parse_annotation(lowercase_)
if not has_gold_parse:
raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.''')
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
SCREAMING_SNAKE_CASE_ : Optional[Any] = evaluate(
key_lines=lowercase_ , sys_lines=lowercase_ , metrics=lowercase_ , NP_only=lowercase_ , remove_nested=lowercase_ , keep_singletons=lowercase_ , min_span=lowercase_ , )
return score
| 91 | 1 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def snake_case_ ( lowerCAmelCase_ )-> tuple:
'''simple docstring'''
return (data["data"], data["target"])
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> np.ndarray:
'''simple docstring'''
_UpperCAmelCase : List[Any] = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(lowerCAmelCase_ , lowerCAmelCase_ )
# Predict target for test data
_UpperCAmelCase : List[Any] = xgb.predict(lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = predictions.reshape(len(lowerCAmelCase_ ) , 1 )
return predictions
def snake_case_ ( )-> None:
'''simple docstring'''
_UpperCAmelCase : int = fetch_california_housing()
_UpperCAmelCase ,_UpperCAmelCase : str = data_handling(lowerCAmelCase_ )
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Dict = train_test_split(
lowerCAmelCase_ , lowerCAmelCase_ , test_size=0.2_5 , random_state=1 )
_UpperCAmelCase : str = xgboost(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# Error printing
print(F'''Mean Absolute Error : {mean_absolute_error(lowerCAmelCase_ , lowerCAmelCase_ )}''' )
print(F'''Mean Square Error : {mean_squared_error(lowerCAmelCase_ , lowerCAmelCase_ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 349 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def snake_case_ ( )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
_UpperCAmelCase : str = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(lowerCAmelCase_ )
# Let's go
_UpperCAmelCase : Union[str, Any] = parser.parse_args()
if not hasattr(lowerCAmelCase_ , """func""" ):
parser.print_help()
exit(1 )
# Run
_UpperCAmelCase : Optional[int] = args.func(lowerCAmelCase_ )
service.run()
if __name__ == "__main__":
main()
| 349 | 1 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase=False):
SCREAMING_SNAKE_CASE = OmegaConf.load(_UpperCAmelCase)
if display:
print(yaml.dump(OmegaConf.to_container(_UpperCAmelCase)))
return config
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=None):
if conf_path is None:
SCREAMING_SNAKE_CASE = './model_checkpoints/vqgan_only.yaml'
SCREAMING_SNAKE_CASE = load_config(_UpperCAmelCase , display=_UpperCAmelCase)
SCREAMING_SNAKE_CASE = VQModel(**config.model.params)
if ckpt_path is None:
SCREAMING_SNAKE_CASE = './model_checkpoints/vqgan_only.pt'
SCREAMING_SNAKE_CASE = torch.load(_UpperCAmelCase , map_location=_UpperCAmelCase)
if ".ckpt" in ckpt_path:
SCREAMING_SNAKE_CASE = sd['state_dict']
model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase)
model.to(_UpperCAmelCase)
del sd
return model
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = model.encode(_UpperCAmelCase)
print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''')
SCREAMING_SNAKE_CASE = model.decode(_UpperCAmelCase)
return xrec
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase=False):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = string.rsplit('.' , 1)
if reload:
SCREAMING_SNAKE_CASE = importlib.import_module(_UpperCAmelCase)
importlib.reload(_UpperCAmelCase)
return getattr(importlib.import_module(_UpperCAmelCase , package=_UpperCAmelCase) , cls)
def lowerCamelCase__ (_UpperCAmelCase):
if "target" not in config:
raise KeyError('Expected key `target` to instantiate.')
return get_obj_from_str(config['target'])(**config.get('params' , {}))
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=True , _UpperCAmelCase=True):
SCREAMING_SNAKE_CASE = instantiate_from_config(_UpperCAmelCase)
if sd is not None:
model.load_state_dict(_UpperCAmelCase)
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
# load the specified checkpoint
if ckpt:
SCREAMING_SNAKE_CASE = torch.load(_UpperCAmelCase , map_location='cpu')
SCREAMING_SNAKE_CASE = pl_sd['global_step']
print(F'''loaded model from global step {global_step}.''')
else:
SCREAMING_SNAKE_CASE = {'state_dict': None}
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = load_model_from_config(config.model , pl_sd['state_dict'] , gpu=_UpperCAmelCase , eval_mode=_UpperCAmelCase)['model']
return model, global_step
| 137 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def lowerCamelCase__ (_UpperCAmelCase=None):
if subparsers is not None:
SCREAMING_SNAKE_CASE = subparsers.add_parser('env')
else:
SCREAMING_SNAKE_CASE = argparse.ArgumentParser('Accelerate env command')
parser.add_argument(
'--config_file' , default=_UpperCAmelCase , help='The config file to use for the default values in the launching script.')
if subparsers is not None:
parser.set_defaults(func=_UpperCAmelCase)
return parser
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = torch.__version__
SCREAMING_SNAKE_CASE = torch.cuda.is_available()
SCREAMING_SNAKE_CASE = is_xpu_available()
SCREAMING_SNAKE_CASE = is_npu_available()
SCREAMING_SNAKE_CASE = 'Not found'
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(_UpperCAmelCase):
SCREAMING_SNAKE_CASE = load_config_from_file(args.config_file).to_dict()
SCREAMING_SNAKE_CASE = {
'`Accelerate` version': version,
'Platform': platform.platform(),
'Python version': platform.python_version(),
'Numpy version': np.__version__,
'PyTorch version (GPU?)': F'''{pt_version} ({pt_cuda_available})''',
'PyTorch XPU available': str(_UpperCAmelCase),
'PyTorch NPU available': str(_UpperCAmelCase),
'System RAM': F'''{psutil.virtual_memory().total / 1024 ** 3:.2f} GB''',
}
if pt_cuda_available:
SCREAMING_SNAKE_CASE = torch.cuda.get_device_name()
print('\nCopy-and-paste the text below in your GitHub issue\n')
print('\n'.join([F'''- {prop}: {val}''' for prop, val in info.items()]))
print('- `Accelerate` default config:' if args.config_file is None else '- `Accelerate` config passed:')
SCREAMING_SNAKE_CASE = (
'\n'.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()])
if isinstance(_UpperCAmelCase , _UpperCAmelCase)
else F'''\t{accelerate_config}'''
)
print(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = accelerate_config
return info
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = env_command_parser()
SCREAMING_SNAKE_CASE = parser.parse_args()
env_command(_UpperCAmelCase)
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 137 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"""microsoft/swinv2-tiny-patch4-window8-256""": (
"""https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"""
),
}
class a_ ( a_ ):
'''simple docstring'''
__a: List[str] = '''swinv2'''
__a: Any = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , lowercase_=2_2_4 , lowercase_=4 , lowercase_=3 , lowercase_=9_6 , lowercase_=[2, 2, 6, 2] , lowercase_=[3, 6, 1_2, 2_4] , lowercase_=7 , lowercase_=4.0 , lowercase_=True , lowercase_=0.0 , lowercase_=0.0 , lowercase_=0.1 , lowercase_="gelu" , lowercase_=False , lowercase_=0.02 , lowercase_=1e-5 , lowercase_=3_2 , **lowercase_ , ) -> List[Any]:
'''simple docstring'''
super().__init__(**lowercase_ )
lowerCAmelCase_ : int = image_size
lowerCAmelCase_ : Optional[Any] = patch_size
lowerCAmelCase_ : str = num_channels
lowerCAmelCase_ : Optional[Any] = embed_dim
lowerCAmelCase_ : str = depths
lowerCAmelCase_ : Optional[int] = len(lowercase_ )
lowerCAmelCase_ : Tuple = num_heads
lowerCAmelCase_ : Optional[Any] = window_size
lowerCAmelCase_ : Any = mlp_ratio
lowerCAmelCase_ : Optional[Any] = qkv_bias
lowerCAmelCase_ : int = hidden_dropout_prob
lowerCAmelCase_ : Dict = attention_probs_dropout_prob
lowerCAmelCase_ : Tuple = drop_path_rate
lowerCAmelCase_ : Optional[int] = hidden_act
lowerCAmelCase_ : Optional[int] = use_absolute_embeddings
lowerCAmelCase_ : Dict = layer_norm_eps
lowerCAmelCase_ : Dict = initializer_range
lowerCAmelCase_ : int = encoder_stride
# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCAmelCase_ : Optional[Any] = int(embed_dim * 2 ** (len(lowercase_ ) - 1) )
lowerCAmelCase_ : str = (0, 0, 0, 0)
| 362 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
lowerCamelCase_ = logging.get_logger(__name__)
@add_end_docstrings(a_ )
class a_ ( a_ ):
'''simple docstring'''
def __init__( self , *lowercase_ , **lowercase_ ) -> Any:
'''simple docstring'''
super().__init__(*lowercase_ , **lowercase_ )
self.check_model_type(lowercase_ )
def _lowercase ( self , lowercase_=None , lowercase_=None , lowercase_=None , **lowercase_ ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ = {}, {}
if padding is not None:
lowerCAmelCase_ = padding
if truncation is not None:
lowerCAmelCase_ = truncation
if top_k is not None:
lowerCAmelCase_ = top_k
return preprocess_params, {}, postprocess_params
def __call__( self , lowercase_ , lowercase_ = None , **lowercase_ ) -> int:
'''simple docstring'''
if isinstance(lowercase_ , (Image.Image, str) ) and isinstance(lowercase_ , lowercase_ ):
lowerCAmelCase_ = {'image': image, 'question': question}
else:
lowerCAmelCase_ = image
lowerCAmelCase_ = super().__call__(lowercase_ , **lowercase_ )
return results
def _lowercase ( self , lowercase_ , lowercase_=False , lowercase_=False ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ = load_image(inputs['image'] )
lowerCAmelCase_ = self.tokenizer(
inputs['question'] , return_tensors=self.framework , padding=lowercase_ , truncation=lowercase_ )
lowerCAmelCase_ = self.image_processor(images=lowercase_ , return_tensors=self.framework )
model_inputs.update(lowercase_ )
return model_inputs
def _lowercase ( self , lowercase_ ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ = self.model(**lowercase_ )
return model_outputs
def _lowercase ( self , lowercase_ , lowercase_=5 ) -> Any:
'''simple docstring'''
if top_k > self.model.config.num_labels:
lowerCAmelCase_ = self.model.config.num_labels
if self.framework == "pt":
lowerCAmelCase_ = model_outputs.logits.sigmoid()[0]
lowerCAmelCase_ , lowerCAmelCase_ = probs.topk(lowercase_ )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowerCAmelCase_ = scores.tolist()
lowerCAmelCase_ = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_ , lowercase_ )]
| 14 | 0 |
from __future__ import annotations
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[Any] = TypeError(
'''Matrices must be formed from a list of zero or more lists containing at '''
'''least one and the same number of values, each of which must be of type '''
'''int or float.''' )
if len(SCREAMING_SNAKE_CASE__ ) != 0:
lowercase : int = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(SCREAMING_SNAKE_CASE__ ) != cols:
raise error
for value in row:
if not isinstance(SCREAMING_SNAKE_CASE__ , (int, float) ):
raise error
lowercase : List[Any] = rows
else:
lowercase : List[str] = []
def __lowerCamelCase ( self ):
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def __lowerCamelCase ( self ):
return len(self.rows )
@property
def __lowerCamelCase ( self ):
return len(self.rows[0] )
@property
def __lowerCamelCase ( self ):
return (self.num_rows, self.num_columns)
@property
def __lowerCamelCase ( self ):
return self.order[0] == self.order[1]
def __lowerCamelCase ( self ):
lowercase : Dict = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def __lowerCamelCase ( self ):
return bool(self.determinant() )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(SCREAMING_SNAKE_CASE__ ).determinant()
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if (row + column) % 2 == 0:
return self.get_minor(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return -1 * self.get_minor(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
return Matrix(
[
[self.get_minor(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def __lowerCamelCase ( self ):
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def __lowerCamelCase ( self ):
lowercase : int = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Dict = self.determinant()
if not determinant:
raise TypeError('''Only matrices with a non-zero determinant have an inverse''' )
return self.adjugate() * (1 / determinant)
def __repr__( self ):
return str(self.rows )
def __str__( self ):
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
'''[''' + '''. '''.join([str(SCREAMING_SNAKE_CASE__ ) for value in row] ) + '''.]'''
for row in self.rows
] )
+ "]"
)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : str = TypeError('''Row must be a list containing all ints and/or floats''' )
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise type_error
for value in row:
if not isinstance(SCREAMING_SNAKE_CASE__ , (int, float) ):
raise type_error
if len(SCREAMING_SNAKE_CASE__ ) != self.num_columns:
raise ValueError(
'''Row must be equal in length to the other rows in the matrix''' )
if position is None:
self.rows.append(SCREAMING_SNAKE_CASE__ )
else:
lowercase : Union[str, Any] = self.rows[0:position] + [row] + self.rows[position:]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : Dict = TypeError(
'''Column must be a list containing all ints and/or floats''' )
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise type_error
for value in column:
if not isinstance(SCREAMING_SNAKE_CASE__ , (int, float) ):
raise type_error
if len(SCREAMING_SNAKE_CASE__ ) != self.num_rows:
raise ValueError(
'''Column must be equal in length to the other columns in the matrix''' )
if position is None:
lowercase : Any = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
lowercase : Optional[Any] = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self , SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return NotImplemented
return self.rows == other.rows
def __ne__( self , SCREAMING_SNAKE_CASE__ ):
return not self == other
def __neg__( self ):
return self * -1
def __add__( self , SCREAMING_SNAKE_CASE__ ):
if self.order != other.order:
raise ValueError('''Addition requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self , SCREAMING_SNAKE_CASE__ ):
if self.order != other.order:
raise ValueError('''Subtraction requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self , SCREAMING_SNAKE_CASE__ ):
if isinstance(SCREAMING_SNAKE_CASE__ , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if self.num_columns != other.num_rows:
raise ValueError(
'''The number of columns in the first matrix must '''
'''be equal to the number of rows in the second''' )
return Matrix(
[
[Matrix.dot_product(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
'''A Matrix can only be multiplied by an int, float, or another matrix''' )
def __pow__( self , SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError('''A Matrix can only be raised to the power of an int''' )
if not self.is_square:
raise ValueError('''Only square matrices can be raised to a power''' )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
'''Only invertable matrices can be raised to a negative power''' )
lowercase : int = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def __lowerCamelCase ( cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return sum(row[i] * column[i] for i in range(len(SCREAMING_SNAKE_CASE__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
import logging
import os
from .state import PartialState
class __SCREAMING_SNAKE_CASE ( logging.LoggerAdapter ):
@staticmethod
def __lowerCamelCase ( SCREAMING_SNAKE_CASE__ ):
lowercase : List[Any] = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
lowercase : List[str] = kwargs.pop('''main_process_only''' , SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = kwargs.pop('''in_order''' , SCREAMING_SNAKE_CASE__ )
if self.isEnabledFor(SCREAMING_SNAKE_CASE__ ):
if self._should_log(SCREAMING_SNAKE_CASE__ ):
lowercase , lowercase : str = self.process(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.logger.log(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
elif in_order:
lowercase : List[Any] = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
lowercase , lowercase : Union[str, Any] = self.process(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.logger.log(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
state.wait_for_everyone()
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None ) ->List[Any]:
"""simple docstring"""
if log_level is None:
lowercase : str = os.environ.get('''ACCELERATE_LOG_LEVEL''', _UpperCamelCase )
lowercase : str = logging.getLogger(_UpperCamelCase )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(_UpperCamelCase, {} )
| 337 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowercase : Union[str, Any] = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = """gptj"""
__lowercase = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowerCAmelCase_=5_04_00 , lowerCAmelCase_=20_48 , lowerCAmelCase_=40_96 , lowerCAmelCase_=28 , lowerCAmelCase_=16 , lowerCAmelCase_=64 , lowerCAmelCase_=None , lowerCAmelCase_="gelu_new" , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=1E-5 , lowerCAmelCase_=0.02 , lowerCAmelCase_=True , lowerCAmelCase_=5_02_56 , lowerCAmelCase_=5_02_56 , lowerCAmelCase_=False , **lowerCAmelCase_ , ):
"""simple docstring"""
_snake_case = vocab_size
_snake_case = n_positions
_snake_case = n_embd
_snake_case = n_layer
_snake_case = n_head
_snake_case = n_inner
_snake_case = rotary_dim
_snake_case = activation_function
_snake_case = resid_pdrop
_snake_case = embd_pdrop
_snake_case = attn_pdrop
_snake_case = layer_norm_epsilon
_snake_case = initializer_range
_snake_case = use_cache
_snake_case = bos_token_id
_snake_case = eos_token_id
super().__init__(
bos_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , tie_word_embeddings=lowerCAmelCase_ , **lowerCAmelCase_ )
class __UpperCAmelCase ( _lowerCamelCase ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ = "default" , lowerCAmelCase_ = None , lowerCAmelCase_ = False , ):
"""simple docstring"""
super().__init__(lowerCAmelCase_ , task=lowerCAmelCase_ , patching_specs=lowerCAmelCase_ , use_past=lowerCAmelCase_ )
if not getattr(self._config , 'pad_token_id' , lowerCAmelCase_ ):
# TODO: how to do that better?
_snake_case = 0
@property
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(lowerCAmelCase_ , direction='inputs' )
_snake_case = {0: 'batch', 1: 'past_sequence + sequence'}
else:
_snake_case = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def lowerCamelCase ( self ):
"""simple docstring"""
return self._config.n_layer
@property
def lowerCamelCase ( self ):
"""simple docstring"""
return self._config.n_head
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = -1 , lowerCAmelCase_ = -1 , lowerCAmelCase_ = False , lowerCAmelCase_ = None , ):
"""simple docstring"""
_snake_case = super(lowerCAmelCase_ , self ).generate_dummy_inputs(
lowerCAmelCase_ , batch_size=lowerCAmelCase_ , seq_length=lowerCAmelCase_ , is_pair=lowerCAmelCase_ , framework=lowerCAmelCase_ )
# We need to order the input in the way they appears in the forward()
_snake_case = OrderedDict({'input_ids': common_inputs['input_ids']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
_snake_case , _snake_case = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
_snake_case = seqlen + 2
_snake_case = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_snake_case = [
(torch.zeros(lowerCAmelCase_ ), torch.zeros(lowerCAmelCase_ )) for _ in range(self.num_layers )
]
_snake_case = common_inputs['attention_mask']
if self.use_past:
_snake_case = ordered_inputs['attention_mask'].dtype
_snake_case = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(lowerCAmelCase_ , lowerCAmelCase_ , dtype=lowerCAmelCase_ )] , dim=1 )
return ordered_inputs
@property
def lowerCamelCase ( self ):
"""simple docstring"""
return 13
| 160 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"pipelines_utils",
"0.22.0",
"Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
standard_warn=False,
stacklevel=3,
)
| 160 | 1 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def A_ ( A__ , A__=1 ) -> Any:
if n_shave_prefix_segments >= 0:
return ".".join(path.split('.' )[n_shave_prefix_segments:] )
else:
return ".".join(path.split('.' )[:n_shave_prefix_segments] )
def A_ ( A__ , A__=0 ) -> Optional[Any]:
a__ : List[str] = []
for old_item in old_list:
a__ : str = old_item.replace('in_layers.0' , 'norm1' )
a__ : List[Any] = new_item.replace('in_layers.2' , 'conv1' )
a__ : str = new_item.replace('out_layers.0' , 'norm2' )
a__ : Optional[int] = new_item.replace('out_layers.3' , 'conv2' )
a__ : Optional[int] = new_item.replace('emb_layers.1' , 'time_emb_proj' )
a__ : List[Any] = new_item.replace('skip_connection' , 'conv_shortcut' )
a__ : Tuple = shave_segments(A__ , n_shave_prefix_segments=A__ )
mapping.append({'old': old_item, 'new': new_item} )
return mapping
def A_ ( A__ , A__=0 ) -> List[Any]:
a__ : Optional[int] = []
for old_item in old_list:
a__ : str = old_item
a__ : List[Any] = new_item.replace('norm.weight' , 'group_norm.weight' )
a__ : Union[str, Any] = new_item.replace('norm.bias' , 'group_norm.bias' )
a__ : List[Any] = new_item.replace('proj_out.weight' , 'proj_attn.weight' )
a__ : List[Any] = new_item.replace('proj_out.bias' , 'proj_attn.bias' )
a__ : Union[str, Any] = shave_segments(A__ , n_shave_prefix_segments=A__ )
mapping.append({'old': old_item, 'new': new_item} )
return mapping
def A_ ( A__ , A__ , A__ , A__=None , A__=None , A__=None ) -> Any:
assert isinstance(A__ , A__ ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
a__ : Dict = old_checkpoint[path]
a__ : Any = old_tensor.shape[0] // 3
a__ : str = (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
a__ : Optional[Any] = old_tensor.shape[0] // config['num_head_channels'] // 3
a__ : int = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
a__ , a__ , a__ : Any = old_tensor.split(channels // num_heads , dim=1 )
a__ : Optional[int] = query.reshape(A__ )
a__ : Union[str, Any] = key.reshape(A__ )
a__ : int = value.reshape(A__ )
for path in paths:
a__ : int = path['new']
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
a__ : Dict = new_path.replace('middle_block.0' , 'mid_block.resnets.0' )
a__ : Optional[Any] = new_path.replace('middle_block.1' , 'mid_block.attentions.0' )
a__ : List[str] = new_path.replace('middle_block.2' , 'mid_block.resnets.1' )
if additional_replacements is not None:
for replacement in additional_replacements:
a__ : str = new_path.replace(replacement['old'] , replacement['new'] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
a__ : List[str] = old_checkpoint[path['old']][:, :, 0]
else:
a__ : Optional[Any] = old_checkpoint[path['old']]
def A_ ( A__ , A__ ) -> Optional[Any]:
a__ : Any = {}
a__ : Union[str, Any] = checkpoint['time_embed.0.weight']
a__ : Tuple = checkpoint['time_embed.0.bias']
a__ : Optional[Any] = checkpoint['time_embed.2.weight']
a__ : int = checkpoint['time_embed.2.bias']
a__ : List[str] = checkpoint['input_blocks.0.0.weight']
a__ : Tuple = checkpoint['input_blocks.0.0.bias']
a__ : Union[str, Any] = checkpoint['out.0.weight']
a__ : Tuple = checkpoint['out.0.bias']
a__ : int = checkpoint['out.2.weight']
a__ : List[str] = checkpoint['out.2.bias']
# Retrieves the keys for the input blocks only
a__ : List[str] = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'input_blocks' in layer} )
a__ : Tuple = {
layer_id: [key for key in checkpoint if F'input_blocks.{layer_id}' in key]
for layer_id in range(A__ )
}
# Retrieves the keys for the middle blocks only
a__ : Optional[int] = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'middle_block' in layer} )
a__ : Optional[int] = {
layer_id: [key for key in checkpoint if F'middle_block.{layer_id}' in key]
for layer_id in range(A__ )
}
# Retrieves the keys for the output blocks only
a__ : Any = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'output_blocks' in layer} )
a__ : Union[str, Any] = {
layer_id: [key for key in checkpoint if F'output_blocks.{layer_id}' in key]
for layer_id in range(A__ )
}
for i in range(1 , A__ ):
a__ : Tuple = (i - 1) // (config['num_res_blocks'] + 1)
a__ : Optional[int] = (i - 1) % (config['num_res_blocks'] + 1)
a__ : Any = [key for key in input_blocks[i] if F'input_blocks.{i}.0' in key]
a__ : Union[str, Any] = [key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]
if F'input_blocks.{i}.0.op.weight' in checkpoint:
a__ : Union[str, Any] = checkpoint[
F'input_blocks.{i}.0.op.weight'
]
a__ : Optional[Any] = checkpoint[
F'input_blocks.{i}.0.op.bias'
]
continue
a__ : Dict = renew_resnet_paths(A__ )
a__ : Optional[Any] = {'old': F'input_blocks.{i}.0', 'new': F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
a__ : List[str] = {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
assign_to_checkpoint(
A__ , A__ , A__ , additional_replacements=[meta_path, resnet_op] , config=A__ )
if len(A__ ):
a__ : Union[str, Any] = renew_attention_paths(A__ )
a__ : str = {
'old': F'input_blocks.{i}.1',
'new': F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
}
a__ : List[str] = {
F'input_blocks.{i}.1.qkv.bias': {
'key': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
'query': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
'value': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
},
F'input_blocks.{i}.1.qkv.weight': {
'key': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
'query': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
'value': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
},
}
assign_to_checkpoint(
A__ , A__ , A__ , additional_replacements=[meta_path] , attention_paths_to_split=A__ , config=A__ , )
a__ : List[Any] = middle_blocks[0]
a__ : str = middle_blocks[1]
a__ : Optional[int] = middle_blocks[2]
a__ : Dict = renew_resnet_paths(A__ )
assign_to_checkpoint(A__ , A__ , A__ , config=A__ )
a__ : List[str] = renew_resnet_paths(A__ )
assign_to_checkpoint(A__ , A__ , A__ , config=A__ )
a__ : int = renew_attention_paths(A__ )
a__ : Tuple = {
'middle_block.1.qkv.bias': {
'key': 'mid_block.attentions.0.key.bias',
'query': 'mid_block.attentions.0.query.bias',
'value': 'mid_block.attentions.0.value.bias',
},
'middle_block.1.qkv.weight': {
'key': 'mid_block.attentions.0.key.weight',
'query': 'mid_block.attentions.0.query.weight',
'value': 'mid_block.attentions.0.value.weight',
},
}
assign_to_checkpoint(
A__ , A__ , A__ , attention_paths_to_split=A__ , config=A__ )
for i in range(A__ ):
a__ : Optional[int] = i // (config['num_res_blocks'] + 1)
a__ : Optional[int] = i % (config['num_res_blocks'] + 1)
a__ : Tuple = [shave_segments(A__ , 2 ) for name in output_blocks[i]]
a__ : int = {}
for layer in output_block_layers:
a__ , a__ : Tuple = layer.split('.' )[0], shave_segments(A__ , 1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(A__ )
else:
a__ : Union[str, Any] = [layer_name]
if len(A__ ) > 1:
a__ : Tuple = [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
a__ : Union[str, Any] = [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]
a__ : int = renew_resnet_paths(A__ )
a__ : str = renew_resnet_paths(A__ )
a__ : str = {'old': F'output_blocks.{i}.0', 'new': F'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
assign_to_checkpoint(A__ , A__ , A__ , additional_replacements=[meta_path] , config=A__ )
if ["conv.weight", "conv.bias"] in output_block_list.values():
a__ : Union[str, Any] = list(output_block_list.values() ).index(['conv.weight', 'conv.bias'] )
a__ : Optional[Any] = checkpoint[
F'output_blocks.{i}.{index}.conv.weight'
]
a__ : List[str] = checkpoint[
F'output_blocks.{i}.{index}.conv.bias'
]
# Clear attentions as they have been attributed above.
if len(A__ ) == 2:
a__ : Optional[int] = []
if len(A__ ):
a__ : List[str] = renew_attention_paths(A__ )
a__ : List[Any] = {
'old': F'output_blocks.{i}.1',
'new': F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
}
a__ : Optional[Any] = {
F'output_blocks.{i}.1.qkv.bias': {
'key': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
'query': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
'value': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
},
F'output_blocks.{i}.1.qkv.weight': {
'key': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
'query': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
'value': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
},
}
assign_to_checkpoint(
A__ , A__ , A__ , additional_replacements=[meta_path] , attention_paths_to_split=to_split if any('qkv' in key for key in attentions ) else None , config=A__ , )
else:
a__ : Optional[Any] = renew_resnet_paths(A__ , n_shave_prefix_segments=1 )
for path in resnet_0_paths:
a__ : str = '.'.join(['output_blocks', str(A__ ), path['old']] )
a__ : Union[str, Any] = '.'.join(['up_blocks', str(A__ ), 'resnets', str(A__ ), path['new']] )
a__ : List[str] = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
lowercase : int = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help="""The config json file corresponding to the architecture.""",
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
lowercase : str = parser.parse_args()
lowercase : Optional[int] = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
lowercase : Tuple = json.loads(f.read())
lowercase : Optional[Any] = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
lowercase : int = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
lowercase : Tuple = DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
lowercase : List[Any] = VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
lowercase : int = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 99 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
# Base Case
if curr_ind == len(UpperCAmelCase ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(UpperCAmelCase ) ):
if valid_connection(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
# Insert current vertex into path as next transition
snake_case_ = next_ver
# Validate created path
if util_hamilton_cycle(UpperCAmelCase , UpperCAmelCase , curr_ind + 1 ):
return True
# Backtrack
snake_case_ = -1
return False
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase = 0 ) -> list[int]:
snake_case_ = [-1] * (len(UpperCAmelCase ) + 1)
# initialize start and end of path with starting index
snake_case_ = snake_case_ = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(UpperCAmelCase , UpperCAmelCase , 1 ) else []
| 69 | 0 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
# Construct model
if openai_config_file == "":
lowerCamelCase_ = OpenAIGPTConfig()
else:
lowerCamelCase_ = OpenAIGPTConfig.from_json_file(lowerCamelCase__ )
lowerCamelCase_ = OpenAIGPTModel(lowerCamelCase__ )
# Load weights from numpy
load_tf_weights_in_openai_gpt(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model
lowerCamelCase_ = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
lowerCamelCase_ = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F'Save PyTorch model to {pytorch_weights_dump_path}' )
torch.save(model.state_dict() , lowerCamelCase__ )
print(F'Save configuration file to {pytorch_config_dump_path}' )
with open(lowerCamelCase__ , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--openai_checkpoint_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the TensorFlow checkpoint path.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--openai_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
'''This specifies the model architecture.'''
),
)
__A =parser.parse_args()
convert_openai_checkpoint_to_pytorch(
args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
)
| 47 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/text-classification/requirements.txt''')
__A =logging.getLogger(__name__)
@dataclass
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = field(
default=1_28 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
@dataclass
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = field(
default=snake_case_ , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'} )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={'help': 'Train language if it is different from the evaluation language.'} )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'} , )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
lowerCAmelCase__ = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
lowerCAmelCase__ = field(
default=snake_case_ , metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} , )
def lowerCamelCase_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCamelCase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 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_xnli" , lowerCamelCase__ )
# 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()
lowerCamelCase_ = training_args.get_process_log_level()
logger.setLevel(lowerCamelCase__ )
datasets.utils.logging.set_verbosity(lowerCamelCase__ )
transformers.utils.logging.set_verbosity(lowerCamelCase__ )
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.
lowerCamelCase_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCamelCase_ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. '
"Use --overwrite_output_dir to overcome." )
elif last_checkpoint is not None:
logger.info(
F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
lowerCamelCase_ = load_dataset(
"xnli" , model_args.language , split="train" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
lowerCamelCase_ = load_dataset(
"xnli" , model_args.train_language , split="train" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCamelCase_ = train_dataset.features["label"].names
if training_args.do_eval:
lowerCamelCase_ = load_dataset(
"xnli" , model_args.language , split="validation" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCamelCase_ = eval_dataset.features["label"].names
if training_args.do_predict:
lowerCamelCase_ = load_dataset(
"xnli" , model_args.language , split="test" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCamelCase_ = predict_dataset.features["label"].names
# Labels
lowerCamelCase_ = len(lowerCamelCase__ )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCamelCase_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowerCamelCase__ , idalabel={str(lowerCamelCase__ ): label for i, label in enumerate(lowerCamelCase__ )} , labelaid={label: i for i, label in enumerate(lowerCamelCase__ )} , finetuning_task="xnli" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowerCamelCase_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , 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 , )
lowerCamelCase_ = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=lowerCamelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
lowerCamelCase_ = "max_length"
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
lowerCamelCase_ = False
def preprocess_function(lowerCamelCase__ ):
# Tokenize the texts
return tokenizer(
examples["premise"] , examples["hypothesis"] , padding=lowerCamelCase__ , max_length=data_args.max_seq_length , truncation=lowerCamelCase__ , )
if training_args.do_train:
if data_args.max_train_samples is not None:
lowerCamelCase_ = min(len(lowerCamelCase__ ) , data_args.max_train_samples )
lowerCamelCase_ = train_dataset.select(range(lowerCamelCase__ ) )
with training_args.main_process_first(desc="train dataset map pre-processing" ):
lowerCamelCase_ = train_dataset.map(
lowerCamelCase__ , batched=lowerCamelCase__ , load_from_cache_file=not data_args.overwrite_cache , desc="Running tokenizer on train dataset" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(lowerCamelCase__ ) ) , 3 ):
logger.info(F'Sample {index} of the training set: {train_dataset[index]}.' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowerCamelCase_ = min(len(lowerCamelCase__ ) , data_args.max_eval_samples )
lowerCamelCase_ = eval_dataset.select(range(lowerCamelCase__ ) )
with training_args.main_process_first(desc="validation dataset map pre-processing" ):
lowerCamelCase_ = eval_dataset.map(
lowerCamelCase__ , batched=lowerCamelCase__ , load_from_cache_file=not data_args.overwrite_cache , desc="Running tokenizer on validation dataset" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
lowerCamelCase_ = min(len(lowerCamelCase__ ) , data_args.max_predict_samples )
lowerCamelCase_ = predict_dataset.select(range(lowerCamelCase__ ) )
with training_args.main_process_first(desc="prediction dataset map pre-processing" ):
lowerCamelCase_ = predict_dataset.map(
lowerCamelCase__ , batched=lowerCamelCase__ , load_from_cache_file=not data_args.overwrite_cache , desc="Running tokenizer on prediction dataset" , )
# Get the metric function
lowerCamelCase_ = evaluate.load("xnli" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowerCamelCase__ ):
lowerCamelCase_ = p.predictions[0] if isinstance(p.predictions , lowerCamelCase__ ) else p.predictions
lowerCamelCase_ = np.argmax(lowerCamelCase__ , axis=1 )
return metric.compute(predictions=lowerCamelCase__ , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
lowerCamelCase_ = default_data_collator
elif training_args.fpaa:
lowerCamelCase_ = DataCollatorWithPadding(lowerCamelCase__ , pad_to_multiple_of=8 )
else:
lowerCamelCase_ = None
# Initialize our Trainer
lowerCamelCase_ = Trainer(
model=lowerCamelCase__ , args=lowerCamelCase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowerCamelCase__ , tokenizer=lowerCamelCase__ , data_collator=lowerCamelCase__ , )
# Training
if training_args.do_train:
lowerCamelCase_ = None
if training_args.resume_from_checkpoint is not None:
lowerCamelCase_ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowerCamelCase_ = last_checkpoint
lowerCamelCase_ = trainer.train(resume_from_checkpoint=lowerCamelCase__ )
lowerCamelCase_ = train_result.metrics
lowerCamelCase_ = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase__ )
)
lowerCamelCase_ = min(lowerCamelCase__ , len(lowerCamelCase__ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train" , lowerCamelCase__ )
trainer.save_metrics("train" , lowerCamelCase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
lowerCamelCase_ = trainer.evaluate(eval_dataset=lowerCamelCase__ )
lowerCamelCase_ = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase__ )
lowerCamelCase_ = min(lowerCamelCase__ , len(lowerCamelCase__ ) )
trainer.log_metrics("eval" , lowerCamelCase__ )
trainer.save_metrics("eval" , lowerCamelCase__ )
# Prediction
if training_args.do_predict:
logger.info("*** Predict ***" )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = trainer.predict(lowerCamelCase__ , metric_key_prefix="predict" )
lowerCamelCase_ = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCamelCase__ )
)
lowerCamelCase_ = min(lowerCamelCase__ , len(lowerCamelCase__ ) )
trainer.log_metrics("predict" , lowerCamelCase__ )
trainer.save_metrics("predict" , lowerCamelCase__ )
lowerCamelCase_ = np.argmax(lowerCamelCase__ , axis=1 )
lowerCamelCase_ = os.path.join(training_args.output_dir , "predictions.txt" )
if trainer.is_world_process_zero():
with open(lowerCamelCase__ , "w" ) as writer:
writer.write("index\tprediction\n" )
for index, item in enumerate(lowerCamelCase__ ):
lowerCamelCase_ = label_list[item]
writer.write(F'{index}\t{item}\n' )
if __name__ == "__main__":
main()
| 47 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
a_ : Optional[int] = R'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n Args:\n title_sep (`str`, *optional*, defaults to `" / "`):\n Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n doc_sep (`str`, *optional*, defaults to `" // "`):\n Separator inserted between the text of the retrieved document and the original input when calling\n [`RagRetriever`].\n n_docs (`int`, *optional*, defaults to 5):\n Number of documents to retrieve.\n max_combined_length (`int`, *optional*, defaults to 300):\n Max length of contextualized input returned by [`~RagRetriever.__call__`].\n retrieval_vector_size (`int`, *optional*, defaults to 768):\n Dimensionality of the document embeddings indexed by [`RagRetriever`].\n retrieval_batch_size (`int`, *optional*, defaults to 8):\n Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n [`RagRetriever`].\n dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n using `datasets.list_datasets()`).\n dataset_split (`str`, *optional*, defaults to `"train"`)\n Which split of the `dataset` to load.\n index_name (`str`, *optional*, defaults to `"compressed"`)\n The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n `"compressed"`.\n index_path (`str`, *optional*)\n The path to the serialized faiss index on disk.\n passages_path (`str`, *optional*):\n A path to text passages compatible with the faiss index. Required if using\n [`~models.rag.retrieval_rag.LegacyIndex`]\n use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n Whether to load a "dummy" variant of the dataset specified by `dataset`.\n label_smoothing (`float`, *optional*, defaults to 0.0):\n Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n in the loss calculation. If set to 0, no label smoothing is performed.\n do_marginalize (`bool`, *optional*, defaults to `False`):\n If `True`, the logits are marginalized over all documents by making use of\n `torch.nn.functional.log_softmax`.\n reduce_loss (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n do_deduplication (`bool`, *optional*, defaults to `True`):\n Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n set to `False` if used while training with distributed backend.\n exclude_bos_score (`bool`, *optional*, defaults to `False`):\n Whether or not to disregard the BOS token when computing the loss.\n output_retrieved(`bool`, *optional*, defaults to `False`):\n If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n `context_attention_mask` are returned. See returned tensors for more detail.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models).\n forced_eos_token_id (`int`, *optional*):\n The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n `eos_token_id`.\n'
@add_start_docstrings(A__ )
class _snake_case ( A__ ):
_lowercase : List[Any] = '''rag'''
_lowercase : str = True
def __init__( self , a=None , a=True , a=None , a=None , a=None , a=None , a=None , a=" / " , a=" // " , a=5 , a=300 , a=768 , a=8 , a="wiki_dpr" , a="train" , a="compressed" , a=None , a=None , a=False , a=False , a=0.0 , a=True , a=False , a=False , a=False , a=True , a=None , **a , ) -> Optional[Any]:
super().__init__(
bos_token_id=a , pad_token_id=a , eos_token_id=a , decoder_start_token_id=a , forced_eos_token_id=a , is_encoder_decoder=a , prefix=a , vocab_size=a , **a , )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
SCREAMING_SNAKE_CASE = kwargs.pop('question_encoder')
SCREAMING_SNAKE_CASE = question_encoder_config.pop('model_type')
SCREAMING_SNAKE_CASE = kwargs.pop('generator')
SCREAMING_SNAKE_CASE = decoder_config.pop('model_type')
from ..auto.configuration_auto import AutoConfig
SCREAMING_SNAKE_CASE = AutoConfig.for_model(a , **a)
SCREAMING_SNAKE_CASE = AutoConfig.for_model(a , **a)
SCREAMING_SNAKE_CASE = reduce_loss
SCREAMING_SNAKE_CASE = label_smoothing
SCREAMING_SNAKE_CASE = exclude_bos_score
SCREAMING_SNAKE_CASE = do_marginalize
SCREAMING_SNAKE_CASE = title_sep
SCREAMING_SNAKE_CASE = doc_sep
SCREAMING_SNAKE_CASE = n_docs
SCREAMING_SNAKE_CASE = max_combined_length
SCREAMING_SNAKE_CASE = dataset
SCREAMING_SNAKE_CASE = dataset_split
SCREAMING_SNAKE_CASE = index_name
SCREAMING_SNAKE_CASE = retrieval_vector_size
SCREAMING_SNAKE_CASE = retrieval_batch_size
SCREAMING_SNAKE_CASE = passages_path
SCREAMING_SNAKE_CASE = index_path
SCREAMING_SNAKE_CASE = use_dummy_dataset
SCREAMING_SNAKE_CASE = output_retrieved
SCREAMING_SNAKE_CASE = do_deduplication
SCREAMING_SNAKE_CASE = use_cache
if self.forced_eos_token_id is None:
SCREAMING_SNAKE_CASE = getattr(self.generator , 'forced_eos_token_id' , a)
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , a , a , **a) -> PretrainedConfig:
return cls(question_encoder=question_encoder_config.to_dict() , generator=generator_config.to_dict() , **a)
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[int]:
SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__)
SCREAMING_SNAKE_CASE = self.question_encoder.to_dict()
SCREAMING_SNAKE_CASE = self.generator.to_dict()
SCREAMING_SNAKE_CASE = self.__class__.model_type
return output
| 137 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
a_ : Dict = 'hf-internal-testing/tiny-random-bert'
a_ : Tuple = os.path.join(TRANSFORMERS_CACHE, 'models--hf-internal-testing--tiny-random-bert')
a_ : Optional[int] = '9b8c223d42b2188cb49d29af482996f9d0f3e5a6'
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
SCREAMING_SNAKE_CASE = cached_file(a , a)
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(a))
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(a , a)))
with open(os.path.join(a , 'refs' , 'main')) as f:
SCREAMING_SNAKE_CASE = f.read()
self.assertEqual(a , os.path.join(a , 'snapshots' , a , a))
self.assertTrue(os.path.isfile(a))
# File is cached at the same place the second time.
SCREAMING_SNAKE_CASE = cached_file(a , a)
self.assertEqual(a , a)
# Using a specific revision to test the full commit hash.
SCREAMING_SNAKE_CASE = cached_file(a , a , revision='9b8c223')
self.assertEqual(a , os.path.join(a , 'snapshots' , a , a))
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
with self.assertRaisesRegex(a , 'is not a valid model identifier'):
SCREAMING_SNAKE_CASE = cached_file('tiny-random-bert' , a)
with self.assertRaisesRegex(a , 'is not a valid git identifier'):
SCREAMING_SNAKE_CASE = cached_file(a , a , revision='aaaa')
with self.assertRaisesRegex(a , 'does not appear to have a file named'):
SCREAMING_SNAKE_CASE = cached_file(a , 'conf')
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
with self.assertRaisesRegex(a , 'does not appear to have a file named'):
SCREAMING_SNAKE_CASE = cached_file(a , 'conf')
with open(os.path.join(a , 'refs' , 'main')) as f:
SCREAMING_SNAKE_CASE = f.read()
self.assertTrue(os.path.isfile(os.path.join(a , '.no_exist' , a , 'conf')))
SCREAMING_SNAKE_CASE = cached_file(a , 'conf' , _raise_exceptions_for_missing_entries=a)
self.assertIsNone(a)
SCREAMING_SNAKE_CASE = cached_file(a , 'conf' , local_files_only=a , _raise_exceptions_for_missing_entries=a)
self.assertIsNone(a)
SCREAMING_SNAKE_CASE = mock.Mock()
SCREAMING_SNAKE_CASE = 500
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = HTTPError
SCREAMING_SNAKE_CASE = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' , return_value=a) as mock_head:
SCREAMING_SNAKE_CASE = cached_file(a , 'conf' , _raise_exceptions_for_connection_errors=a)
self.assertIsNone(a)
# This check we did call the fake head request
mock_head.assert_called()
def SCREAMING_SNAKE_CASE__ ( self) -> int:
self.assertTrue(has_file('hf-internal-testing/tiny-bert-pt-only' , a))
self.assertFalse(has_file('hf-internal-testing/tiny-bert-pt-only' , a))
self.assertFalse(has_file('hf-internal-testing/tiny-bert-pt-only' , a))
def SCREAMING_SNAKE_CASE__ ( self) -> Any:
# `get_file_from_repo` returns None if the file does not exist
self.assertIsNone(get_file_from_repo('bert-base-cased' , 'ahah.txt'))
# The function raises if the repository does not exist.
with self.assertRaisesRegex(a , 'is not a valid model identifier'):
get_file_from_repo('bert-base-case' , a)
# The function raises if the revision does not exist.
with self.assertRaisesRegex(a , 'is not a valid git identifier'):
get_file_from_repo('bert-base-cased' , a , revision='ahaha')
SCREAMING_SNAKE_CASE = get_file_from_repo('bert-base-cased' , a)
# The name is the cached name which is not very easy to test, so instead we load the content.
SCREAMING_SNAKE_CASE = json.loads(open(a , 'r').read())
self.assertEqual(config['hidden_size'] , 768)
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE = Path(a) / 'a.txt'
filename.touch()
self.assertEqual(get_file_from_repo(a , 'a.txt') , str(a))
self.assertIsNone(get_file_from_repo(a , 'b.txt'))
| 137 | 1 |
'''simple docstring'''
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
_lowerCamelCase = open # noqa: we just need to have a builtin inside this module to test it properly
| 355 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class _snake_case (unittest.TestCase):
def __init__( self ,_snake_case ,_snake_case=7 ,_snake_case=3 ,_snake_case=18 ,_snake_case=30 ,_snake_case=4_00 ,_snake_case=True ,_snake_case=None ,_snake_case=True ,_snake_case=None ,_snake_case=True ,_snake_case=[0.48145466, 0.4578275, 0.40821073] ,_snake_case=[0.26862954, 0.26130258, 0.27577711] ,_snake_case=True ,):
UpperCAmelCase_ : List[str] = size if size is not None else {"height": 2_24, "width": 2_24}
UpperCAmelCase_ : Union[str, Any] = crop_size if crop_size is not None else {"height": 18, "width": 18}
UpperCAmelCase_ : Optional[int] = parent
UpperCAmelCase_ : Union[str, Any] = batch_size
UpperCAmelCase_ : Dict = num_channels
UpperCAmelCase_ : int = image_size
UpperCAmelCase_ : Dict = min_resolution
UpperCAmelCase_ : Tuple = max_resolution
UpperCAmelCase_ : List[Any] = do_resize
UpperCAmelCase_ : Optional[int] = size
UpperCAmelCase_ : Union[str, Any] = do_center_crop
UpperCAmelCase_ : Any = crop_size
UpperCAmelCase_ : str = do_normalize
UpperCAmelCase_ : Tuple = image_mean
UpperCAmelCase_ : List[Any] = image_std
UpperCAmelCase_ : Dict = do_convert_rgb
def UpperCamelCase__ ( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def UpperCamelCase__ ( self ,_snake_case=False ,_snake_case=False ,_snake_case=False ):
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
UpperCAmelCase_ : Optional[int] = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
2_55 ,size=(self.num_channels, self.max_resolution, self.max_resolution) ,dtype=np.uinta ) )
else:
UpperCAmelCase_ : Optional[Any] = []
for i in range(self.batch_size ):
UpperCAmelCase_ , UpperCAmelCase_ : Dict = np.random.choice(np.arange(self.min_resolution ,self.max_resolution ) ,2 )
image_inputs.append(np.random.randint(2_55 ,size=(self.num_channels, width, height) ,dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
UpperCAmelCase_ : Optional[int] = [Image.fromarray(np.moveaxis(_snake_case ,0 ,-1 ) ) for x in image_inputs]
if torchify:
UpperCAmelCase_ : Optional[Any] = [torch.from_numpy(_snake_case ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class _snake_case (__SCREAMING_SNAKE_CASE , unittest.TestCase):
__A : Tuple =ChineseCLIPImageProcessor if is_vision_available() else None
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Tuple = ChineseCLIPImageProcessingTester(self ,do_center_crop=_snake_case )
@property
def UpperCamelCase__ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_snake_case ,"do_resize" ) )
self.assertTrue(hasattr(_snake_case ,"size" ) )
self.assertTrue(hasattr(_snake_case ,"do_center_crop" ) )
self.assertTrue(hasattr(_snake_case ,"center_crop" ) )
self.assertTrue(hasattr(_snake_case ,"do_normalize" ) )
self.assertTrue(hasattr(_snake_case ,"image_mean" ) )
self.assertTrue(hasattr(_snake_case ,"image_std" ) )
self.assertTrue(hasattr(_snake_case ,"do_convert_rgb" ) )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : str = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"height": 2_24, "width": 2_24} )
self.assertEqual(image_processor.crop_size ,{"height": 18, "width": 18} )
UpperCAmelCase_ : Dict = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 ,crop_size=84 )
self.assertEqual(image_processor.size ,{"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size ,{"height": 84, "width": 84} )
def UpperCamelCase__ ( self ):
pass
def UpperCamelCase__ ( self ):
# Initialize image_processing
UpperCAmelCase_ : int = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase_ : Tuple = self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case )
for image in image_inputs:
self.assertIsInstance(_snake_case ,Image.Image )
# Test not batched input
UpperCAmelCase_ : Optional[Any] = image_processing(image_inputs[0] ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
# Test batched
UpperCAmelCase_ : int = image_processing(_snake_case ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
def UpperCamelCase__ ( self ):
# Initialize image_processing
UpperCAmelCase_ : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase_ : List[str] = self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case ,numpify=_snake_case )
for image in image_inputs:
self.assertIsInstance(_snake_case ,np.ndarray )
# Test not batched input
UpperCAmelCase_ : Tuple = image_processing(image_inputs[0] ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
# Test batched
UpperCAmelCase_ : Optional[int] = image_processing(_snake_case ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
def UpperCamelCase__ ( self ):
# Initialize image_processing
UpperCAmelCase_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase_ : List[Any] = self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case ,torchify=_snake_case )
for image in image_inputs:
self.assertIsInstance(_snake_case ,torch.Tensor )
# Test not batched input
UpperCAmelCase_ : str = image_processing(image_inputs[0] ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
# Test batched
UpperCAmelCase_ : List[str] = image_processing(_snake_case ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
@require_torch
@require_vision
class _snake_case (__SCREAMING_SNAKE_CASE , unittest.TestCase):
__A : Any =ChineseCLIPImageProcessor if is_vision_available() else None
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Dict = ChineseCLIPImageProcessingTester(self ,num_channels=4 ,do_center_crop=_snake_case )
UpperCAmelCase_ : Optional[Any] = 3
@property
def UpperCamelCase__ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_snake_case ,"do_resize" ) )
self.assertTrue(hasattr(_snake_case ,"size" ) )
self.assertTrue(hasattr(_snake_case ,"do_center_crop" ) )
self.assertTrue(hasattr(_snake_case ,"center_crop" ) )
self.assertTrue(hasattr(_snake_case ,"do_normalize" ) )
self.assertTrue(hasattr(_snake_case ,"image_mean" ) )
self.assertTrue(hasattr(_snake_case ,"image_std" ) )
self.assertTrue(hasattr(_snake_case ,"do_convert_rgb" ) )
def UpperCamelCase__ ( self ):
pass
def UpperCamelCase__ ( self ):
# Initialize image_processing
UpperCAmelCase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase_ : str = self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case )
for image in image_inputs:
self.assertIsInstance(_snake_case ,Image.Image )
# Test not batched input
UpperCAmelCase_ : Any = image_processing(image_inputs[0] ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
# Test batched
UpperCAmelCase_ : Any = image_processing(_snake_case ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) ,)
| 67 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[int] = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ ='yolos'
def __init__(self , a_=7_68 , a_=12 , a_=12 , a_=30_72 , a_="gelu" , a_=0.0 , a_=0.0 , a_=0.02 , a_=1E-12 , a_=[5_12, 8_64] , a_=16 , a_=3 , a_=True , a_=1_00 , a_=True , a_=False , a_=1 , a_=5 , a_=2 , a_=5 , a_=2 , a_=0.1 , **a_ , ):
'''simple docstring'''
super().__init__(**a_ )
__snake_case : Union[str, Any] = hidden_size
__snake_case : Union[str, Any] = num_hidden_layers
__snake_case : Dict = num_attention_heads
__snake_case : str = intermediate_size
__snake_case : List[str] = hidden_act
__snake_case : Tuple = hidden_dropout_prob
__snake_case : Optional[int] = attention_probs_dropout_prob
__snake_case : Union[str, Any] = initializer_range
__snake_case : Tuple = layer_norm_eps
__snake_case : List[Any] = image_size
__snake_case : Tuple = patch_size
__snake_case : str = num_channels
__snake_case : Tuple = qkv_bias
__snake_case : Union[str, Any] = num_detection_tokens
__snake_case : List[str] = use_mid_position_embeddings
__snake_case : Tuple = auxiliary_loss
# Hungarian matcher
__snake_case : List[str] = class_cost
__snake_case : int = bbox_cost
__snake_case : int = giou_cost
# Loss coefficients
__snake_case : Optional[int] = bbox_loss_coefficient
__snake_case : List[str] = giou_loss_coefficient
__snake_case : List[Any] = eos_coefficient
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ =version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
return 1E-4
@property
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
return 12
| 102 |
from __future__ import annotations
from collections.abc import Iterator
class _a :
def __init__( self: List[str] , UpperCamelCase_: int ) -> None:
"""simple docstring"""
lowercase__ = value
lowercase__ = None
lowercase__ = None
class _a :
def __init__( self: Union[str, Any] , UpperCamelCase_: Node ) -> None:
"""simple docstring"""
lowercase__ = tree
def lowerCamelCase_ ( self: Any , UpperCamelCase_: Node | None ) -> int:
"""simple docstring"""
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self: List[str] ) -> Iterator[int]:
"""simple docstring"""
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 110 | 0 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : torch.FloatTensor
snake_case__ : Optional[torch.FloatTensor] = None
def SCREAMING_SNAKE_CASE_ ( __A : Optional[Any] , __A : str=0.999 , __A : Dict="cosine" , ) -> Optional[int]:
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(__A : Dict ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__A : List[str] ):
return math.exp(t * -12.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
a_ : str = []
for i in range(__A ):
a_ : List[str] = i / num_diffusion_timesteps
a_ : Any = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__A ) / alpha_bar_fn(__A ) , __A ) )
return torch.tensor(__A , dtype=torch.floataa )
class SCREAMING_SNAKE_CASE__ ( lowercase__ , lowercase__ ):
snake_case__ : str = 1
@register_to_config
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : int = 1_0_0_0 , SCREAMING_SNAKE_CASE__ : float = 0.0001 , SCREAMING_SNAKE_CASE__ : float = 0.02 , SCREAMING_SNAKE_CASE__ : str = "linear" , SCREAMING_SNAKE_CASE__ : Optional[Union[np.ndarray, List[float]]] = None , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : int = 0 , SCREAMING_SNAKE_CASE__ : str = "epsilon" , SCREAMING_SNAKE_CASE__ : float = 1.0 , **SCREAMING_SNAKE_CASE__ : List[Any] , ) -> List[str]:
if kwargs.get('set_alpha_to_one' , SCREAMING_SNAKE_CASE__ ) is not None:
a_ : Tuple = (
'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
)
deprecate('set_alpha_to_one' , '1.0.0' , SCREAMING_SNAKE_CASE__ , standard_warn=SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = kwargs['set_alpha_to_one']
if trained_betas is not None:
a_ : int = torch.tensor(SCREAMING_SNAKE_CASE__ , dtype=torch.floataa )
elif beta_schedule == "linear":
a_ : Tuple = torch.linspace(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
a_ : Any = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , SCREAMING_SNAKE_CASE__ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
a_ : Any = betas_for_alpha_bar(SCREAMING_SNAKE_CASE__ )
else:
raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""" )
a_ : Dict = 1.0 - self.betas
a_ : Tuple = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
a_ : List[str] = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
a_ : Dict = 1.0
# setable values
a_ : int = None
a_ : List[Any] = torch.from_numpy(np.arange(0 , SCREAMING_SNAKE_CASE__ ).copy().astype(np.intaa ) )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : torch.FloatTensor , SCREAMING_SNAKE_CASE__ : Optional[int] = None ) -> torch.FloatTensor:
return sample
def SCREAMING_SNAKE_CASE ( self : List[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Union[str, torch.device] = None ) -> Optional[Any]:
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
F""" maximal {self.config.num_train_timesteps} timesteps.""" )
a_ : List[str] = num_inference_steps
a_ : List[str] = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
a_ : Any = (np.arange(0 , SCREAMING_SNAKE_CASE__ ) * step_ratio).round().copy().astype(np.intaa )
a_ : Optional[Any] = torch.from_numpy(SCREAMING_SNAKE_CASE__ ).to(SCREAMING_SNAKE_CASE__ )
self.timesteps += self.config.steps_offset
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : torch.FloatTensor , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : torch.FloatTensor , SCREAMING_SNAKE_CASE__ : float = 0.0 , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : Optional[torch.FloatTensor] = None , SCREAMING_SNAKE_CASE__ : bool = True , ) -> Union[DDIMSchedulerOutput, Tuple]:
# 1. get previous step value (=t+1)
a_ : Union[str, Any] = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
a_ : Any = self.alphas_cumprod[timestep]
a_ : str = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
a_ : str = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
a_ : Optional[int] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
a_ : Tuple = model_output
elif self.config.prediction_type == "sample":
a_ : Dict = model_output
a_ : str = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
a_ : str = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
a_ : Tuple = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
' `v_prediction`' )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
a_ : Optional[int] = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
a_ : int = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
a_ : Optional[Any] = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__ , pred_original_sample=SCREAMING_SNAKE_CASE__ )
def __len__( self : Any ) -> List[str]:
return self.config.num_train_timesteps
| 120 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Dict = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : int = [
'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTMSNModel',
'ViTMSNForImageClassification',
'ViTMSNPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 120 | 1 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class a :
def __init__( self : str ):
snake_case_ = {}
def A_ ( self : Dict , lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : Optional[Any]=1 ):
if self.graph.get(lowercase_ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
snake_case_ = [[w, v]]
if not self.graph.get(lowercase_ ):
snake_case_ = []
def A_ ( self : Any ):
return list(self.graph )
def A_ ( self : Any , lowercase_ : Any , lowercase_ : Optional[Any] ):
if self.graph.get(lowercase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowercase_ )
def A_ ( self : int , lowercase_ : Optional[int]=-2 , lowercase_ : Optional[int]=-1 ):
if s == d:
return []
snake_case_ = []
snake_case_ = []
if s == -2:
snake_case_ = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
snake_case_ = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
snake_case_ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowercase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
snake_case_ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowercase_ ) != 0:
snake_case_ = stack[len(lowercase_ ) - 1]
else:
snake_case_ = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return visited
def A_ ( self : Optional[int] , lowercase_ : str=-1 ):
if c == -1:
snake_case_ = floor(random() * 1_0000 ) + 10
for i in range(lowercase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
snake_case_ = floor(random() * c ) + 1
if n != i:
self.add_pair(lowercase_ , lowercase_ , 1 )
def A_ ( self : Optional[int] , lowercase_ : Optional[Any]=-2 ):
snake_case_ = deque()
snake_case_ = []
if s == -2:
snake_case_ = list(self.graph )[0]
d.append(lowercase_ )
visited.append(lowercase_ )
while d:
snake_case_ = 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 : Optional[int] , lowercase_ : Optional[int] ):
snake_case_ = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def A_ ( self : List[Any] , lowercase_ : Optional[Any] ):
return len(self.graph[u] )
def A_ ( self : Any , lowercase_ : List[Any]=-2 ):
snake_case_ = []
snake_case_ = []
if s == -2:
snake_case_ = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
snake_case_ = s
snake_case_ = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
snake_case_ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
snake_case_ = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowercase_ ) != 0:
snake_case_ = stack[len(lowercase_ ) - 1]
else:
snake_case_ = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return sorted_nodes
def A_ ( self : Tuple ):
snake_case_ = []
snake_case_ = []
snake_case_ = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
snake_case_ = -2
snake_case_ = []
snake_case_ = s
snake_case_ = False
snake_case_ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
snake_case_ = 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
):
snake_case_ = len(lowercase_ ) - 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] )
snake_case_ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
snake_case_ = True
if len(lowercase_ ) != 0:
snake_case_ = stack[len(lowercase_ ) - 1]
else:
snake_case_ = False
indirect_parents.append(lowercase_ )
snake_case_ = s
snake_case_ = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return list(lowercase_ )
def A_ ( self : Dict ):
snake_case_ = []
snake_case_ = []
snake_case_ = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
snake_case_ = -2
snake_case_ = []
snake_case_ = s
snake_case_ = False
snake_case_ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
snake_case_ = 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
):
snake_case_ = len(lowercase_ ) - 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] )
snake_case_ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
snake_case_ = True
if len(lowercase_ ) != 0:
snake_case_ = stack[len(lowercase_ ) - 1]
else:
snake_case_ = False
indirect_parents.append(lowercase_ )
snake_case_ = s
snake_case_ = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return False
def A_ ( self : Optional[int] , lowercase_ : List[Any]=-2 , lowercase_ : str=-1 ):
snake_case_ = time()
self.dfs(lowercase_ , lowercase_ )
snake_case_ = time()
return end - begin
def A_ ( self : List[Any] , lowercase_ : List[str]=-2 ):
snake_case_ = time()
self.bfs(lowercase_ )
snake_case_ = time()
return end - begin
class a :
def __init__( self : List[Any] ):
snake_case_ = {}
def A_ ( self : int , lowercase_ : int , lowercase_ : Optional[int] , lowercase_ : List[Any]=1 ):
# check if the u exists
if self.graph.get(lowercase_ ):
# 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
snake_case_ = [[w, v]]
# add the other way
if self.graph.get(lowercase_ ):
# 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
snake_case_ = [[w, u]]
def A_ ( self : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : int ):
if self.graph.get(lowercase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowercase_ )
# the other way round
if self.graph.get(lowercase_ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowercase_ )
def A_ ( self : List[Any] , lowercase_ : List[str]=-2 , lowercase_ : Tuple=-1 ):
if s == d:
return []
snake_case_ = []
snake_case_ = []
if s == -2:
snake_case_ = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
snake_case_ = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
snake_case_ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowercase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
snake_case_ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowercase_ ) != 0:
snake_case_ = stack[len(lowercase_ ) - 1]
else:
snake_case_ = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return visited
def A_ ( self : List[Any] , lowercase_ : Optional[Any]=-1 ):
if c == -1:
snake_case_ = floor(random() * 1_0000 ) + 10
for i in range(lowercase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
snake_case_ = floor(random() * c ) + 1
if n != i:
self.add_pair(lowercase_ , lowercase_ , 1 )
def A_ ( self : Tuple , lowercase_ : Dict=-2 ):
snake_case_ = deque()
snake_case_ = []
if s == -2:
snake_case_ = list(self.graph )[0]
d.append(lowercase_ )
visited.append(lowercase_ )
while d:
snake_case_ = 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 : List[str] , lowercase_ : Tuple ):
return len(self.graph[u] )
def A_ ( self : str ):
snake_case_ = []
snake_case_ = []
snake_case_ = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
snake_case_ = -2
snake_case_ = []
snake_case_ = s
snake_case_ = False
snake_case_ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
snake_case_ = 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
):
snake_case_ = len(lowercase_ ) - 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] )
snake_case_ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
snake_case_ = True
if len(lowercase_ ) != 0:
snake_case_ = stack[len(lowercase_ ) - 1]
else:
snake_case_ = False
indirect_parents.append(lowercase_ )
snake_case_ = s
snake_case_ = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return list(lowercase_ )
def A_ ( self : Any ):
snake_case_ = []
snake_case_ = []
snake_case_ = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
snake_case_ = -2
snake_case_ = []
snake_case_ = s
snake_case_ = False
snake_case_ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
snake_case_ = 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
):
snake_case_ = len(lowercase_ ) - 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] )
snake_case_ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
snake_case_ = True
if len(lowercase_ ) != 0:
snake_case_ = stack[len(lowercase_ ) - 1]
else:
snake_case_ = False
indirect_parents.append(lowercase_ )
snake_case_ = s
snake_case_ = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return False
def A_ ( self : Dict ):
return list(self.graph )
def A_ ( self : List[Any] , lowercase_ : List[Any]=-2 , lowercase_ : Any=-1 ):
snake_case_ = time()
self.dfs(lowercase_ , lowercase_ )
snake_case_ = time()
return end - begin
def A_ ( self : Optional[Any] , lowercase_ : int=-2 ):
snake_case_ = time()
self.bfs(lowercase_ )
snake_case_ = time()
return end - begin
| 56 |
'''simple docstring'''
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = AutoencoderKL
snake_case_ = "sample"
snake_case_ = 1e-2
@property
def A_ ( self : Dict ):
snake_case_ = 4
snake_case_ = 3
snake_case_ = (32, 32)
snake_case_ = floats_tensor((batch_size, num_channels) + sizes ).to(lowercase_ )
return {"sample": image}
@property
def A_ ( self : List[Any] ):
return (3, 32, 32)
@property
def A_ ( self : Dict ):
return (3, 32, 32)
def A_ ( self : Union[str, Any] ):
snake_case_ = {
'''block_out_channels''': [32, 64],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 4,
}
snake_case_ = self.dummy_input
return init_dict, inputs_dict
def A_ ( self : Any ):
pass
def A_ ( self : str ):
pass
@unittest.skipIf(torch_device == '''mps''' , '''Gradient checkpointing skipped on MPS''' )
def A_ ( self : Dict ):
# enable deterministic behavior for gradient checkpointing
snake_case_ ,snake_case_ = self.prepare_init_args_and_inputs_for_common()
snake_case_ = self.model_class(**lowercase_ )
model.to(lowercase_ )
assert not model.is_gradient_checkpointing and model.training
snake_case_ = model(**lowercase_ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
snake_case_ = torch.randn_like(lowercase_ )
snake_case_ = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
snake_case_ = self.model_class(**lowercase_ )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(lowercase_ )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
snake_case_ = model_a(**lowercase_ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
snake_case_ = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1e-5 )
snake_case_ = dict(model.named_parameters() )
snake_case_ = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5e-5 ) )
def A_ ( self : Tuple ):
snake_case_ ,snake_case_ = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' , output_loading_info=lowercase_ )
self.assertIsNotNone(lowercase_ )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(lowercase_ )
snake_case_ = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def A_ ( self : Tuple ):
snake_case_ = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' )
snake_case_ = model.to(lowercase_ )
model.eval()
if torch_device == "mps":
snake_case_ = torch.manual_seed(0 )
else:
snake_case_ = torch.Generator(device=lowercase_ ).manual_seed(0 )
snake_case_ = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
snake_case_ = image.to(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , sample_posterior=lowercase_ , generator=lowercase_ ).sample
snake_case_ = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
snake_case_ = torch.tensor(
[
-4.0_078e-01,
-3.8_323e-04,
-1.2_681e-01,
-1.1_462e-01,
2.0_095e-01,
1.0_893e-01,
-8.8_247e-02,
-3.0_361e-01,
-9.8_644e-03,
] )
elif torch_device == "cpu":
snake_case_ = torch.tensor(
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] )
else:
snake_case_ = torch.tensor(
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485] )
self.assertTrue(torch_all_close(lowercase_ , lowercase_ , rtol=1e-2 ) )
@slow
class a ( unittest.TestCase ):
def A_ ( self : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[int] ):
return F"gaussian_noise_s={seed}_shape={'_'.join([str(lowercase_ ) for s in shape] )}.npy"
def A_ ( self : Any ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : Dict , lowercase_ : List[Any]=0 , lowercase_ : Union[str, Any]=(4, 3, 512, 512) , lowercase_ : Optional[Any]=False ):
snake_case_ = torch.floataa if fpaa else torch.floataa
snake_case_ = torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase_ , lowercase_ ) ) ).to(lowercase_ ).to(lowercase_ )
return image
def A_ ( self : Any , lowercase_ : Dict="CompVis/stable-diffusion-v1-4" , lowercase_ : List[str]=False ):
snake_case_ = '''fp16''' if fpaa else None
snake_case_ = torch.floataa if fpaa else torch.floataa
snake_case_ = AutoencoderKL.from_pretrained(
lowercase_ , subfolder='''vae''' , torch_dtype=lowercase_ , revision=lowercase_ , )
model.to(lowercase_ ).eval()
return model
def A_ ( self : Any , lowercase_ : int=0 ):
if torch_device == "mps":
return torch.manual_seed(lowercase_ )
return torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
@parameterized.expand(
[
# fmt: off
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def A_ ( self : Union[str, Any] , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Tuple ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , generator=lowercase_ , sample_posterior=lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
snake_case_ = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(lowercase_ , lowercase_ , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : Dict ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , fpaa=lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , generator=lowercase_ , sample_posterior=lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def A_ ( self : Tuple , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
snake_case_ = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(lowercase_ , lowercase_ , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Dict , lowercase_ : Tuple , lowercase_ : Optional[int] ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
snake_case_ = sample[-1, -2:, :2, -2:].flatten().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Union[str, Any] , lowercase_ : Any , lowercase_ : Optional[Any] ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) , fpaa=lowercase_ )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
snake_case_ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=5e-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def A_ ( self : Optional[Any] , lowercase_ : List[str] ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) , fpaa=lowercase_ )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def A_ ( self : Optional[Any] , lowercase_ : Any ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
# fmt: on
] )
def A_ ( self : str , lowercase_ : Optional[int] , lowercase_ : Tuple ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model.encode(lowercase_ ).latent_dist
snake_case_ = dist.sample(generator=lowercase_ )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
snake_case_ = sample[0, -1, -3:, -3:].flatten().cpu()
snake_case_ = torch.tensor(lowercase_ )
snake_case_ = 3e-3 if torch_device != '''mps''' else 1e-2
assert torch_all_close(lowercase_ , lowercase_ , atol=lowercase_ )
| 56 | 1 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCamelCase__ = 3
def __lowerCAmelCase (_UpperCamelCase ):
print('Generating primitive root of p' )
while True:
__lowerCAmelCase : Optional[Any] = random.randrange(3 , _UpperCamelCase )
if pow(_UpperCamelCase , 2 , _UpperCamelCase ) == 1:
continue
if pow(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) == 1:
continue
return g
def __lowerCAmelCase (_UpperCamelCase ):
print('Generating prime p...' )
__lowerCAmelCase : int = rabin_miller.generate_large_prime(_UpperCamelCase ) # select large prime number.
__lowerCAmelCase : str = primitive_root(_UpperCamelCase ) # one primitive root on modulo p.
__lowerCAmelCase : Tuple = random.randrange(3 , _UpperCamelCase ) # private_key -> have to be greater than 2 for safety.
__lowerCAmelCase : Tuple = cryptomath.find_mod_inverse(pow(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) , _UpperCamelCase )
__lowerCAmelCase : Union[str, Any] = (key_size, e_a, e_a, p)
__lowerCAmelCase : List[Any] = (key_size, d)
return public_key, private_key
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
if os.path.exists(F"{name}_pubkey.txt" ) or os.path.exists(F"{name}_privkey.txt" ):
print('\nWARNING:' )
print(
F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
'Use a different name or delete these files and re-run this program.' )
sys.exit()
__lowerCAmelCase , __lowerCAmelCase : Optional[int] = generate_key(_UpperCamelCase )
print(F"\nWriting public key to file {name}_pubkey.txt..." )
with open(F"{name}_pubkey.txt" , 'w' ) as fo:
fo.write(F"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}" )
print(F"Writing private key to file {name}_privkey.txt..." )
with open(F"{name}_privkey.txt" , 'w' ) as fo:
fo.write(F"{private_key[0]},{private_key[1]}" )
def __lowerCAmelCase ():
print('Making key files...' )
make_key_files('elgamal' , 2048 )
print('Key files generation successful' )
if __name__ == "__main__":
main() | 355 |
"""simple docstring"""
import qiskit
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : Union[str, Any] = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
__lowerCAmelCase : str = qiskit.QuantumCircuit(_UpperCamelCase , _UpperCamelCase )
# Map the quantum measurement to the classical bits
circuit.measure([0] , [0] )
# Execute the circuit on the simulator
__lowerCAmelCase : Optional[int] = qiskit.execute(_UpperCamelCase , _UpperCamelCase , shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(_UpperCamelCase )
if __name__ == "__main__":
print(f'Total count for various states are: {single_qubit_measure(1, 1)}') | 182 | 0 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
MODEL_FOR_PRETRAINING_MAPPING,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
)
from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class __A:
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=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_=1_28 , SCREAMING_SNAKE_CASE_=32 , 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 , ):
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
def UpperCAmelCase_ (self ):
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase__ = None
if self.use_input_mask:
UpperCamelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase__ = 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__ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase_ (self ):
return NezhaConfig(
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 , )
def UpperCAmelCase_ (self ):
(
(
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) ,
) = self.prepare_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,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = NezhaModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCamelCase__ = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase )
UpperCamelCase__ = model(_lowerCamelCase , token_type_ids=_lowerCamelCase )
UpperCamelCase__ = model(_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 UpperCAmelCase_ (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_ , ):
UpperCamelCase__ = True
UpperCamelCase__ = NezhaModel(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCamelCase__ = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , encoder_attention_mask=_lowerCamelCase , )
UpperCamelCase__ = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , )
UpperCamelCase__ = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_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 UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = NezhaForMaskedLM(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCamelCase__ = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = NezhaForNextSentencePrediction(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCamelCase__ = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = NezhaForPreTraining(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCamelCase__ = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase , next_sentence_label=_lowerCamelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = NezhaForQuestionAnswering(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCamelCase__ = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , start_positions=_lowerCamelCase , end_positions=_lowerCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = self.num_labels
UpperCamelCase__ = NezhaForSequenceClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCamelCase__ = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = self.num_labels
UpperCamelCase__ = NezhaForTokenClassification(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCamelCase__ = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = self.num_choices
UpperCamelCase__ = NezhaForMultipleChoice(config=_lowerCamelCase )
model.to(_lowerCamelCase )
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(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.prepare_config_and_inputs()
(
(
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) ,
) = config_and_inputs
UpperCamelCase__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __A( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (
(
NezhaModel,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE__ = (
{
"feature-extraction": NezhaModel,
"fill-mask": NezhaForMaskedLM,
"question-answering": NezhaForQuestionAnswering,
"text-classification": NezhaForSequenceClassification,
"token-classification": NezhaForTokenClassification,
"zero-shot": NezhaForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE__ = True
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase__ = super()._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
if return_labels:
if model_class in get_values(_lowerCamelCase ):
UpperCamelCase__ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_lowerCamelCase )
UpperCamelCase__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_lowerCamelCase )
return inputs_dict
def UpperCAmelCase_ (self ):
UpperCamelCase__ = NezhaModelTester(self )
UpperCamelCase__ = ConfigTester(self , config_class=_lowerCamelCase , hidden_size=37 )
def UpperCAmelCase_ (self ):
self.config_tester.run_common_tests()
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_lowerCamelCase )
def UpperCAmelCase_ (self ):
(
(
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) ,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
UpperCamelCase__ = None
self.model_tester.create_and_check_model_as_decoder(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*_lowerCamelCase )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_lowerCamelCase )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase )
@slow
def UpperCAmelCase_ (self ):
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ = NezhaModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
@slow
@require_torch_gpu
def UpperCAmelCase_ (self ):
UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# NezhaForMultipleChoice behaves incorrectly in JIT environments.
if model_class == NezhaForMultipleChoice:
return
UpperCamelCase__ = True
UpperCamelCase__ = model_class(config=_lowerCamelCase )
UpperCamelCase__ = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
UpperCamelCase__ = 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 , """bert.pt""" ) )
UpperCamelCase__ = torch.jit.load(os.path.join(_lowerCamelCase , """bert.pt""" ) , map_location=_lowerCamelCase )
loaded(inputs_dict["""input_ids"""].to(_lowerCamelCase ) , inputs_dict["""attention_mask"""].to(_lowerCamelCase ) )
@require_torch
class __A( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase_ (self ):
UpperCamelCase__ = NezhaModel.from_pretrained("""sijunhe/nezha-cn-base""" )
UpperCamelCase__ = torch.tensor([[0, 1, 2, 3, 4, 5]] )
UpperCamelCase__ = torch.tensor([[0, 1, 1, 1, 1, 1]] )
with torch.no_grad():
UpperCamelCase__ = model(_lowerCamelCase , attention_mask=_lowerCamelCase )[0]
UpperCamelCase__ = torch.Size((1, 6, 7_68) )
self.assertEqual(output.shape , _lowerCamelCase )
UpperCamelCase__ = torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowerCamelCase , atol=1E-4 ) )
@slow
def UpperCAmelCase_ (self ):
UpperCamelCase__ = NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base""" )
UpperCamelCase__ = torch.tensor([[0, 1, 2, 3, 4, 5]] )
UpperCamelCase__ = torch.tensor([[1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
UpperCamelCase__ = model(_lowerCamelCase , attention_mask=_lowerCamelCase )[0]
UpperCamelCase__ = torch.Size((1, 6, 2_11_28) )
self.assertEqual(output.shape , _lowerCamelCase )
UpperCamelCase__ = torch.tensor(
[[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowerCamelCase , atol=1E-4 ) )
| 244 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
lowercase_ = random.Random()
if is_torch_available():
import torch
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase=1.0 , __UpperCamelCase=None , __UpperCamelCase=None ):
"""simple docstring"""
if rng is None:
__A = global_rng
__A = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class snake_case ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Any, _lowerCamelCase : List[str], _lowerCamelCase : Any=7, _lowerCamelCase : Optional[int]=4_00, _lowerCamelCase : Optional[int]=20_00, _lowerCamelCase : Dict=1, _lowerCamelCase : Optional[Any]=0.0, _lowerCamelCase : int=1_60_00, _lowerCamelCase : Optional[int]=True, _lowerCamelCase : Dict=True, ):
'''simple docstring'''
__A = parent
__A = batch_size
__A = min_seq_length
__A = max_seq_length
__A = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
__A = feature_size
__A = padding_value
__A = sampling_rate
__A = return_attention_mask
__A = do_normalize
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
'''simple docstring'''
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def _SCREAMING_SNAKE_CASE ( self : Any, _lowerCamelCase : Optional[Any]=False, _lowerCamelCase : int=False ):
'''simple docstring'''
def _flatten(_lowerCamelCase : List[str] ):
return list(itertools.chain(*_lowerCamelCase ) )
if equal_length:
__A = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
__A = [
_flatten(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 = [np.asarray(_lowerCamelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class snake_case ( _lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
A_ : int = ASTFeatureExtractor
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
'''simple docstring'''
__A = ASTFeatureExtractionTester(self )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
'''simple docstring'''
# Tests that all call wrap to encode_plus and batch_encode_plus
__A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
__A = [floats_list((1, x) )[0] for x in range(8_00, 14_00, 2_00 )]
__A = [np.asarray(_lowerCamelCase ) for speech_input in speech_inputs]
# Test not batched input
__A = feat_extract(speech_inputs[0], return_tensors='''np''' ).input_values
__A = feat_extract(np_speech_inputs[0], return_tensors='''np''' ).input_values
self.assertTrue(np.allclose(_lowerCamelCase, _lowerCamelCase, atol=1e-3 ) )
# Test batched
__A = feat_extract(_lowerCamelCase, padding=_lowerCamelCase, return_tensors='''np''' ).input_values
__A = feat_extract(_lowerCamelCase, padding=_lowerCamelCase, return_tensors='''np''' ).input_values
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 = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
__A = np.asarray(_lowerCamelCase )
__A = feat_extract(_lowerCamelCase, return_tensors='''np''' ).input_values
__A = feat_extract(_lowerCamelCase, return_tensors='''np''' ).input_values
for enc_seq_a, enc_seq_a in zip(_lowerCamelCase, _lowerCamelCase ):
self.assertTrue(np.allclose(_lowerCamelCase, _lowerCamelCase, atol=1e-3 ) )
@require_torch
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
'''simple docstring'''
import torch
__A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__A = np.random.rand(1_00 ).astype(np.floataa )
__A = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
__A = feature_extractor.pad([{'''input_values''': inputs}], return_tensors='''np''' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
__A = feature_extractor.pad([{'''input_values''': inputs}], return_tensors='''pt''' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def _SCREAMING_SNAKE_CASE ( self : Optional[int], _lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
from datasets import load_dataset
__A = load_dataset('''hf-internal-testing/librispeech_asr_dummy''', '''clean''', split='''validation''' )
# automatic decoding with librispeech
__A = ds.sort('''id''' ).select(range(_lowerCamelCase ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
@require_torch
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
'''simple docstring'''
# fmt: off
__A = torch.tensor(
[-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
-1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
-1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
-0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69] )
# fmt: on
__A = self._load_datasamples(1 )
__A = ASTFeatureExtractor()
__A = feature_extractor(_lowerCamelCase, return_tensors='''pt''' ).input_values
self.assertEquals(input_values.shape, (1, 10_24, 1_28) )
self.assertTrue(torch.allclose(input_values[0, 0, :30], _lowerCamelCase, atol=1e-4 ) )
| 266 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class A__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
lowercase = XLMTokenizer
lowercase = False
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
A_ = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
A_ = dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) )
A_ = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
A_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
A_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" ) as fp:
fp.write(json.dumps(__UpperCAmelCase ) )
with open(self.merges_file , """w""" ) as fp:
fp.write("""\n""".join(__UpperCAmelCase ) )
def snake_case_ ( self , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
A_ = """lower newer"""
A_ = """lower newer"""
return input_text, output_text
def snake_case_ ( self ) -> Optional[int]:
'''simple docstring'''
A_ = XLMTokenizer(self.vocab_file , self.merges_file )
A_ = """lower"""
A_ = ["""low""", """er</w>"""]
A_ = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
A_ = tokens + ["""<unk>"""]
A_ = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
@slow
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
A_ = XLMTokenizer.from_pretrained("""xlm-mlm-en-2048""" )
A_ = tokenizer.encode("""sequence builders""" , add_special_tokens=__UpperCAmelCase )
A_ = tokenizer.encode("""multi-sequence build""" , add_special_tokens=__UpperCAmelCase )
A_ = tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase )
A_ = tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase , __UpperCAmelCase )
assert encoded_sentence == [0] + text + [1]
assert encoded_pair == [0] + text + [1] + text_a + [1]
| 366 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
__lowerCamelCase = logging.getLogger(__name__)
class A__ ( _snake_case ):
lowercase = "summarization"
lowercase = ["loss"]
lowercase = ROUGE_KEYS
lowercase = "rouge2"
def __init__( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Dict:
'''simple docstring'''
if hparams.sortish_sampler and hparams.gpus > 1:
A_ = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training""" )
if hparams.sortish_sampler:
raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously""" )
super().__init__(UpperCamelCase__ , num_labels=UpperCamelCase__ , mode=self.mode , **UpperCamelCase__ )
use_task_specific_params(self.model , """summarization""" )
save_git_info(self.hparams.output_dir )
A_ = Path(self.output_dir ) / """metrics.json"""
A_ = Path(self.output_dir ) / """hparams.pkl"""
pickle_save(self.hparams , self.hparams_save_path )
A_ = 0
A_ = defaultdict(UpperCamelCase__ )
A_ = self.config.model_type
A_ = self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size
A_ = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
A_ = {
"""train""": self.hparams.n_train,
"""val""": self.hparams.n_val,
"""test""": self.hparams.n_test,
}
A_ = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
A_ = {
"""train""": self.hparams.max_target_length,
"""val""": self.hparams.val_max_target_length,
"""test""": self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], f'''target_lens: {self.target_lens}'''
assert self.target_lens["train"] <= self.target_lens["test"], f'''target_lens: {self.target_lens}'''
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
A_ = get_git_info()["""repo_sha"""]
A_ = hparams.num_workers
A_ = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , UpperCamelCase__ ):
A_ = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
A_ = self.decoder_start_token_id
A_ = (
SeqaSeqDataset if hasattr(self.tokenizer , """prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
)
A_ = False
A_ = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
A_ = self.hparams.eval_max_gen_length
else:
A_ = self.model.config.max_length
A_ = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def snake_case_ ( self , UpperCamelCase__ ) -> Dict[str, List[str]]:
'''simple docstring'''
A_ = {
k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
}
save_json(UpperCamelCase__ , Path(self.output_dir ) / """text_batch.json""" )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / """tok_batch.json""" )
A_ = True
return readable_batch
def snake_case_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> str:
'''simple docstring'''
return self.model(UpperCamelCase__ , **UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
A_ = self.tokenizer.batch_decode(
UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__ )
return lmap(str.strip , UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
A_ = self.tokenizer.pad_token_id
A_ , A_ = batch["""input_ids"""], batch["""attention_mask"""]
A_ = batch["""labels"""]
if isinstance(self.model , UpperCamelCase__ ):
A_ = self.model._shift_right(UpperCamelCase__ )
else:
A_ = shift_tokens_right(UpperCamelCase__ , UpperCamelCase__ )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
A_ = decoder_input_ids
self.save_readable_batch(UpperCamelCase__ )
A_ = self(UpperCamelCase__ , attention_mask=UpperCamelCase__ , decoder_input_ids=UpperCamelCase__ , use_cache=UpperCamelCase__ )
A_ = outputs["""logits"""]
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
A_ = nn.CrossEntropyLoss(ignore_index=UpperCamelCase__ )
assert lm_logits.shape[-1] == self.vocab_size
A_ = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
A_ = nn.functional.log_softmax(UpperCamelCase__ , dim=-1 )
A_ , A_ = label_smoothed_nll_loss(
UpperCamelCase__ , UpperCamelCase__ , self.hparams.label_smoothing , ignore_index=UpperCamelCase__ )
return (loss,)
@property
def snake_case_ ( self ) -> int:
'''simple docstring'''
return self.tokenizer.pad_token_id
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
A_ = self._step(UpperCamelCase__ )
A_ = dict(zip(self.loss_names , UpperCamelCase__ ) )
# tokens per batch
A_ = batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
A_ = batch["""input_ids"""].shape[0]
A_ = batch["""input_ids"""].eq(self.pad ).sum()
A_ = batch["""input_ids"""].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
return self._generative_step(UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__="val" ) -> Dict:
'''simple docstring'''
self.step_count += 1
A_ = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
A_ = losses["""loss"""]
A_ = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
}
A_ = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
A_ = torch.tensor(UpperCamelCase__ ).type_as(UpperCamelCase__ )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(UpperCamelCase__ )
A_ = {f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
A_ = self.step_count
self.metrics[prefix].append(UpperCamelCase__ ) # callback writes this to self.metrics_save_path
A_ = flatten_list([x["""preds"""] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
f'''{prefix}_loss''': loss,
f'''{prefix}_{self.val_metric}''': metric_tensor,
}
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
return calculate_rouge(UpperCamelCase__ , UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ ) -> dict:
'''simple docstring'''
A_ = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
A_ = self.model.generate(
batch["""input_ids"""] , attention_mask=batch["""attention_mask"""] , use_cache=UpperCamelCase__ , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
A_ = (time.time() - ta) / batch["""input_ids"""].shape[0]
A_ = self.ids_to_clean_text(UpperCamelCase__ )
A_ = self.ids_to_clean_text(batch["""labels"""] )
A_ = self._step(UpperCamelCase__ )
A_ = dict(zip(self.loss_names , UpperCamelCase__ ) )
A_ = self.calc_generative_metrics(UpperCamelCase__ , UpperCamelCase__ )
A_ = np.mean(lmap(UpperCamelCase__ , UpperCamelCase__ ) )
base_metrics.update(gen_time=UpperCamelCase__ , gen_len=UpperCamelCase__ , preds=UpperCamelCase__ , target=UpperCamelCase__ , **UpperCamelCase__ )
return base_metrics
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
return self._generative_step(UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
return self.validation_epoch_end(UpperCamelCase__ , prefix="""test""" )
def snake_case_ ( self , UpperCamelCase__ ) -> SeqaSeqDataset:
'''simple docstring'''
A_ = self.n_obs[type_path]
A_ = self.target_lens[type_path]
A_ = self.dataset_class(
self.tokenizer , type_path=UpperCamelCase__ , n_obs=UpperCamelCase__ , max_target_length=UpperCamelCase__ , **self.dataset_kwargs , )
return dataset
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False ) -> DataLoader:
'''simple docstring'''
A_ = self.get_dataset(UpperCamelCase__ )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
A_ = dataset.make_sortish_sampler(UpperCamelCase__ , distributed=self.hparams.gpus > 1 )
return DataLoader(
UpperCamelCase__ , batch_size=UpperCamelCase__ , collate_fn=dataset.collate_fn , shuffle=UpperCamelCase__ , num_workers=self.num_workers , sampler=UpperCamelCase__ , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
A_ = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
UpperCamelCase__ , batch_sampler=UpperCamelCase__ , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
UpperCamelCase__ , batch_size=UpperCamelCase__ , collate_fn=dataset.collate_fn , shuffle=UpperCamelCase__ , num_workers=self.num_workers , sampler=UpperCamelCase__ , )
def snake_case_ ( self ) -> DataLoader:
'''simple docstring'''
A_ = self.get_dataloader("""train""" , batch_size=self.hparams.train_batch_size , shuffle=UpperCamelCase__ )
return dataloader
def snake_case_ ( self ) -> DataLoader:
'''simple docstring'''
return self.get_dataloader("""val""" , batch_size=self.hparams.eval_batch_size )
def snake_case_ ( self ) -> DataLoader:
'''simple docstring'''
return self.get_dataloader("""test""" , batch_size=self.hparams.eval_batch_size )
@staticmethod
def snake_case_ ( UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
BaseTransformer.add_model_specific_args(UpperCamelCase__ , UpperCamelCase__ )
add_generic_args(UpperCamelCase__ , UpperCamelCase__ )
parser.add_argument(
"""--max_source_length""" , default=1024 , type=UpperCamelCase__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--max_target_length""" , default=56 , type=UpperCamelCase__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--val_max_target_length""" , default=142 , type=UpperCamelCase__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--test_max_target_length""" , default=142 , type=UpperCamelCase__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument("""--freeze_encoder""" , action="""store_true""" )
parser.add_argument("""--freeze_embeds""" , action="""store_true""" )
parser.add_argument("""--sortish_sampler""" , action="""store_true""" , default=UpperCamelCase__ )
parser.add_argument("""--overwrite_output_dir""" , action="""store_true""" , default=UpperCamelCase__ )
parser.add_argument("""--max_tokens_per_batch""" , type=UpperCamelCase__ , default=UpperCamelCase__ )
parser.add_argument("""--logger_name""" , type=UpperCamelCase__ , choices=["""default""", """wandb""", """wandb_shared"""] , default="""default""" )
parser.add_argument("""--n_train""" , type=UpperCamelCase__ , default=-1 , required=UpperCamelCase__ , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_val""" , type=UpperCamelCase__ , default=500 , required=UpperCamelCase__ , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_test""" , type=UpperCamelCase__ , default=-1 , required=UpperCamelCase__ , help="""# examples. -1 means use all.""" )
parser.add_argument(
"""--task""" , type=UpperCamelCase__ , default="""summarization""" , required=UpperCamelCase__ , help="""# examples. -1 means use all.""" )
parser.add_argument("""--label_smoothing""" , type=UpperCamelCase__ , default=0.0 , required=UpperCamelCase__ )
parser.add_argument("""--src_lang""" , type=UpperCamelCase__ , default="""""" , required=UpperCamelCase__ )
parser.add_argument("""--tgt_lang""" , type=UpperCamelCase__ , default="""""" , required=UpperCamelCase__ )
parser.add_argument("""--eval_beams""" , type=UpperCamelCase__ , default=UpperCamelCase__ , required=UpperCamelCase__ )
parser.add_argument(
"""--val_metric""" , type=UpperCamelCase__ , default=UpperCamelCase__ , required=UpperCamelCase__ , choices=["""bleu""", """rouge2""", """loss""", None] )
parser.add_argument("""--eval_max_gen_length""" , type=UpperCamelCase__ , default=UpperCamelCase__ , help="""never generate more than n tokens""" )
parser.add_argument("""--save_top_k""" , type=UpperCamelCase__ , default=1 , required=UpperCamelCase__ , help="""How many checkpoints to save""" )
parser.add_argument(
"""--early_stopping_patience""" , type=UpperCamelCase__ , default=-1 , required=UpperCamelCase__ , help=(
"""-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
""" val_check_interval will effect it."""
) , )
return parser
class A__ ( _snake_case ):
lowercase = "translation"
lowercase = ["loss"]
lowercase = ["bleu"]
lowercase = "bleu"
def __init__( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(UpperCamelCase__ , **UpperCamelCase__ )
A_ = hparams.src_lang
A_ = hparams.tgt_lang
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> dict:
'''simple docstring'''
return calculate_bleu(UpperCamelCase__ , UpperCamelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__=None ) -> SummarizationModule:
Path(args.output_dir ).mkdir(exist_ok=UpperCAmelCase__ )
check_output_dir(UpperCAmelCase__, expected_items=3 )
if model is None:
if "summarization" in args.task:
A_ = SummarizationModule(UpperCAmelCase__ )
else:
A_ = TranslationModule(UpperCAmelCase__ )
A_ = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith("""/tmp""" )
or str(args.output_dir ).startswith("""/var""" )
):
A_ = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
A_ = os.environ.get("""WANDB_PROJECT""", UpperCAmelCase__ )
A_ = WandbLogger(name=model.output_dir.name, project=UpperCAmelCase__ )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
A_ = WandbLogger(name=model.output_dir.name, project=F'''hf_{dataset}''' )
if args.early_stopping_patience >= 0:
A_ = get_early_stopping_callback(model.val_metric, args.early_stopping_patience )
else:
A_ = False
A_ = args.val_metric == """loss"""
A_ = generic_train(
UpperCAmelCase__, UpperCAmelCase__, logging_callback=SeqaSeqLoggingCallback(), checkpoint_callback=get_checkpoint_callback(
args.output_dir, model.val_metric, args.save_top_k, UpperCAmelCase__ ), early_stopping_callback=UpperCAmelCase__, logger=UpperCAmelCase__, )
pickle_save(model.hparams, model.output_dir / """hparams.pkl""" )
if not args.do_predict:
return model
A_ = """"""
A_ = sorted(glob.glob(os.path.join(args.output_dir, """*.ckpt""" ), recursive=UpperCAmelCase__ ) )
if checkpoints:
A_ = checkpoints[-1]
A_ = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
__lowerCamelCase = pl.Trainer.add_argparse_args(parser)
__lowerCamelCase = SummarizationModule.add_model_specific_args(parser, os.getcwd())
__lowerCamelCase = parser.parse_args()
main(args)
| 101 | 0 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def _lowercase ( __A ):
'''simple docstring'''
return (data["data"], data["target"])
def _lowercase ( __A ,__A ,__A ):
'''simple docstring'''
__UpperCamelCase = XGBRegressor(verbosity=0 ,random_state=42 )
xgb.fit(__A ,__A )
# Predict target for test data
__UpperCamelCase = xgb.predict(__A )
__UpperCamelCase = predictions.reshape(len(__A ) ,1 )
return predictions
def _lowercase ( ):
'''simple docstring'''
__UpperCamelCase = fetch_california_housing()
__UpperCamelCase , __UpperCamelCase = data_handling(__A )
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = train_test_split(
__A ,__A ,test_size=0.25 ,random_state=1 )
__UpperCamelCase = xgboost(__A ,__A ,__A )
# Error printing
print(f"Mean Absolute Error : {mean_absolute_error(__A ,__A )}" )
print(f"Mean Square Error : {mean_squared_error(__A ,__A )}" )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 349 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def _lowercase ( __A ):
'''simple docstring'''
return (data["data"], data["target"])
def _lowercase ( __A ,__A ,__A ):
'''simple docstring'''
__UpperCamelCase = XGBRegressor(verbosity=0 ,random_state=42 )
xgb.fit(__A ,__A )
# Predict target for test data
__UpperCamelCase = xgb.predict(__A )
__UpperCamelCase = predictions.reshape(len(__A ) ,1 )
return predictions
def _lowercase ( ):
'''simple docstring'''
__UpperCamelCase = fetch_california_housing()
__UpperCamelCase , __UpperCamelCase = data_handling(__A )
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = train_test_split(
__A ,__A ,test_size=0.25 ,random_state=1 )
__UpperCamelCase = xgboost(__A ,__A ,__A )
# Error printing
print(f"Mean Absolute Error : {mean_absolute_error(__A ,__A )}" )
print(f"Mean Square Error : {mean_squared_error(__A ,__A )}" )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 349 | 1 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class UpperCamelCase_ :
def __init__( self , A , A=2 , A=3 , A=4 , A=2 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=36 , A=3 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.0_2 , A=6 , A=6 , A=3 , A=4 , A=None , A=1000 , ) -> Union[str, Any]:
UpperCAmelCase : List[str] = parent
UpperCAmelCase : Tuple = batch_size
UpperCAmelCase : Optional[int] = num_channels
UpperCAmelCase : Tuple = image_size
UpperCAmelCase : Dict = patch_size
UpperCAmelCase : Any = text_seq_length
UpperCAmelCase : List[Any] = is_training
UpperCAmelCase : Optional[int] = use_input_mask
UpperCAmelCase : Tuple = use_token_type_ids
UpperCAmelCase : Tuple = use_labels
UpperCAmelCase : Dict = vocab_size
UpperCAmelCase : List[Any] = hidden_size
UpperCAmelCase : List[str] = num_hidden_layers
UpperCAmelCase : Any = num_attention_heads
UpperCAmelCase : Any = intermediate_size
UpperCAmelCase : str = hidden_act
UpperCAmelCase : Optional[Any] = hidden_dropout_prob
UpperCAmelCase : List[Any] = attention_probs_dropout_prob
UpperCAmelCase : List[Any] = max_position_embeddings
UpperCAmelCase : List[Any] = type_vocab_size
UpperCAmelCase : Optional[int] = type_sequence_label_size
UpperCAmelCase : Union[str, Any] = initializer_range
UpperCAmelCase : Dict = coordinate_size
UpperCAmelCase : Optional[int] = shape_size
UpperCAmelCase : Tuple = num_labels
UpperCAmelCase : Tuple = num_choices
UpperCAmelCase : Optional[Any] = scope
UpperCAmelCase : Union[str, Any] = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
UpperCAmelCase : Any = text_seq_length
UpperCAmelCase : Optional[int] = (image_size // patch_size) ** 2 + 1
UpperCAmelCase : str = self.text_seq_length + self.image_seq_length
def _lowercase( self ) -> Any:
UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
UpperCAmelCase : str = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
UpperCAmelCase : Optional[Any] = bbox[i, j, 3]
UpperCAmelCase : int = bbox[i, j, 1]
UpperCAmelCase : Any = t
if bbox[i, j, 2] < bbox[i, j, 0]:
UpperCAmelCase : List[str] = bbox[i, j, 2]
UpperCAmelCase : List[str] = bbox[i, j, 0]
UpperCAmelCase : int = t
UpperCAmelCase : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : List[Any] = None
if self.use_input_mask:
UpperCAmelCase : List[str] = random_attention_mask([self.batch_size, self.text_seq_length] )
UpperCAmelCase : Any = None
if self.use_token_type_ids:
UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
UpperCAmelCase : Tuple = None
UpperCAmelCase : Optional[Any] = None
if self.use_labels:
UpperCAmelCase : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
UpperCAmelCase : Any = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def _lowercase( self , A , A , A , A , A , A , A , A ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = LayoutLMvaModel(config=A )
model.to(A )
model.eval()
# text + image
UpperCAmelCase : Dict = model(A , pixel_values=A )
UpperCAmelCase : List[Any] = model(
A , bbox=A , pixel_values=A , attention_mask=A , token_type_ids=A )
UpperCAmelCase : int = model(A , bbox=A , pixel_values=A , token_type_ids=A )
UpperCAmelCase : List[Any] = model(A , bbox=A , pixel_values=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
UpperCAmelCase : Optional[Any] = model(A )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
UpperCAmelCase : Optional[int] = model(pixel_values=A )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def _lowercase( self , A , A , A , A , A , A , A , A ) -> Dict:
UpperCAmelCase : Any = self.num_labels
UpperCAmelCase : List[str] = LayoutLMvaForSequenceClassification(A )
model.to(A )
model.eval()
UpperCAmelCase : Dict = model(
A , bbox=A , pixel_values=A , attention_mask=A , token_type_ids=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase( self , A , A , A , A , A , A , A , A ) -> Optional[Any]:
UpperCAmelCase : Any = self.num_labels
UpperCAmelCase : Optional[Any] = LayoutLMvaForTokenClassification(config=A )
model.to(A )
model.eval()
UpperCAmelCase : int = model(
A , bbox=A , pixel_values=A , attention_mask=A , token_type_ids=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def _lowercase( self , A , A , A , A , A , A , A , A ) -> Any:
UpperCAmelCase : Optional[Any] = LayoutLMvaForQuestionAnswering(config=A )
model.to(A )
model.eval()
UpperCAmelCase : List[str] = model(
A , bbox=A , pixel_values=A , attention_mask=A , token_type_ids=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 _lowercase( self ) -> int:
UpperCAmelCase : Optional[int] = self.prepare_config_and_inputs()
(
UpperCAmelCase
) : Optional[int] = config_and_inputs
UpperCAmelCase : Dict = {
"""input_ids""": input_ids,
"""bbox""": bbox,
"""pixel_values""": pixel_values,
"""token_type_ids""": token_type_ids,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_torch
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = False
lowercase = False
lowercase = False
lowercase = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowercase = (
{'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
if is_torch_available()
else {}
)
def _lowercase( self , A , A , A , A , A ) -> str:
# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
# the sequence dimension of the text embedding only.
# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
return True
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Any = LayoutLMvaModelTester(self )
UpperCAmelCase : List[str] = ConfigTester(self , config_class=A , hidden_size=37 )
def _lowercase( self , A , A , A=False ) -> Optional[int]:
UpperCAmelCase : Optional[Any] = copy.deepcopy(A )
if model_class in get_values(A ):
UpperCAmelCase : str = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(A , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(A ):
UpperCAmelCase : Any = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=A )
elif model_class in get_values(A ):
UpperCAmelCase : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A )
UpperCAmelCase : Any = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A )
elif model_class in [
*get_values(A ),
]:
UpperCAmelCase : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A )
elif model_class in [
*get_values(A ),
]:
UpperCAmelCase : Union[str, Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=A , )
return inputs_dict
def _lowercase( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _lowercase( self ) -> Any:
UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCAmelCase : List[Any] = type
self.model_tester.create_and_check_model(*A )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A )
def _lowercase( self ) -> int:
UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A )
@slow
def _lowercase( self ) -> int:
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : List[str] = LayoutLMvaModel.from_pretrained(A )
self.assertIsNotNone(A )
def __lowerCamelCase ( ) -> int:
UpperCAmelCase : Dict = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
@cached_property
def _lowercase( self ) -> List[Any]:
return LayoutLMvaImageProcessor(apply_ocr=A ) if is_vision_available() else None
@slow
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Union[str, Any] = LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" ).to(A )
UpperCAmelCase : Dict = self.default_image_processor
UpperCAmelCase : str = prepare_img()
UpperCAmelCase : List[str] = image_processor(images=A , return_tensors="""pt""" ).pixel_values.to(A )
UpperCAmelCase : Tuple = torch.tensor([[1, 2]] )
UpperCAmelCase : Union[str, Any] = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
UpperCAmelCase : Dict = model(
input_ids=input_ids.to(A ) , bbox=bbox.to(A ) , pixel_values=pixel_values.to(A ) , )
# verify the logits
UpperCAmelCase : List[Any] = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , A )
UpperCAmelCase : Tuple = torch.tensor(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(A )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , A , atol=1e-4 ) )
| 351 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCamelCase_ :
lowercase = MBartConfig
lowercase = {}
lowercase = 'gelu'
def __init__( self , A , A=13 , A=7 , A=True , A=False , A=99 , A=32 , A=2 , A=4 , A=37 , A=0.1 , A=0.1 , A=20 , A=2 , A=1 , A=0 , ) -> Optional[int]:
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : Tuple = seq_length
UpperCAmelCase : str = is_training
UpperCAmelCase : Optional[int] = use_labels
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = intermediate_size
UpperCAmelCase : Dict = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : Optional[Any] = eos_token_id
UpperCAmelCase : List[str] = pad_token_id
UpperCAmelCase : List[Any] = bos_token_id
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[str] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Union[str, Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : str = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[Any] = prepare_mbart_inputs_dict(A , A , A )
return config, inputs_dict
def _lowercase( self , A , A ) -> List[str]:
UpperCAmelCase : List[str] = TFMBartModel(config=A ).get_decoder()
UpperCAmelCase : int = inputs_dict["""input_ids"""]
UpperCAmelCase : str = input_ids[:1, :]
UpperCAmelCase : Optional[Any] = inputs_dict["""attention_mask"""][:1, :]
UpperCAmelCase : List[str] = inputs_dict["""head_mask"""]
UpperCAmelCase : List[Any] = 1
# first forward pass
UpperCAmelCase : List[str] = model(A , attention_mask=A , head_mask=A , use_cache=A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = outputs.to_tuple()
UpperCAmelCase : int = past_key_values[1]
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , ) -> List[str]:
if attention_mask is None:
UpperCAmelCase : Tuple = tf.cast(tf.math.not_equal(_lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : int = 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:
UpperCAmelCase : List[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : Tuple = 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 UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowercase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowercase = (
{
'conversational': TFMBartForConditionalGeneration,
'feature-extraction': TFMBartModel,
'summarization': TFMBartForConditionalGeneration,
'text2text-generation': TFMBartForConditionalGeneration,
'translation': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase = True
lowercase = False
lowercase = False
def _lowercase( self , A , A , A , A , A ) -> int:
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : int = TFMBartModelTester(self )
UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A )
def _lowercase( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Dict:
UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCamelCase_ ( unittest.TestCase ):
lowercase = [
' UN Chief Says There Is No Military Solution in Syria',
]
lowercase = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
]
lowercase = 'facebook/mbart-large-en-ro'
@cached_property
def _lowercase( self ) -> Any:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _lowercase( self , **A ) -> Any:
UpperCAmelCase : Optional[int] = self.translate_src_text(**A )
self.assertListEqual(self.expected_text , A )
def _lowercase( self , **A ) -> Optional[Any]:
UpperCAmelCase : List[str] = self.tokenizer(self.src_text , **A , return_tensors="""tf""" )
UpperCAmelCase : int = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
UpperCAmelCase : Any = self.tokenizer.batch_decode(A , skip_special_tokens=A )
return generated_words
@slow
def _lowercase( self ) -> List[Any]:
self._assert_generated_batch_equal_expected()
| 338 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCamelCase = get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')
class _A ( UpperCAmelCase__ , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Dict = BartphoTokenizer
_SCREAMING_SNAKE_CASE : Union[str, Any] = False
_SCREAMING_SNAKE_CASE : Optional[int] = True
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
__UpperCAmelCase : List[Any] = ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
__UpperCAmelCase : Dict = dict(zip(UpperCAmelCase__ , range(len(UpperCAmelCase__ ) ) ) )
__UpperCAmelCase : Union[str, Any] = {"""unk_token""": """<unk>"""}
__UpperCAmelCase : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""monolingual_vocab_file"""] )
with open(self.monolingual_vocab_file , """w""" , encoding="""utf-8""" ) as fp:
for token in vocab_tokens:
fp.write(f'{token} {vocab_tokens[token]}\n' )
__UpperCAmelCase : Union[str, Any] = BartphoTokenizer(UpperCAmelCase__ , self.monolingual_vocab_file , **self.special_tokens_map )
tokenizer.save_pretrained(self.tmpdirname )
def __A ( self , **__UpperCAmelCase ) -> Any:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return BartphoTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def __A ( self , __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : Any = """This is a là test"""
__UpperCAmelCase : List[Any] = """This is a<unk><unk> test"""
return input_text, output_text
def __A ( self ) -> int:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = BartphoTokenizer(UpperCAmelCase__ , self.monolingual_vocab_file , **self.special_tokens_map )
__UpperCAmelCase : Dict = """This is a là test"""
__UpperCAmelCase : Union[str, Any] = """▁This ▁is ▁a ▁l à ▁t est""".split()
__UpperCAmelCase : Tuple = tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
__UpperCAmelCase : Dict = tokens + [tokenizer.unk_token]
__UpperCAmelCase : str = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , UpperCAmelCase__ )
| 254 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
A__ = BeautifulSoup(requests.get(lowercase_ , params=lowercase_ ).content , '''html.parser''' )
A__ = soup.find('''div''' , attrs={'''class''': '''gs_ri'''} )
A__ = div.find('''div''' , attrs={'''class''': '''gs_fl'''} ).find_all('''a''' )
return anchors[2].get_text()
if __name__ == "__main__":
_lowerCamelCase : Optional[Any] = {
"""title""": (
"""Precisely geometry controlled microsupercapacitors for ultrahigh areal """
"""capacitance, volumetric capacitance, and energy density"""
),
"""journal""": """Chem. Mater.""",
"""volume""": 30,
"""pages""": """3979-3990""",
"""year""": 2018,
"""hl""": """en""",
}
print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))
| 14 | 0 |
import inspect
import unittest
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( self : List[str]) ->Optional[int]:
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def SCREAMING_SNAKE_CASE ( self : str) ->List[str]:
'''simple docstring'''
import diffusers
from diffusers.dependency_versions_table import deps
A__ = inspect.getmembers(UpperCAmelCase__ , inspect.isclass)
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
A__ = '''k-diffusion'''
elif backend == "invisible_watermark":
A__ = '''invisible-watermark'''
assert backend in deps, f"""{backend} is not in the deps table!"""
| 361 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( self : Dict) ->Tuple:
'''simple docstring'''
A__ = [[1, 2, 4], [1, 2, 3, 4]]
A__ = DisjunctiveConstraint(UpperCAmelCase__)
self.assertTrue(isinstance(dc.token_ids , UpperCAmelCase__))
with self.assertRaises(UpperCAmelCase__):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]))
with self.assertRaises(UpperCAmelCase__):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4]), torch.LongTensor([1, 2, 3, 4, 5])])
def SCREAMING_SNAKE_CASE ( self : int) ->str:
'''simple docstring'''
A__ = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(UpperCAmelCase__):
DisjunctiveConstraint(UpperCAmelCase__) # fails here
def SCREAMING_SNAKE_CASE ( self : Any) ->str:
'''simple docstring'''
A__ = [[1, 2, 3], [1, 2, 4]]
A__ = DisjunctiveConstraint(UpperCAmelCase__)
A__ , A__ , A__ = dc.update(1)
A__ = stepped is True and completed is False and reset is False
self.assertTrue(UpperCAmelCase__)
self.assertTrue(not dc.completed)
self.assertTrue(dc.current_seq == [1])
A__ , A__ , A__ = dc.update(2)
A__ = stepped is True and completed is False and reset is False
self.assertTrue(UpperCAmelCase__)
self.assertTrue(not dc.completed)
self.assertTrue(dc.current_seq == [1, 2])
A__ , A__ , A__ = dc.update(3)
A__ = stepped is True and completed is True and reset is False
self.assertTrue(UpperCAmelCase__)
self.assertTrue(dc.completed) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3])
def SCREAMING_SNAKE_CASE ( self : Optional[Any]) ->Tuple:
'''simple docstring'''
A__ = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
A__ = DisjunctiveConstraint(UpperCAmelCase__)
A__ , A__ , A__ = dc.update(1)
self.assertTrue(not dc.completed)
self.assertTrue(dc.current_seq == [1])
A__ , A__ , A__ = dc.update(2)
self.assertTrue(not dc.completed)
self.assertTrue(dc.current_seq == [1, 2])
A__ , A__ , A__ = dc.update(4)
self.assertTrue(not dc.completed)
self.assertTrue(dc.current_seq == [1, 2, 4])
A__ , A__ , A__ = dc.update(5)
self.assertTrue(dc.completed) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5])
dc.reset()
A__ , A__ , A__ = dc.update(1)
self.assertTrue(not dc.completed)
self.assertTrue(dc.remaining() == 3)
self.assertTrue(dc.current_seq == [1])
A__ , A__ , A__ = dc.update(2)
self.assertTrue(not dc.completed)
self.assertTrue(dc.remaining() == 2)
self.assertTrue(dc.current_seq == [1, 2])
A__ , A__ , A__ = dc.update(5)
self.assertTrue(dc.completed) # Completed!
self.assertTrue(dc.remaining() == 0)
self.assertTrue(dc.current_seq == [1, 2, 5])
| 231 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def __A ( a_ :int , a_ :int = 2 , a_ :int = 1 , a_ :int = 3 , ) -> int | None:
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError('''The input value cannot be less than 2''')
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(a_ :int , a_ :int , a_ :int) -> int:
return (pow(a_ , 2) + step) % modulus
for _ in range(a_):
# These track the position within the cycle detection logic.
__a : str = seed
__a : Optional[int] = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
__a : Dict = rand_fn(a_ , a_ , a_)
__a : str = rand_fn(a_ , a_ , a_)
__a : Optional[Any] = rand_fn(a_ , a_ , a_)
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
__a : Optional[int] = gcd(hare - tortoise , a_)
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
__a : Dict = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
A = argparse.ArgumentParser()
parser.add_argument(
'''num''',
type=int,
help='''The value to find a divisor of''',
)
parser.add_argument(
'''--attempts''',
type=int,
default=3,
help='''The number of attempts before giving up''',
)
A = parser.parse_args()
A = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F'{args.num} is probably prime')
else:
A = args.num // divisor
print(F'{args.num} = {divisor} * {quotient}') | 160 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
A = None
A = logging.get_logger(__name__)
A = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
A = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
},
'''tokenizer_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
},
}
# TODO(PVP) - this should be removed in Transformers v5
A = {
'''t5-small''': 512,
'''t5-base''': 512,
'''t5-large''': 512,
'''t5-3b''': 512,
'''t5-11b''': 512,
}
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = VOCAB_FILES_NAMES
__lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase = ['''input_ids''', '''attention_mask''']
__lowerCAmelCase = TaTokenizer
__lowerCAmelCase = []
def __init__( self , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase="</s>" , _UpperCAmelCase="<unk>" , _UpperCAmelCase="<pad>" , _UpperCAmelCase=100 , _UpperCAmelCase=None , **_UpperCAmelCase , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__a : Dict = [f"""<extra_id_{i}>""" for i in range(_UpperCAmelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
__a : Union[str, Any] = len(set(filter(lambda _UpperCAmelCase : bool('''extra_id_''' in str(_UpperCAmelCase ) ) , _UpperCAmelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
super().__init__(
_UpperCAmelCase , tokenizer_file=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , extra_ids=_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , **_UpperCAmelCase , )
__a : Union[str, Any] = vocab_file
__a : int = False if not self.vocab_file else True
__a : List[str] = extra_ids
@staticmethod
def _lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
__a : Any = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f""" {pretrained_model_name_or_path} automatically truncating your input to"""
f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _UpperCAmelCase , )
return max_model_length
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(_UpperCAmelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__a : Optional[Any] = os.path.join(
_UpperCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ):
copyfile(self.vocab_file , _UpperCAmelCase )
logger.info(f"""Copy vocab file to {out_vocab_file}""" )
return (out_vocab_file,)
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
__a : str = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
__a : List[str] = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
__a : Tuple = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def _lowerCamelCase ( self ):
return list(
set(filter(lambda _UpperCAmelCase : bool(re.search(R'''<extra_id_\d+>''' , _UpperCAmelCase ) ) is not None , self.additional_special_tokens ) ) )
def _lowerCamelCase ( self ):
return [self.convert_tokens_to_ids(_UpperCAmelCase ) for token in self.get_sentinel_tokens()] | 160 | 1 |
from bisect import bisect
from itertools import accumulate
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = sorted(zip(_lowercase , _lowercase ) , key=lambda _lowercase : x[0] / x[1] , reverse=_lowercase )
_A ,_A = [i[0] for i in r], [i[1] for i in r]
_A = list(accumulate(_lowercase ) )
_A = bisect(_lowercase , _lowercase )
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()
| 75 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
__A = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
print('Torch version:', None)
try:
import deepspeed
print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
print('DeepSpeed version:', None)
try:
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
print('TensorFlow version:', None)
| 75 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : int ) -> int:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError('Input must be an integer' )
if input_num <= 0:
raise ValueError('Input must be positive' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 47 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCamelCase : Optional[int] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
@dataclass
class A__ :
A__ = field(
default='cifar10' , metadata={'help': 'Name of a dataset from the datasets package'} )
A__ = field(
default=A__ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
A__ = field(
default=A__ , metadata={'help': 'The column name of the images in the files.'} )
A__ = field(default=A__ , metadata={'help': 'A folder containing the training data.'} )
A__ = field(default=A__ , metadata={'help': 'A folder containing the validation data.'} )
A__ = field(
default=0.15 , metadata={'help': 'Percent to split off of train for validation.'} )
A__ = field(
default=A__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
A__ = field(
default=A__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
def A ( self : Union[str, Any] ) -> int:
'''simple docstring'''
_SCREAMING_SNAKE_CASE ={}
if self.train_dir is not None:
_SCREAMING_SNAKE_CASE =self.train_dir
if self.validation_dir is not None:
_SCREAMING_SNAKE_CASE =self.validation_dir
_SCREAMING_SNAKE_CASE =data_files if data_files else None
@dataclass
class A__ :
A__ = field(
default=A__ , metadata={
'help': (
'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
)
} , )
A__ = field(
default=A__ , metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'} )
A__ = field(
default=A__ , metadata={
'help': (
'Override some existing default config settings when a model is trained from scratch. Example: '
'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
)
} , )
A__ = field(
default=A__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'} )
A__ = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
A__ = field(default=A__ , metadata={'help': 'Name or path of preprocessor config.'} )
A__ = field(
default=A__ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
A__ = field(
default=0.75 , metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'} )
A__ = field(
default=A__ , metadata={'help': 'Whether or not to train with normalized pixel values as target.'} )
@dataclass
class A__ ( A__ ):
A__ = field(
default=1E-3 , metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'} )
def _lowerCAmelCase ( _UpperCamelCase : int ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =torch.stack([example['pixel_values'] for example in examples] )
return {"pixel_values": pixel_values}
def _lowerCAmelCase ( ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
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.
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =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_mae' , _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()
_SCREAMING_SNAKE_CASE =training_args.get_process_log_level()
logger.setLevel(_UpperCamelCase )
transformers.utils.logging.set_verbosity(_UpperCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(f"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
_SCREAMING_SNAKE_CASE =None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_SCREAMING_SNAKE_CASE =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.' )
# Initialize our dataset.
_SCREAMING_SNAKE_CASE =load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
_SCREAMING_SNAKE_CASE =None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _UpperCamelCase ) and data_args.train_val_split > 0.0:
_SCREAMING_SNAKE_CASE =ds['train'].train_test_split(data_args.train_val_split )
_SCREAMING_SNAKE_CASE =split['train']
_SCREAMING_SNAKE_CASE =split['test']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_SCREAMING_SNAKE_CASE ={
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name:
_SCREAMING_SNAKE_CASE =ViTMAEConfig.from_pretrained(model_args.config_name , **_UpperCamelCase )
elif model_args.model_name_or_path:
_SCREAMING_SNAKE_CASE =ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **_UpperCamelCase )
else:
_SCREAMING_SNAKE_CASE =ViTMAEConfig()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(f"New config: {config}" )
# adapt config
config.update(
{
'mask_ratio': model_args.mask_ratio,
'norm_pix_loss': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
_SCREAMING_SNAKE_CASE =ViTImageProcessor.from_pretrained(model_args.image_processor_name , **_UpperCamelCase )
elif model_args.model_name_or_path:
_SCREAMING_SNAKE_CASE =ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **_UpperCamelCase )
else:
_SCREAMING_SNAKE_CASE =ViTImageProcessor()
# create model
if model_args.model_name_or_path:
_SCREAMING_SNAKE_CASE =ViTMAEForPreTraining.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 , )
else:
logger.info('Training new model from scratch' )
_SCREAMING_SNAKE_CASE =ViTMAEForPreTraining(_UpperCamelCase )
if training_args.do_train:
_SCREAMING_SNAKE_CASE =ds['train'].column_names
else:
_SCREAMING_SNAKE_CASE =ds['validation'].column_names
if data_args.image_column_name is not None:
_SCREAMING_SNAKE_CASE =data_args.image_column_name
elif "image" in column_names:
_SCREAMING_SNAKE_CASE ='image'
elif "img" in column_names:
_SCREAMING_SNAKE_CASE ='img'
else:
_SCREAMING_SNAKE_CASE =column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
_SCREAMING_SNAKE_CASE =image_processor.size['shortest_edge']
else:
_SCREAMING_SNAKE_CASE =(image_processor.size['height'], image_processor.size['width'])
_SCREAMING_SNAKE_CASE =Compose(
[
Lambda(lambda _UpperCamelCase : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(_UpperCamelCase , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(_UpperCamelCase : Dict ):
_SCREAMING_SNAKE_CASE =[transforms(_UpperCamelCase ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
_SCREAMING_SNAKE_CASE =ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_UpperCamelCase )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
_SCREAMING_SNAKE_CASE =(
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_UpperCamelCase )
# Compute absolute learning rate
_SCREAMING_SNAKE_CASE =(
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
_SCREAMING_SNAKE_CASE =training_args.base_learning_rate * total_train_batch_size / 2_56
# Initialize our trainer
_SCREAMING_SNAKE_CASE =Trainer(
model=_UpperCamelCase , args=_UpperCamelCase , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=_UpperCamelCase , data_collator=_UpperCamelCase , )
# Training
if training_args.do_train:
_SCREAMING_SNAKE_CASE =None
if training_args.resume_from_checkpoint is not None:
_SCREAMING_SNAKE_CASE =training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_SCREAMING_SNAKE_CASE =last_checkpoint
_SCREAMING_SNAKE_CASE =trainer.train(resume_from_checkpoint=_UpperCamelCase )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_SCREAMING_SNAKE_CASE =trainer.evaluate()
trainer.log_metrics('eval' , _UpperCamelCase )
trainer.save_metrics('eval' , _UpperCamelCase )
# Write model card and (optionally) push to hub
_SCREAMING_SNAKE_CASE ={
'tasks': 'masked-auto-encoding',
'dataset': data_args.dataset_name,
'tags': ['masked-auto-encoding'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCamelCase )
else:
trainer.create_model_card(**_UpperCamelCase )
def _lowerCAmelCase ( _UpperCamelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 47 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 204 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase : Optional[Any] = {
'''configuration_efficientformer''': [
'''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''EfficientFormerConfig''',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = ['''EfficientFormerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Optional[int] = [
'''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''EfficientFormerForImageClassification''',
'''EfficientFormerForImageClassificationWithTeacher''',
'''EfficientFormerModel''',
'''EfficientFormerPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = [
'''TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFEfficientFormerForImageClassification''',
'''TFEfficientFormerForImageClassificationWithTeacher''',
'''TFEfficientFormerModel''',
'''TFEfficientFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientformer import EfficientFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientformer import (
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
EfficientFormerPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
TFEfficientFormerPreTrainedModel,
)
else:
import sys
__lowerCamelCase : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 204 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __magic_name__ ( UpperCAmelCase__ ):
def __init__( self , __snake_case , __snake_case ) -> List[Any]:
'''simple docstring'''
super().__init__()
# make sure scheduler can always be converted to DDIM
__a =DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=__snake_case , scheduler=__snake_case )
@torch.no_grad()
def __call__( self , __snake_case = 1 , __snake_case = None , __snake_case = 0.0 , __snake_case = 50 , __snake_case = None , __snake_case = "pil" , __snake_case = True , ) -> List[Any]:
'''simple docstring'''
if isinstance(self.unet.config.sample_size , __snake_case ):
__a =(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
__a =(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(__snake_case , __snake_case ) and len(__snake_case ) != batch_size:
raise ValueError(
f'You have passed a list of generators of length {len(__snake_case )}, but requested an effective batch'
f' size of {batch_size}. Make sure the batch size matches the length of the generators.' )
__a =randn_tensor(__snake_case , generator=__snake_case , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(__snake_case )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
__a =self.unet(__snake_case , __snake_case ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
__a =self.scheduler.step(
__snake_case , __snake_case , __snake_case , eta=__snake_case , use_clipped_model_output=__snake_case , generator=__snake_case ).prev_sample
__a =(image / 2 + 0.5).clamp(0 , 1 )
__a =image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__a =self.numpy_to_pil(__snake_case )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__snake_case )
| 218 | '''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __lowerCAmelCase ( UpperCamelCase__ ) -> list[list[float]]:
__lowerCamelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(UpperCamelCase__ ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
__lowerCamelCase = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError('''This matrix has no inverse.''' )
# Creates a copy of the matrix with swapped positions of the elements
__lowerCamelCase = [[0.0, 0.0], [0.0, 0.0]]
__lowerCamelCase , __lowerCamelCase = matrix[1][1], matrix[0][0]
__lowerCamelCase , __lowerCamelCase = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(UpperCamelCase__ ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(UpperCamelCase__ ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
__lowerCamelCase = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError('''This matrix has no inverse.''' )
# Creating cofactor matrix
__lowerCamelCase = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
__lowerCamelCase = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
__lowerCamelCase = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
__lowerCamelCase = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
__lowerCamelCase = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
__lowerCamelCase = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
__lowerCamelCase = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
__lowerCamelCase = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
__lowerCamelCase = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
__lowerCamelCase = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
__lowerCamelCase = array(UpperCamelCase__ )
for i in range(3 ):
for j in range(3 ):
__lowerCamelCase = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
__lowerCamelCase = array(UpperCamelCase__ )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(UpperCamelCase__ )
# Calculate the inverse of the matrix
return [[float(d(UpperCamelCase__ ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError('''Please provide a matrix of size 2x2 or 3x3.''' )
| 67 | 0 |
"""simple docstring"""
import os
def SCREAMING_SNAKE_CASE ( snake_case_ : str ):
snake_case__ : str = len(grid[0] )
snake_case__ : List[str] = len(snake_case_ )
snake_case__ : Any = 0
snake_case__ : Dict = 0
snake_case__ : Any = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(snake_case_ ):
for j in range(n_rows - 3 ):
snake_case__ : Optional[Any] = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
snake_case__ : int = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
snake_case__ : Any = (
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
snake_case__ : str = (
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
snake_case__ : Union[str, Any] = max(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
if max_product > largest:
snake_case__ : str = max_product
return largest
def SCREAMING_SNAKE_CASE ( ):
snake_case__ : List[Any] = []
with open(os.path.dirname(snake_case_ ) + "/grid.txt" ) as file:
for line in file:
grid.append(line.strip("\n" ).split(" " ) )
snake_case__ : str = [[int(snake_case_ ) for i in grid[j]] for j in range(len(snake_case_ ) )]
return largest_product(snake_case_ )
if __name__ == "__main__":
print(solution())
| 351 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ , unittest.TestCase ):
"""simple docstring"""
a_ = PhobertTokenizer
a_ = False
def _lowercase ( self : List[str] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case__ : Optional[int] = ["T@@", "i", "I", "R@@", "r", "e@@"]
snake_case__ : int = dict(zip(__A , range(len(__A ) ) ) )
snake_case__ : Dict = ["#version: 0.2", "l à</w>"]
snake_case__ : Optional[Any] = {"unk_token": "<unk>"}
snake_case__ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
snake_case__ : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
for token in vocab_tokens:
fp.write(f'''{token} {vocab_tokens[token]}\n''' )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(__A ) )
def _lowercase ( self : List[str] , **__A : Any ):
kwargs.update(self.special_tokens_map )
return PhobertTokenizer.from_pretrained(self.tmpdirname , **__A )
def _lowercase ( self : Tuple , __A : List[Any] ):
snake_case__ : str = "Tôi là VinAI Research"
snake_case__ : int = "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
return input_text, output_text
def _lowercase ( self : Optional[int] ):
snake_case__ : int = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case__ : Tuple = "Tôi là VinAI Research"
snake_case__ : List[Any] = "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
snake_case__ : int = tokenizer.tokenize(__A )
print(__A )
self.assertListEqual(__A , __A )
snake_case__ : Any = tokens + [tokenizer.unk_token]
snake_case__ : Any = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
| 286 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[Any] = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisionConfig",
],
"processing_clipseg": ["CLIPSegProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = [
"CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
"CLIPSegModel",
"CLIPSegPreTrainedModel",
"CLIPSegTextModel",
"CLIPSegVisionModel",
"CLIPSegForImageSegmentation",
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
__A : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 120 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class __snake_case ( _SCREAMING_SNAKE_CASE):
"""simple docstring"""
pass
class __snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase : Any ) -> None:
lowerCAmelCase_ : Any = data
lowerCAmelCase_ : Node | None = None
def __iter__( self : Union[str, Any] ) -> Optional[Any]:
lowerCAmelCase_ : Union[str, Any] = self
lowerCAmelCase_ : Any = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(lowerCamelCase )
yield node.data
lowerCAmelCase_ : int = node.next_node
@property
def __lowercase ( self : str ) -> bool:
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
__A : Dict = Node(1)
__A : Optional[Any] = Node(2)
__A : int = Node(3)
__A : Optional[Any] = Node(4)
print(root_node.has_loop) # False
__A : Any = root_node.next_node
print(root_node.has_loop) # True
__A : List[Any] = Node(5)
__A : Dict = Node(6)
__A : str = Node(5)
__A : Dict = Node(6)
print(root_node.has_loop) # False
__A : Optional[int] = Node(1)
print(root_node.has_loop) # False
| 120 | 1 |
"""simple docstring"""
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
UpperCAmelCase_ : Union[str, Any] = object()
# For specifying empty leaf dict `{}`
UpperCAmelCase_ : Union[str, Any] = object()
def UpperCamelCase ( _A : Optional[int] , _A : Dict )-> List[str]:
"""simple docstring"""
A__ = tuple((re.compile(x + "$" ) for x in qs) )
for i in range(len(lowerCamelCase__ ) - len(lowerCamelCase__ ) + 1 ):
A__ = [x.match(lowerCamelCase__ ) for x, y in zip(lowerCamelCase__ , ks[i:] )]
if matches and all(lowerCamelCase__ ):
return True
return False
def UpperCamelCase ( _A : List[str] )-> str:
"""simple docstring"""
def replace(_A : Dict , _A : Any ):
for rule, replacement in rules:
if _match(lowerCamelCase__ , lowerCamelCase__ ):
return replacement
return val
return replace
def UpperCamelCase ( )-> str:
"""simple docstring"""
return [
# embeddings
(("transformer", "wpe", "embedding"), P("mp" , lowerCamelCase__ )),
(("transformer", "wte", "embedding"), P("mp" , lowerCamelCase__ )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(lowerCamelCase__ , "mp" )),
(("attention", "out_proj", "kernel"), P("mp" , lowerCamelCase__ )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(lowerCamelCase__ , "mp" )),
(("mlp", "c_fc", "bias"), P("mp" )),
(("mlp", "c_proj", "kernel"), P("mp" , lowerCamelCase__ )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def UpperCamelCase ( _A : Union[str, Any] )-> Any:
"""simple docstring"""
A__ = _get_partition_rules()
A__ = _replacement_rules(lowerCamelCase__ )
A__ = {k: _unmatched for k in flatten_dict(lowerCamelCase__ )}
A__ = {k: replace(lowerCamelCase__ , lowerCamelCase__ ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(lowerCamelCase__ ) )
| 350 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
UpperCAmelCase_ : List[Any] = [
"python",
"tqdm",
"regex",
"requests",
"packaging",
"filelock",
"numpy",
"tokenizers",
"huggingface-hub",
"safetensors",
"accelerate",
"pyyaml",
]
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
elif pkg == "accelerate":
# must be loaded here, or else tqdm check may fail
from .utils import is_accelerate_available
# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
# Transformers with PyTorch
if not is_accelerate_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(F'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')
def UpperCamelCase ( _A : List[Any] , _A : int=None )-> Optional[int]:
"""simple docstring"""
require_version(deps[pkg] , _A )
| 198 | 0 |
'''simple docstring'''
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def __lowerCamelCase ( lowerCAmelCase_ = True , *lowerCAmelCase_ , **lowerCAmelCase_ ) -> Dict:
if not is_tqdm_available():
raise ImportError('Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.' )
_a : Tuple = False
if main_process_only:
_a : str = PartialState().local_process_index == 0
return _tqdm(*lowerCAmelCase_ , **lowerCAmelCase_ , disable=lowerCAmelCase_ )
| 89 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCamelCase : Optional[int] = {
'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}
class lowercase__ ( UpperCamelCase_):
UpperCamelCase_ = """nllb-moe"""
UpperCamelCase_ = ["""past_key_values"""]
UpperCamelCase_ = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : List[str] , UpperCamelCase__ : List[str]=12_8112 , UpperCamelCase__ : str=1024 , UpperCamelCase__ : Optional[int]=12 , UpperCamelCase__ : Union[str, Any]=4096 , UpperCamelCase__ : Optional[Any]=16 , UpperCamelCase__ : Optional[Any]=12 , UpperCamelCase__ : str=4096 , UpperCamelCase__ : Dict=16 , UpperCamelCase__ : Any=0.05 , UpperCamelCase__ : Any=0.05 , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : List[Any]="relu" , UpperCamelCase__ : Union[str, Any]=1024 , UpperCamelCase__ : Optional[int]=0.1 , UpperCamelCase__ : Any=0.1 , UpperCamelCase__ : Optional[Any]=0.0 , UpperCamelCase__ : str=0.02 , UpperCamelCase__ : Tuple=2 , UpperCamelCase__ : List[str]=True , UpperCamelCase__ : Dict=False , UpperCamelCase__ : Any="float32" , UpperCamelCase__ : List[Any]=False , UpperCamelCase__ : Tuple=128 , UpperCamelCase__ : Tuple=64 , UpperCamelCase__ : List[str]=4 , UpperCamelCase__ : Union[str, Any]=4 , UpperCamelCase__ : Dict=0.001 , UpperCamelCase__ : Optional[Any]=0.001 , UpperCamelCase__ : Optional[Any]="all" , UpperCamelCase__ : Tuple=False , UpperCamelCase__ : List[Any]=False , UpperCamelCase__ : List[Any]=1.0 , UpperCamelCase__ : str=0.2 , UpperCamelCase__ : List[Any]=1 , UpperCamelCase__ : List[Any]=0 , UpperCamelCase__ : Optional[int]=2 , UpperCamelCase__ : Tuple=False , **UpperCamelCase__ : Union[str, Any] , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = vocab_size
SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
SCREAMING_SNAKE_CASE : int = d_model
SCREAMING_SNAKE_CASE : Any = encoder_ffn_dim
SCREAMING_SNAKE_CASE : List[Any] = encoder_layers
SCREAMING_SNAKE_CASE : Union[str, Any] = encoder_attention_heads
SCREAMING_SNAKE_CASE : List[str] = decoder_ffn_dim
SCREAMING_SNAKE_CASE : Dict = decoder_layers
SCREAMING_SNAKE_CASE : List[Any] = decoder_attention_heads
SCREAMING_SNAKE_CASE : List[str] = dropout
SCREAMING_SNAKE_CASE : Any = attention_dropout
SCREAMING_SNAKE_CASE : Union[str, Any] = activation_dropout
SCREAMING_SNAKE_CASE : List[Any] = activation_function
SCREAMING_SNAKE_CASE : Union[str, Any] = init_std
SCREAMING_SNAKE_CASE : int = encoder_layerdrop
SCREAMING_SNAKE_CASE : List[Any] = decoder_layerdrop
SCREAMING_SNAKE_CASE : Any = use_cache
SCREAMING_SNAKE_CASE : str = encoder_layers
SCREAMING_SNAKE_CASE : List[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE : List[str] = router_z_loss_coef
SCREAMING_SNAKE_CASE : List[str] = router_aux_loss_coef
SCREAMING_SNAKE_CASE : int = decoder_sparse_step
SCREAMING_SNAKE_CASE : Optional[int] = encoder_sparse_step
SCREAMING_SNAKE_CASE : List[str] = num_experts
SCREAMING_SNAKE_CASE : int = expert_capacity
SCREAMING_SNAKE_CASE : Any = router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""" )
SCREAMING_SNAKE_CASE : str = router_dtype
SCREAMING_SNAKE_CASE : List[Any] = router_ignore_padding_tokens
SCREAMING_SNAKE_CASE : int = batch_prioritized_routing
SCREAMING_SNAKE_CASE : str = second_expert_policy
SCREAMING_SNAKE_CASE : Optional[Any] = normalize_router_prob_before_dropping
SCREAMING_SNAKE_CASE : Optional[Any] = moe_eval_capacity_token_fraction
SCREAMING_SNAKE_CASE : Optional[int] = moe_token_dropout
SCREAMING_SNAKE_CASE : Optional[int] = output_router_logits
super().__init__(
pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , is_encoder_decoder=UpperCamelCase__ , decoder_start_token_id=UpperCamelCase__ , **UpperCamelCase__ , )
| 182 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class UpperCamelCase__:
def __init__( self ,__UpperCAmelCase ) -> None:
A__ = value
A__ = None
A__ = None
class UpperCamelCase__:
def __init__( self ,__UpperCAmelCase ) -> None:
A__ = tree
def snake_case__ ( self ,__UpperCAmelCase ) -> int:
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self ) -> Iterator[int]:
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 154 | """simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__lowerCamelCase = 20_48
__lowerCamelCase = 40_96
__lowerCamelCase = 42
__lowerCamelCase = os.environ.pop("PROCESS_TRAIN", "false")
__lowerCamelCase = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def UpperCAmelCase ( UpperCamelCase__ ):
"""simple docstring"""
def choose_first(UpperCamelCase__ , UpperCamelCase__=False ):
assert isinstance(UpperCamelCase__ , UpperCamelCase__ )
if len(UpperCamelCase__ ) == 1:
A__ = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
A__ = {k: [a[k]] for k in a}
if len(a['start_token'] ) > 0:
break
return a
A__ = {'id': example['id']}
A__ = example['annotations']
A__ = annotation['yes_no_answer']
if 0 in yes_no_answer or 1 in yes_no_answer:
A__ = ['yes'] if 1 in yes_no_answer else ['no']
A__ = A__ = []
A__ = A__ = []
A__ = ['<cls>']
else:
A__ = ['short']
A__ = choose_first(annotation['short_answers'] )
if len(out['start_token'] ) == 0:
# answer will be long if short is not available
A__ = ['long']
A__ = choose_first(annotation['long_answer'] , is_long_answer=UpperCamelCase__ )
A__ = []
answer.update(UpperCamelCase__ )
# disregard some samples
if len(answer['start_token'] ) > 1 or answer["start_token"] == answer["end_token"]:
A__ = True
else:
A__ = False
A__ = ['start_token', 'end_token', 'start_byte', 'end_byte', 'text']
if not all(isinstance(answer[k] , UpperCamelCase__ ) for k in cols ):
raise ValueError('Issue in ID' , example['id'] )
return answer
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__=False ):
"""simple docstring"""
A__ = _get_single_answer(UpperCamelCase__ )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
A__ = example['document']['tokens']
A__ = []
for i in range(len(doc['token'] ) ):
if not doc["is_html"][i]:
context.append(doc['token'][i] )
return {
"context": " ".join(UpperCamelCase__ ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
A__ = ['start_token', 'end_token']
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
A__ = example['document']['tokens']
A__ = answer['start_token']
A__ = answer['end_token']
A__ = []
for i in range(len(doc['token'] ) ):
if not doc["is_html"][i]:
context.append(doc['token'][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
A__ = ' '.join(context[start_token:end_token] )
# checking above code
if assertion:
A__ = doc['is_html'][answer['start_token'] : answer['end_token']]
A__ = doc['token'][answer['start_token'] : answer['end_token']]
A__ = ' '.join([old[i] for i in range(len(UpperCamelCase__ ) ) if not is_html[i]] )
if new != old:
print('ID:' , example['id'] )
print('New:' , UpperCamelCase__ , end='\n' )
print('Old:' , UpperCamelCase__ , end='\n\n' )
return {
"context": " ".join(UpperCamelCase__ ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=2_048 , UpperCamelCase__=4_096 , UpperCamelCase__=True ):
"""simple docstring"""
A__ = get_context_and_ans(UpperCamelCase__ , assertion=UpperCamelCase__ )
A__ = out['answer']
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
A__ = tokenizer(example['question']['text'] , out['context'] ).input_ids
A__ = input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
A__ = []
A__ = []
A__ = input_ids[:q_len]
A__ = range(UpperCamelCase__ , len(UpperCamelCase__ ) , max_length - doc_stride )
for i in doc_start_indices:
A__ = i + max_length - q_len
A__ = input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer['category'][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(UpperCamelCase__ ),
"end_token": [-100] * len(UpperCamelCase__ ),
"category": category,
},
}
A__ = out['context'].split()
A__ = splitted_context[answer['end_token']]
A__ = len(
tokenizer(
' '.join(splitted_context[: answer['start_token']] ) , add_special_tokens=UpperCamelCase__ , ).input_ids )
A__ = len(
tokenizer(' '.join(splitted_context[: answer['end_token']] ) , add_special_tokens=UpperCamelCase__ ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
A__ = len(tokenizer(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
A__ = input_ids[answer['start_token'] : answer['end_token'] + 1] # right & left are inclusive
A__ = answer['start_token']
A__ = answer['end_token']
if assertion:
A__ = tokenizer.decode(UpperCamelCase__ )
if answer["span"] != new:
print('ISSUE IN TOKENIZATION' )
print('OLD:' , answer['span'] )
print('NEW:' , UpperCamelCase__ , end='\n\n' )
if len(UpperCamelCase__ ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
A__ = input_ids[:q_len]
A__ = range(UpperCamelCase__ , len(UpperCamelCase__ ) , max_length - doc_stride )
A__ = []
A__ = []
A__ = []
A__ = [] # null, yes, no, long, short
for i in doc_start_indices:
A__ = i + max_length - q_len
A__ = input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
A__ = start_token - i + q_len
A__ = end_token - i + q_len
answers_category.append(answer['category'][0] ) # ["short"] -> "short"
else:
A__ = -100
A__ = -100
answers_category.append('null' )
A__ = inputs[-1][start_token : end_token + 1]
answers_start_token.append(UpperCamelCase__ )
answers_end_token.append(UpperCamelCase__ )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print('ISSUE in strided for ID:' , example['id'] )
print('New:' , tokenizer.decode(UpperCamelCase__ ) )
print('Old:' , tokenizer.decode(UpperCamelCase__ ) , end='\n\n' )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=2_048 , UpperCamelCase__=4_096 , UpperCamelCase__=False ):
"""simple docstring"""
A__ = get_strided_contexts_and_ans(
UpperCamelCase__ , UpperCamelCase__ , doc_stride=UpperCamelCase__ , max_length=UpperCamelCase__ , assertion=UpperCamelCase__ , )
return example
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
with jsonlines.open(UpperCamelCase__ , 'a' ) as writer:
for example in tqdm(UpperCamelCase__ , total=len(UpperCamelCase__ ) , desc='Saving samples ... ' ):
A__ = example['labels']
for ids, start, end, cat in zip(
example['input_ids'] , labels['start_token'] , labels['end_token'] , labels['category'] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
'input_ids': ids,
'start_token': start,
'end_token': end,
'category': CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
__lowerCamelCase = load_dataset("natural_questions")
__lowerCamelCase = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
__lowerCamelCase = data["train" if PROCESS_TRAIN == "true" else "validation"]
__lowerCamelCase = {
"tokenizer": tokenizer,
"doc_stride": DOC_STRIDE,
"max_length": MAX_LENGTH,
"assertion": False,
}
__lowerCamelCase = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
__lowerCamelCase = data.remove_columns(["annotations", "document", "id", "question"])
print(data)
np.random.seed(SEED)
__lowerCamelCase = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
save_to_disk(data, file_name=cache_file_name)
| 154 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int=7 , UpperCAmelCase__ : Dict=3 , UpperCAmelCase__ : List[Any]=30 , UpperCAmelCase__ : Any=400 , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : Any=True , UpperCAmelCase__ : Optional[Any]=[0.5, 0.5, 0.5] , UpperCAmelCase__ : Any=[0.5, 0.5, 0.5] , UpperCAmelCase__ : List[str]=True , UpperCAmelCase__ : Optional[int]=1 / 255 , UpperCAmelCase__ : Optional[Any]=True , ) ->str:
'''simple docstring'''
A__ = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
A__ = parent
A__ = batch_size
A__ = num_channels
A__ = min_resolution
A__ = max_resolution
A__ = do_resize
A__ = size
A__ = do_normalize
A__ = image_mean
A__ = image_std
A__ = do_rescale
A__ = rescale_factor
A__ = do_pad
def SCREAMING_SNAKE_CASE ( self : Any) ->List[str]:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int=False) ->Optional[Any]:
'''simple docstring'''
if not batched:
A__ = image_inputs[0]
if isinstance(UpperCAmelCase__ , Image.Image):
A__ , A__ = image.size
else:
A__ , A__ = image.shape[1], image.shape[2]
if w < h:
A__ = int(self.size['''shortest_edge'''] * h / w)
A__ = self.size['''shortest_edge''']
elif w > h:
A__ = self.size['''shortest_edge''']
A__ = int(self.size['''shortest_edge'''] * w / h)
else:
A__ = self.size['''shortest_edge''']
A__ = self.size['''shortest_edge''']
else:
A__ = []
for image in image_inputs:
A__ , A__ = self.get_expected_values([image])
expected_values.append((expected_height, expected_width))
A__ = max(UpperCAmelCase__ , key=lambda UpperCAmelCase__: item[0])[0]
A__ = max(UpperCAmelCase__ , key=lambda UpperCAmelCase__: item[1])[1]
return expected_height, expected_width
@require_torch
@require_vision
class UpperCamelCase_ ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = DeformableDetrImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE ( self : List[str]) ->Tuple:
'''simple docstring'''
A__ = DeformableDetrImageProcessingTester(self)
@property
def SCREAMING_SNAKE_CASE ( self : Optional[int]) ->Any:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE ( self : List[str]) ->List[str]:
'''simple docstring'''
A__ = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(UpperCAmelCase__ , '''image_mean'''))
self.assertTrue(hasattr(UpperCAmelCase__ , '''image_std'''))
self.assertTrue(hasattr(UpperCAmelCase__ , '''do_normalize'''))
self.assertTrue(hasattr(UpperCAmelCase__ , '''do_resize'''))
self.assertTrue(hasattr(UpperCAmelCase__ , '''do_rescale'''))
self.assertTrue(hasattr(UpperCAmelCase__ , '''do_pad'''))
self.assertTrue(hasattr(UpperCAmelCase__ , '''size'''))
def SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->int:
'''simple docstring'''
A__ = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1_333})
self.assertEqual(image_processor.do_pad , UpperCAmelCase__)
A__ = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCAmelCase__)
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84})
self.assertEqual(image_processor.do_pad , UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Any) ->List[str]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self : Optional[Any]) ->Dict:
'''simple docstring'''
A__ = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__)
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , Image.Image)
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors='''pt''').pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCAmelCase__)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCAmelCase__ , batched=UpperCAmelCase__)
A__ = image_processing(UpperCAmelCase__ , return_tensors='''pt''').pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE ( self : int) ->Optional[int]:
'''simple docstring'''
A__ = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , numpify=UpperCAmelCase__)
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , np.ndarray)
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors='''pt''').pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCAmelCase__)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ = image_processing(UpperCAmelCase__ , return_tensors='''pt''').pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCAmelCase__ , batched=UpperCAmelCase__)
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE ( self : int) ->Tuple:
'''simple docstring'''
A__ = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , torchify=UpperCAmelCase__)
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , torch.Tensor)
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors='''pt''').pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCAmelCase__)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ = image_processing(UpperCAmelCase__ , return_tensors='''pt''').pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCAmelCase__ , batched=UpperCAmelCase__)
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->List[str]:
'''simple docstring'''
A__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''') as f:
A__ = json.loads(f.read())
A__ = {'''image_id''': 39_769, '''annotations''': target}
# encode them
A__ = DeformableDetrImageProcessor()
A__ = image_processing(images=UpperCAmelCase__ , annotations=UpperCAmelCase__ , return_tensors='''pt''')
# verify pixel values
A__ = torch.Size([1, 3, 800, 1_066])
self.assertEqual(encoding['''pixel_values'''].shape , UpperCAmelCase__)
A__ = torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCAmelCase__ , atol=1e-4))
# verify area
A__ = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCAmelCase__))
# verify boxes
A__ = torch.Size([6, 4])
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCAmelCase__)
A__ = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCAmelCase__ , atol=1e-3))
# verify image_id
A__ = torch.tensor([39_769])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCAmelCase__))
# verify is_crowd
A__ = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCAmelCase__))
# verify class_labels
A__ = torch.tensor([75, 75, 63, 65, 17, 17])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCAmelCase__))
# verify orig_size
A__ = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCAmelCase__))
# verify size
A__ = torch.tensor([800, 1_066])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCAmelCase__))
@slow
def SCREAMING_SNAKE_CASE ( self : Dict) ->Optional[int]:
'''simple docstring'''
A__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''') as f:
A__ = json.loads(f.read())
A__ = {'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}
A__ = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')
# encode them
A__ = DeformableDetrImageProcessor(format='''coco_panoptic''')
A__ = image_processing(images=UpperCAmelCase__ , annotations=UpperCAmelCase__ , masks_path=UpperCAmelCase__ , return_tensors='''pt''')
# verify pixel values
A__ = torch.Size([1, 3, 800, 1_066])
self.assertEqual(encoding['''pixel_values'''].shape , UpperCAmelCase__)
A__ = torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCAmelCase__ , atol=1e-4))
# verify area
A__ = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCAmelCase__))
# verify boxes
A__ = torch.Size([6, 4])
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCAmelCase__)
A__ = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCAmelCase__ , atol=1e-3))
# verify image_id
A__ = torch.tensor([39_769])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCAmelCase__))
# verify is_crowd
A__ = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCAmelCase__))
# verify class_labels
A__ = torch.tensor([17, 17, 63, 75, 75, 93])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCAmelCase__))
# verify masks
A__ = 822_873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , UpperCAmelCase__)
# verify orig_size
A__ = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCAmelCase__))
# verify size
A__ = torch.tensor([800, 1_066])
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCAmelCase__))
| 14 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
lowercase__ :Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowercase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,):
super().__init__()
self.register_modules(
vae=A__ ,text_encoder=A__ ,tokenizer=A__ ,unet=A__ ,scheduler=A__ ,safety_checker=A__ ,feature_extractor=A__ ,)
def A__ ( self ,A__ = "auto"):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowercase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A__)
def A__ ( self):
self.enable_attention_slicing(A__)
@torch.no_grad()
def __call__( self ,A__ ,A__ = 5_1_2 ,A__ = 5_1_2 ,A__ = 5_0 ,A__ = 7.5 ,A__ = None ,A__ = 1 ,A__ = 0.0 ,A__ = None ,A__ = None ,A__ = "pil" ,A__ = True ,A__ = None ,A__ = 1 ,A__ = None ,**A__ ,):
if isinstance(A__ ,A__):
lowercase = 1
elif isinstance(A__ ,A__):
lowercase = len(A__)
else:
raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(A__)}')
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(A__ ,A__) or callback_steps <= 0)
):
raise ValueError(
f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
f' {type(A__)}.')
# get prompt text embeddings
lowercase = self.tokenizer(
A__ ,padding='''max_length''' ,max_length=self.tokenizer.model_max_length ,return_tensors='''pt''' ,)
lowercase = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
lowercase = 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}')
lowercase = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
lowercase = self.text_encoder(text_input_ids.to(self.device))[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
lowercase , lowercase , lowercase = text_embeddings.shape
lowercase = text_embeddings.repeat(1 ,A__ ,1)
lowercase = text_embeddings.view(bs_embed * num_images_per_prompt ,A__ ,-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.
lowercase = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowercase = 42
if negative_prompt is None:
lowercase = ['''''']
elif type(A__) is not type(A__):
raise TypeError(
f'`negative_prompt` should be the same type to `prompt`, but got {type(A__)} !='
f' {type(A__)}.')
elif isinstance(A__ ,A__):
lowercase = [negative_prompt]
elif batch_size != len(A__):
raise ValueError(
f'`negative_prompt`: {negative_prompt} has batch size {len(A__)}, but `prompt`:'
f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
''' the batch size of `prompt`.''')
else:
lowercase = negative_prompt
lowercase = text_input_ids.shape[-1]
lowercase = self.tokenizer(
A__ ,padding='''max_length''' ,max_length=A__ ,truncation=A__ ,return_tensors='''pt''' ,)
lowercase = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowercase = uncond_embeddings.shape[1]
lowercase = uncond_embeddings.repeat(A__ ,A__ ,1)
lowercase = uncond_embeddings.view(batch_size * num_images_per_prompt ,A__ ,-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
lowercase = 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`.
lowercase = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
lowercase = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
lowercase = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
lowercase = torch.randn(
A__ ,generator=A__ ,device='''cpu''' ,dtype=A__).to(self.device)
lowercase = torch.randn(A__ ,generator=A__ ,device='''cpu''' ,dtype=A__).to(
self.device)
else:
lowercase = torch.randn(
A__ ,generator=A__ ,device=self.device ,dtype=A__)
lowercase = torch.randn(A__ ,generator=A__ ,device=self.device ,dtype=A__)
else:
if latents_reference.shape != latents_shape:
raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
lowercase = latents_reference.to(self.device)
lowercase = latents.to(self.device)
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
lowercase = (latents_shape[3] - latents_shape_reference[3]) // 2
lowercase = (latents_shape[2] - latents_shape_reference[2]) // 2
lowercase = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
lowercase = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
lowercase = 0 if dx < 0 else dx
lowercase = 0 if dy < 0 else dy
lowercase = max(-dx ,0)
lowercase = max(-dy ,0)
# import pdb
# pdb.set_trace()
lowercase = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(A__)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
lowercase = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
lowercase = 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]
lowercase = '''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
lowercase = {}
if accepts_eta:
lowercase = eta
for i, t in enumerate(self.progress_bar(A__)):
# expand the latents if we are doing classifier free guidance
lowercase = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
lowercase = self.scheduler.scale_model_input(A__ ,A__)
# predict the noise residual
lowercase = self.unet(A__ ,A__ ,encoder_hidden_states=A__).sample
# perform guidance
if do_classifier_free_guidance:
lowercase , lowercase = noise_pred.chunk(2)
lowercase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
lowercase = self.scheduler.step(A__ ,A__ ,A__ ,**A__).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(A__ ,A__ ,A__)
lowercase = 1 / 0.18215 * latents
lowercase = self.vae.decode(A__).sample
lowercase = (image / 2 + 0.5).clamp(0 ,1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowercase = image.cpu().permute(0 ,2 ,3 ,1).float().numpy()
if self.safety_checker is not None:
lowercase = self.feature_extractor(self.numpy_to_pil(A__) ,return_tensors='''pt''').to(
self.device)
lowercase , lowercase = self.safety_checker(
images=A__ ,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype))
else:
lowercase = None
if output_type == "pil":
lowercase = self.numpy_to_pil(A__)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=A__ ,nsfw_content_detected=A__)
| 101 | 0 |
from __future__ import annotations
import time
_lowerCamelCase = list[tuple[int, int]]
_lowerCamelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_lowerCamelCase = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class a :
'''simple docstring'''
def __init__( self : Optional[int] , __snake_case : int , __snake_case : int , __snake_case : int , __snake_case : int , __snake_case : Node | None ):
UpperCAmelCase_ = pos_x
UpperCAmelCase_ = pos_y
UpperCAmelCase_ = (pos_y, pos_x)
UpperCAmelCase_ = goal_x
UpperCAmelCase_ = goal_y
UpperCAmelCase_ = parent
class a :
'''simple docstring'''
def __init__( self : List[Any] , __snake_case : tuple[int, int] , __snake_case : tuple[int, int] ):
UpperCAmelCase_ = Node(start[1] , start[0] , goal[1] , goal[0] , __snake_case )
UpperCAmelCase_ = Node(goal[1] , goal[0] , goal[1] , goal[0] , __snake_case )
UpperCAmelCase_ = [self.start]
UpperCAmelCase_ = False
def lowerCamelCase_ ( self : int ):
while self.node_queue:
UpperCAmelCase_ = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
UpperCAmelCase_ = True
return self.retrace_path(__snake_case )
UpperCAmelCase_ = self.get_successors(__snake_case )
for node in successors:
self.node_queue.append(__snake_case )
if not self.reached:
return [self.start.pos]
return None
def lowerCamelCase_ ( self : Dict , __snake_case : Node ):
UpperCAmelCase_ = []
for action in delta:
UpperCAmelCase_ = parent.pos_x + action[1]
UpperCAmelCase_ = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__snake_case ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(__snake_case , __snake_case , self.target.pos_y , self.target.pos_x , __snake_case ) )
return successors
def lowerCamelCase_ ( self : Union[str, Any] , __snake_case : Node | None ):
UpperCAmelCase_ = node
UpperCAmelCase_ = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
UpperCAmelCase_ = current_node.parent
path.reverse()
return path
class a :
'''simple docstring'''
def __init__( self : Union[str, Any] , __snake_case : Optional[Any] , __snake_case : Any ):
UpperCAmelCase_ = BreadthFirstSearch(__snake_case , __snake_case )
UpperCAmelCase_ = BreadthFirstSearch(__snake_case , __snake_case )
UpperCAmelCase_ = False
def lowerCamelCase_ ( self : str ):
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
UpperCAmelCase_ = self.fwd_bfs.node_queue.pop(0 )
UpperCAmelCase_ = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
UpperCAmelCase_ = True
return self.retrace_bidirectional_path(
__snake_case , __snake_case )
UpperCAmelCase_ = current_bwd_node
UpperCAmelCase_ = current_fwd_node
UpperCAmelCase_ = {
self.fwd_bfs: self.fwd_bfs.get_successors(__snake_case ),
self.bwd_bfs: self.bwd_bfs.get_successors(__snake_case ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(__snake_case )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def lowerCamelCase_ ( self : Optional[Any] , __snake_case : Node , __snake_case : Node ):
UpperCAmelCase_ = self.fwd_bfs.retrace_path(__snake_case )
UpperCAmelCase_ = self.bwd_bfs.retrace_path(__snake_case )
bwd_path.pop()
bwd_path.reverse()
UpperCAmelCase_ = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
_lowerCamelCase = (0, 0)
_lowerCamelCase = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
_lowerCamelCase = time.time()
_lowerCamelCase = BreadthFirstSearch(init, goal)
_lowerCamelCase = bfs.search()
_lowerCamelCase = time.time() - start_bfs_time
print('Unidirectional BFS computation time : ', bfs_time)
_lowerCamelCase = time.time()
_lowerCamelCase = BidirectionalBreadthFirstSearch(init, goal)
_lowerCamelCase = bd_bfs.search()
_lowerCamelCase = time.time() - start_bd_bfs_time
print('Bidirectional BFS computation time : ', bd_bfs_time)
| 177 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=_A )
class a ( _A ):
'''simple docstring'''
lowerCAmelCase : str = field(default='audio-classification' , metadata={'include_in_asdict_even_if_is_default': True} )
lowerCAmelCase : ClassVar[Features] = Features({'audio': Audio()} )
lowerCAmelCase : ClassVar[Features] = Features({'labels': ClassLabel} )
lowerCAmelCase : str = "audio"
lowerCAmelCase : str = "labels"
def lowerCamelCase_ ( self : Optional[Any] , __snake_case : List[Any] ):
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] , __snake_case ):
raise ValueError(F'Column {self.label_column} is not a ClassLabel.' )
UpperCAmelCase_ = copy.deepcopy(self )
UpperCAmelCase_ = self.label_schema.copy()
UpperCAmelCase_ = features[self.label_column]
UpperCAmelCase_ = label_schema
return task_template
@property
def lowerCamelCase_ ( self : Tuple ):
return {
self.audio_column: "audio",
self.label_column: "labels",
}
| 177 | 1 |
from __future__ import annotations
lowerCAmelCase : Optional[int] = tuple[int, int, int]
lowerCAmelCase : str = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
lowerCAmelCase : Tuple = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
# -------------------------- default selection --------------------------
# rotors --------------------------
lowerCAmelCase : Tuple = '''EGZWVONAHDCLFQMSIPJBYUKXTR'''
lowerCAmelCase : Optional[Any] = '''FOBHMDKEXQNRAULPGSJVTYICZW'''
lowerCAmelCase : Tuple = '''ZJXESIUQLHAVRMDOYGTNFWPBKC'''
# reflector --------------------------
lowerCAmelCase : List[str] = {
'''A''': '''N''',
'''N''': '''A''',
'''B''': '''O''',
'''O''': '''B''',
'''C''': '''P''',
'''P''': '''C''',
'''D''': '''Q''',
'''Q''': '''D''',
'''E''': '''R''',
'''R''': '''E''',
'''F''': '''S''',
'''S''': '''F''',
'''G''': '''T''',
'''T''': '''G''',
'''H''': '''U''',
'''U''': '''H''',
'''I''': '''V''',
'''V''': '''I''',
'''J''': '''W''',
'''W''': '''J''',
'''K''': '''X''',
'''X''': '''K''',
'''L''': '''Y''',
'''Y''': '''L''',
'''M''': '''Z''',
'''Z''': '''M''',
}
# -------------------------- extra rotors --------------------------
lowerCAmelCase : Dict = '''RMDJXFUWGISLHVTCQNKYPBEZOA'''
lowerCAmelCase : Optional[Any] = '''SGLCPQWZHKXAREONTFBVIYJUDM'''
lowerCAmelCase : Any = '''HVSICLTYKQUBXDWAJZOMFGPREN'''
lowerCAmelCase : Union[str, Any] = '''RZWQHFMVDBKICJLNTUXAGYPSOE'''
lowerCAmelCase : Dict = '''LFKIJODBEGAMQPXVUHYSTCZRWN'''
lowerCAmelCase : Optional[int] = '''KOAEGVDHXPQZMLFTYWJNBRCIUS'''
def A_ ( a , a , a ):
"""simple docstring"""
if (unique_rotsel := len(set(snake_case__ ) )) < 3:
SCREAMING_SNAKE_CASE_ : Tuple = f"Please use 3 unique rotors (not {unique_rotsel})"
raise Exception(snake_case__ )
# Checks if rotor positions are valid
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = rotpos
if not 0 < rotorposa <= len(snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = f"First rotor position is not within range of 1..26 ({rotorposa}"
raise ValueError(snake_case__ )
if not 0 < rotorposa <= len(snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = f"Second rotor position is not within range of 1..26 ({rotorposa})"
raise ValueError(snake_case__ )
if not 0 < rotorposa <= len(snake_case__ ):
SCREAMING_SNAKE_CASE_ : List[str] = f"Third rotor position is not within range of 1..26 ({rotorposa})"
raise ValueError(snake_case__ )
# Validates string and returns dict
SCREAMING_SNAKE_CASE_ : List[str] = _plugboard(snake_case__ )
return rotpos, rotsel, pbdict
def A_ ( a ):
"""simple docstring"""
if not isinstance(snake_case__ , snake_case__ ):
SCREAMING_SNAKE_CASE_ : Tuple = f"Plugboard setting isn't type string ({type(snake_case__ )})"
raise TypeError(snake_case__ )
elif len(snake_case__ ) % 2 != 0:
SCREAMING_SNAKE_CASE_ : List[Any] = f"Odd number of symbols ({len(snake_case__ )})"
raise Exception(snake_case__ )
elif pbstring == "":
return {}
pbstring.replace(' ' , '' )
# Checks if all characters are unique
SCREAMING_SNAKE_CASE_ : Union[str, Any] = set()
for i in pbstring:
if i not in abc:
SCREAMING_SNAKE_CASE_ : Optional[int] = f"'{i}' not in list of symbols"
raise Exception(snake_case__ )
elif i in tmppbl:
SCREAMING_SNAKE_CASE_ : Optional[int] = f"Duplicate symbol ({i})"
raise Exception(snake_case__ )
else:
tmppbl.add(snake_case__ )
del tmppbl
# Created the dictionary
SCREAMING_SNAKE_CASE_ : str = {}
for j in range(0 , len(snake_case__ ) - 1 , 2 ):
SCREAMING_SNAKE_CASE_ : str = pbstring[j + 1]
SCREAMING_SNAKE_CASE_ : Optional[int] = pbstring[j]
return pb
def A_ ( a , a , a = (rotora, rotora, rotora) , a = "" , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = text.upper()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[str] = _validator(
snake_case__ , snake_case__ , plugb.upper() )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = rotor_position
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = rotor_selection
rotorposa -= 1
rotorposa -= 1
rotorposa -= 1
SCREAMING_SNAKE_CASE_ : Any = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = plugboard[symbol]
# rotor ra --------------------------
SCREAMING_SNAKE_CASE_ : Dict = abc.index(snake_case__ ) + rotorposa
SCREAMING_SNAKE_CASE_ : Optional[int] = rotora[index % len(snake_case__ )]
# rotor rb --------------------------
SCREAMING_SNAKE_CASE_ : List[str] = abc.index(snake_case__ ) + rotorposa
SCREAMING_SNAKE_CASE_ : List[Any] = rotora[index % len(snake_case__ )]
# rotor rc --------------------------
SCREAMING_SNAKE_CASE_ : List[str] = abc.index(snake_case__ ) + rotorposa
SCREAMING_SNAKE_CASE_ : List[str] = rotora[index % len(snake_case__ )]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
SCREAMING_SNAKE_CASE_ : Optional[Any] = reflector[symbol]
# 2nd rotors
SCREAMING_SNAKE_CASE_ : List[Any] = abc[rotora.index(snake_case__ ) - rotorposa]
SCREAMING_SNAKE_CASE_ : Any = abc[rotora.index(snake_case__ ) - rotorposa]
SCREAMING_SNAKE_CASE_ : Optional[int] = abc[rotora.index(snake_case__ ) - rotorposa]
# 2nd plugboard
if symbol in plugboard:
SCREAMING_SNAKE_CASE_ : Tuple = plugboard[symbol]
# moves/resets rotor positions
rotorposa += 1
if rotorposa >= len(snake_case__ ):
SCREAMING_SNAKE_CASE_ : Tuple = 0
rotorposa += 1
if rotorposa >= len(snake_case__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 0
rotorposa += 1
if rotorposa >= len(snake_case__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(snake_case__ )
return "".join(snake_case__ )
if __name__ == "__main__":
lowerCAmelCase : Any = '''This is my Python script that emulates the Enigma machine from WWII.'''
lowerCAmelCase : str = (1, 1, 1)
lowerCAmelCase : Any = '''pictures'''
lowerCAmelCase : Tuple = (rotora, rotora, rotora)
lowerCAmelCase : List[Any] = enigma(message, rotor_pos, rotor_sel, pb)
print('Encrypted message:', en)
print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))
| 253 | import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name='''my_dataset''' )} ),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1_3_3_7 , num_examples=4_2 )} ),
SplitDict({'''train''': SplitInfo()} ),
] , )
def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> Union[str, Any]:
lowerCAmelCase = split_dict._to_yaml_list()
assert len(snake_case__ ) == len(snake_case__ )
lowerCAmelCase = SplitDict._from_yaml_list(snake_case__ )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
lowerCAmelCase = None
# the split name of split_dict takes over the name of the split info object
lowerCAmelCase = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
'''split_info''' , [SplitInfo(), SplitInfo(dataset_name=snake_case__ ), SplitInfo(dataset_name='''my_dataset''' )] )
def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> Optional[int]:
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
lowerCAmelCase = asdict(SplitDict({'''train''': split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 338 | 0 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
SCREAMING_SNAKE_CASE_:str = subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
SCREAMING_SNAKE_CASE_:Tuple = (
subprocess.check_output(F"""git diff --diff-filter=d --name-only {fork_point_sha}""".split()).decode("""utf-8""").split()
)
SCREAMING_SNAKE_CASE_:Optional[int] = """|""".join(sys.argv[1:])
SCREAMING_SNAKE_CASE_:Union[str, Any] = re.compile(RF"""^({joined_dirs}).*?\.py$""")
SCREAMING_SNAKE_CASE_:Optional[int] = [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")
| 115 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE_:Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:Union[str, Any] = {
"""microsoft/conditional-detr-resnet-50""": (
"""https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
),
}
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : Dict = "conditional_detr"
__lowerCamelCase : str = ["past_key_values"]
__lowerCamelCase : str = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self, lowerCamelCase__=True, lowerCamelCase__=None, lowerCamelCase__=3, lowerCamelCase__=300, lowerCamelCase__=6, lowerCamelCase__=2048, lowerCamelCase__=8, lowerCamelCase__=6, lowerCamelCase__=2048, lowerCamelCase__=8, lowerCamelCase__=0.0, lowerCamelCase__=0.0, lowerCamelCase__=True, lowerCamelCase__="relu", lowerCamelCase__=256, lowerCamelCase__=0.1, lowerCamelCase__=0.0, lowerCamelCase__=0.0, lowerCamelCase__=0.02, lowerCamelCase__=1.0, lowerCamelCase__=False, lowerCamelCase__="sine", lowerCamelCase__="resnet50", lowerCamelCase__=True, lowerCamelCase__=False, lowerCamelCase__=2, lowerCamelCase__=5, lowerCamelCase__=2, lowerCamelCase__=1, lowerCamelCase__=1, lowerCamelCase__=2, lowerCamelCase__=5, lowerCamelCase__=2, lowerCamelCase__=0.25, **lowerCamelCase__, ):
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
A : List[Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(lowerCamelCase__, lowerCamelCase__ ):
A : Any = backbone_config.get("""model_type""" )
A : Optional[Any] = CONFIG_MAPPING[backbone_model_type]
A : Tuple = config_class.from_dict(lowerCamelCase__ )
A : Dict = use_timm_backbone
A : int = backbone_config
A : Union[str, Any] = num_channels
A : Optional[Any] = num_queries
A : Union[str, Any] = d_model
A : str = encoder_ffn_dim
A : List[Any] = encoder_layers
A : Tuple = encoder_attention_heads
A : Union[str, Any] = decoder_ffn_dim
A : Tuple = decoder_layers
A : int = decoder_attention_heads
A : Union[str, Any] = dropout
A : List[str] = attention_dropout
A : Optional[int] = activation_dropout
A : Optional[Any] = activation_function
A : Any = init_std
A : List[Any] = init_xavier_std
A : Any = encoder_layerdrop
A : List[str] = decoder_layerdrop
A : int = encoder_layers
A : Union[str, Any] = auxiliary_loss
A : Union[str, Any] = position_embedding_type
A : Tuple = backbone
A : Dict = use_pretrained_backbone
A : int = dilation
# Hungarian matcher
A : List[Any] = class_cost
A : List[Any] = bbox_cost
A : int = giou_cost
# Loss coefficients
A : List[Any] = mask_loss_coefficient
A : Any = dice_loss_coefficient
A : int = cls_loss_coefficient
A : Tuple = bbox_loss_coefficient
A : List[Any] = giou_loss_coefficient
A : int = focal_alpha
super().__init__(is_encoder_decoder=lowerCamelCase__, **lowerCamelCase__ )
@property
def _lowerCAmelCase ( self ):
return self.encoder_attention_heads
@property
def _lowerCAmelCase ( self ):
return self.d_model
def _lowerCAmelCase ( self ):
A : Dict = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
A : List[Any] = self.backbone_config.to_dict()
A : List[str] = self.__class__.model_type
return output
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : Tuple = version.parse("1.11" )
@property
def _lowerCAmelCase ( self ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowerCAmelCase ( self ):
return 1e-5
@property
def _lowerCAmelCase ( self ):
return 12
| 115 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case :int = logging.get_logger(__name__)
__snake_case :List[Any] = {
'''facebook/deit-base-distilled-patch16-224''': (
'''https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json'''
),
# See all DeiT models at https://huggingface.co/models?filter=deit
}
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : Optional[int] = '''deit'''
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Any=768 , __SCREAMING_SNAKE_CASE : str=12 , __SCREAMING_SNAKE_CASE : Any=12 , __SCREAMING_SNAKE_CASE : int=3_072 , __SCREAMING_SNAKE_CASE : Any="gelu" , __SCREAMING_SNAKE_CASE : List[str]=0.0 , __SCREAMING_SNAKE_CASE : Optional[int]=0.0 , __SCREAMING_SNAKE_CASE : Optional[int]=0.02 , __SCREAMING_SNAKE_CASE : int=1E-12 , __SCREAMING_SNAKE_CASE : Optional[int]=224 , __SCREAMING_SNAKE_CASE : Optional[int]=16 , __SCREAMING_SNAKE_CASE : Optional[Any]=3 , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : Any=16 , **__SCREAMING_SNAKE_CASE : List[Any] , ):
'''simple docstring'''
super().__init__(**__SCREAMING_SNAKE_CASE)
__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 = initializer_range
__a = layer_norm_eps
__a = image_size
__a = patch_size
__a = num_channels
__a = qkv_bias
__a = encoder_stride
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : Optional[int] = version.parse('''1.11''' )
@property
def _lowerCamelCase ( self : str):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
])
@property
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
return 1E-4
| 49 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW models at https://huggingface.co/models?filter=sew
}
class _lowerCAmelCase ( __a ):
_lowercase ='''sew'''
def __init__( self , _UpperCamelCase=32 , _UpperCamelCase=768 , _UpperCamelCase=12 , _UpperCamelCase=12 , _UpperCamelCase=3_072 , _UpperCamelCase=2 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=0.0 , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=0.02 , _UpperCamelCase=1e-5 , _UpperCamelCase="group" , _UpperCamelCase="gelu" , _UpperCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , _UpperCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , _UpperCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , _UpperCamelCase=False , _UpperCamelCase=128 , _UpperCamelCase=16 , _UpperCamelCase=True , _UpperCamelCase=0.05 , _UpperCamelCase=10 , _UpperCamelCase=2 , _UpperCamelCase=0.0 , _UpperCamelCase=10 , _UpperCamelCase=0 , _UpperCamelCase="mean" , _UpperCamelCase=False , _UpperCamelCase=False , _UpperCamelCase=256 , _UpperCamelCase=0 , _UpperCamelCase=1 , _UpperCamelCase=2 , **_UpperCamelCase , ) -> Union[str, Any]:
super().__init__(**_UpperCamelCase , pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase )
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = feat_extract_norm
lowerCAmelCase_ = feat_extract_activation
lowerCAmelCase_ = list(_UpperCamelCase )
lowerCAmelCase_ = list(_UpperCamelCase )
lowerCAmelCase_ = list(_UpperCamelCase )
lowerCAmelCase_ = conv_bias
lowerCAmelCase_ = num_conv_pos_embeddings
lowerCAmelCase_ = num_conv_pos_embedding_groups
lowerCAmelCase_ = len(self.conv_dim )
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = squeeze_factor
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = hidden_dropout
lowerCAmelCase_ = attention_dropout
lowerCAmelCase_ = activation_dropout
lowerCAmelCase_ = feat_proj_dropout
lowerCAmelCase_ = final_dropout
lowerCAmelCase_ = layerdrop
lowerCAmelCase_ = layer_norm_eps
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect."
"It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCAmelCase_ = apply_spec_augment
lowerCAmelCase_ = mask_time_prob
lowerCAmelCase_ = mask_time_length
lowerCAmelCase_ = mask_time_min_masks
lowerCAmelCase_ = mask_feature_prob
lowerCAmelCase_ = mask_feature_length
lowerCAmelCase_ = mask_feature_min_masks
# ctc loss
lowerCAmelCase_ = ctc_loss_reduction
lowerCAmelCase_ = ctc_zero_infinity
# sequence classification
lowerCAmelCase_ = use_weighted_layer_sum
lowerCAmelCase_ = classifier_proj_size
@property
def __a ( self ) -> Optional[Any]:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 231 | 0 |
import os
import time
import numpy as np
import onnxruntime as ort
snake_case = """1"""
snake_case = """0"""
snake_case = """1"""
snake_case = ort.SessionOptions()
snake_case = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("""Create inference session...""")
snake_case = ["""TensorrtExecutionProvider""", """CUDAExecutionProvider"""]
snake_case = ort.InferenceSession("""model.onnx""", sess_options=sess_opt, providers=execution_provider)
snake_case = ort.RunOptions()
snake_case = 128
snake_case = 1
snake_case = np.ones((batch, sequence), dtype=np.intaa)
snake_case = np.ones((batch, sequence), dtype=np.intaa)
snake_case = np.ones((batch, sequence), dtype=np.intaa)
print("""Warm up phase...""")
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Start inference...""")
snake_case = time.time()
snake_case = 2_000
snake_case = {}
for iter in range(max_iters):
snake_case = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Average Inference Time = {:.3f} ms""".format((time.time() - start_time) * 1_000 / max_iters))
| 371 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""SpeechEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""FlaxSpeechEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319 | 0 |
'''simple docstring'''
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class __UpperCamelCase :
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
lowerCamelCase_ =AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
lowerCamelCase_ =UNetaDConditionModel(
sample_size=32, layers_per_block=1, block_out_channels=[32, 64], down_block_types=[
'''ResnetDownsampleBlock2D''',
'''SimpleCrossAttnDownBlock2D''',
], mid_block_type='''UNetMidBlock2DSimpleCrossAttn''', up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''], in_channels=3, out_channels=6, cross_attention_dim=32, encoder_hid_dim=32, attention_head_dim=8, addition_embed_type='''text''', addition_embed_type_num_heads=2, cross_attention_norm='''group_norm''', resnet_time_scale_shift='''scale_shift''', act_fn='''gelu''', )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowerCamelCase_ =DDPMScheduler(
num_train_timesteps=1_000, beta_schedule='''squaredcos_cap_v2''', beta_start=0.0_0_0_1, beta_end=0.0_2, thresholding=lowerCAmelCase, dynamic_thresholding_ratio=0.9_5, sample_max_value=1.0, prediction_type='''epsilon''', variance_type='''learned_range''', )
torch.manual_seed(0 )
lowerCamelCase_ =IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
lowerCamelCase_ =AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
lowerCamelCase_ =UNetaDConditionModel(
sample_size=32, layers_per_block=[1, 2], block_out_channels=[32, 64], down_block_types=[
'''ResnetDownsampleBlock2D''',
'''SimpleCrossAttnDownBlock2D''',
], mid_block_type='''UNetMidBlock2DSimpleCrossAttn''', up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''], in_channels=6, out_channels=6, cross_attention_dim=32, encoder_hid_dim=32, attention_head_dim=8, addition_embed_type='''text''', addition_embed_type_num_heads=2, cross_attention_norm='''group_norm''', resnet_time_scale_shift='''scale_shift''', act_fn='''gelu''', class_embed_type='''timestep''', mid_block_scale_factor=1.4_1_4, time_embedding_act_fn='''gelu''', time_embedding_dim=32, )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowerCamelCase_ =DDPMScheduler(
num_train_timesteps=1_000, beta_schedule='''squaredcos_cap_v2''', beta_start=0.0_0_0_1, beta_end=0.0_2, thresholding=lowerCAmelCase, dynamic_thresholding_ratio=0.9_5, sample_max_value=1.0, prediction_type='''epsilon''', variance_type='''learned_range''', )
torch.manual_seed(0 )
lowerCamelCase_ =DDPMScheduler(
num_train_timesteps=1_000, beta_schedule='''squaredcos_cap_v2''', beta_start=0.0_0_0_1, beta_end=0.0_2, )
torch.manual_seed(0 )
lowerCamelCase_ =IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =inputs['''prompt''']
lowerCamelCase_ =inputs['''generator''']
lowerCamelCase_ =inputs['''num_inference_steps''']
lowerCamelCase_ =inputs['''output_type''']
if "image" in inputs:
lowerCamelCase_ =inputs['''image''']
else:
lowerCamelCase_ =None
if "mask_image" in inputs:
lowerCamelCase_ =inputs['''mask_image''']
else:
lowerCamelCase_ =None
if "original_image" in inputs:
lowerCamelCase_ =inputs['''original_image''']
else:
lowerCamelCase_ =None
lowerCamelCase_, lowerCamelCase_ =pipe.encode_prompt(lowerCAmelCase )
# inputs with prompt converted to embeddings
lowerCamelCase_ ={
'''prompt_embeds''': prompt_embeds,
'''negative_prompt_embeds''': negative_prompt_embeds,
'''generator''': generator,
'''num_inference_steps''': num_inference_steps,
'''output_type''': output_type,
}
if image is not None:
lowerCamelCase_ =image
if mask_image is not None:
lowerCamelCase_ =mask_image
if original_image is not None:
lowerCamelCase_ =original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =pipe(**lowerCAmelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =self.pipeline_class.from_pretrained(lowerCAmelCase )
pipe_loaded.to(lowerCAmelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowerCAmelCase, lowerCAmelCase ) is None, f'''`{optional_component}` did not stay set to None after loading.''', )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =inputs['''generator''']
lowerCamelCase_ =inputs['''num_inference_steps''']
lowerCamelCase_ =inputs['''output_type''']
# inputs with prompt converted to embeddings
lowerCamelCase_ ={
'''prompt_embeds''': prompt_embeds,
'''negative_prompt_embeds''': negative_prompt_embeds,
'''generator''': generator,
'''num_inference_steps''': num_inference_steps,
'''output_type''': output_type,
}
if image is not None:
lowerCamelCase_ =image
if mask_image is not None:
lowerCamelCase_ =mask_image
if original_image is not None:
lowerCamelCase_ =original_image
lowerCamelCase_ =pipe_loaded(**lowerCAmelCase )[0]
lowerCamelCase_ =np.abs(to_np(lowerCAmelCase ) - to_np(lowerCAmelCase ) ).max()
self.assertLess(lowerCAmelCase, 1e-4 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =pipe(**lowerCAmelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =self.pipeline_class.from_pretrained(lowerCAmelCase )
pipe_loaded.to(lowerCAmelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =pipe_loaded(**lowerCAmelCase )[0]
lowerCamelCase_ =np.abs(to_np(lowerCAmelCase ) - to_np(lowerCAmelCase ) ).max()
self.assertLess(lowerCAmelCase, 1e-4 )
| 75 |
'''simple docstring'''
from __future__ import annotations
def a_ ( __snake_case : str , __snake_case : list[str] | None = None , __snake_case : dict[str, float] | None = None , __snake_case : bool = False , ) -> tuple[int, float, str]:
"""simple docstring"""
lowerCamelCase_ =cipher_alphabet or [chr(__snake_case ) for i in range(97 , 123 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
lowerCamelCase_ ={
'''a''': 0.0_8_4_9_7,
'''b''': 0.0_1_4_9_2,
'''c''': 0.0_2_2_0_2,
'''d''': 0.0_4_2_5_3,
'''e''': 0.1_1_1_6_2,
'''f''': 0.0_2_2_2_8,
'''g''': 0.0_2_0_1_5,
'''h''': 0.0_6_0_9_4,
'''i''': 0.0_7_5_4_6,
'''j''': 0.0_0_1_5_3,
'''k''': 0.0_1_2_9_2,
'''l''': 0.0_4_0_2_5,
'''m''': 0.0_2_4_0_6,
'''n''': 0.0_6_7_4_9,
'''o''': 0.0_7_5_0_7,
'''p''': 0.0_1_9_2_9,
'''q''': 0.0_0_0_9_5,
'''r''': 0.0_7_5_8_7,
'''s''': 0.0_6_3_2_7,
'''t''': 0.0_9_3_5_6,
'''u''': 0.0_2_7_5_8,
'''v''': 0.0_0_9_7_8,
'''w''': 0.0_2_5_6_0,
'''x''': 0.0_0_1_5_0,
'''y''': 0.0_1_9_9_4,
'''z''': 0.0_0_0_7_7,
}
else:
# Custom frequencies dictionary
lowerCamelCase_ =frequencies_dict
if not case_sensitive:
lowerCamelCase_ =ciphertext.lower()
# Chi squared statistic values
lowerCamelCase_ ={}
# cycle through all of the shifts
for shift in range(len(__snake_case ) ):
lowerCamelCase_ =''''''
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
lowerCamelCase_ =(alphabet_letters.index(letter.lower() ) - shift) % len(
__snake_case )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
lowerCamelCase_ =0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
lowerCamelCase_ =letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
lowerCamelCase_ =decrypted_with_shift.lower().count(__snake_case )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
lowerCamelCase_ =frequencies[letter] * occurrences
# Complete the chi squared statistic formula
lowerCamelCase_ =((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
lowerCamelCase_ =decrypted_with_shift.count(__snake_case )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
lowerCamelCase_ =frequencies[letter] * occurrences
# Complete the chi squared statistic formula
lowerCamelCase_ =((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
lowerCamelCase_ =(
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(__snake_case : int ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
lowerCamelCase_ =min(
__snake_case , key=__snake_case , )
# Get all the data from the most likely cipher (key, decoded message)
(
(
lowerCamelCase_
), (
lowerCamelCase_
),
) =chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
)
| 75 | 1 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : Optional[Any] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : Dict = 0
while b > 0:
if b & 1:
A_ : Any = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res
| 357 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , ):
if config_name_or_path is None:
A_ : Optional[Any] = '''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''
if generator_tokenizer_name_or_path is None:
A_ : Union[str, Any] = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
A_ : List[str] = question_encoder_name_or_path
A_ : int = RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration
# Save model.
A_ : Optional[Any] = RagConfig.from_pretrained(SCREAMING_SNAKE_CASE )
A_ : int = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )
A_ : Any = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )
A_ : str = gen_config
A_ : Tuple = question_encoder_config
A_ : List[Any] = model_class.from_pretrained_question_encoder_generator(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , config=SCREAMING_SNAKE_CASE )
rag_model.save_pretrained(SCREAMING_SNAKE_CASE )
# Sanity check.
model_class.from_pretrained(SCREAMING_SNAKE_CASE )
# Save tokenizers.
A_ : Tuple = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/''' )
A_ : str = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/''' )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""",
choices=["""rag_sequence""", """rag_token"""],
required=True,
type=str,
help="""RAG model type: rag_sequence, rag_token""",
)
parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
parser.add_argument(
"""--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
)
parser.add_argument(
"""--generator_tokenizer_name_or_path""",
type=str,
help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
)
parser.add_argument(
"""--question_encoder_tokenizer_name_or_path""",
type=str,
help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
)
parser.add_argument(
"""--config_name_or_path""",
type=str,
help=(
"""Identifier of the model config to use, if not provided, resolves to a base config for a given"""
""" ``model_type``"""
),
)
UpperCamelCase = parser.parse_args()
UpperCamelCase = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 65 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase : int = logging.get_logger(__name__)
lowerCamelCase : Union[str, Any] = {
"microsoft/swin-tiny-patch4-window7-224": (
"https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class A( UpperCamelCase , UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = '''swin'''
UpperCamelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Union[str, Any] , A_ : Optional[int]=224 , A_ : Any=4 , A_ : Union[str, Any]=3 , A_ : str=96 , A_ : Optional[int]=[2, 2, 6, 2] , A_ : Optional[int]=[3, 6, 12, 24] , A_ : int=7 , A_ : Any=4.0 , A_ : Dict=True , A_ : Optional[int]=0.0 , A_ : Optional[int]=0.0 , A_ : int=0.1 , A_ : str="gelu" , A_ : Union[str, Any]=False , A_ : int=0.02 , A_ : List[Any]=1E-5 , A_ : List[str]=32 , A_ : List[str]=None , A_ : int=None , **A_ : int , ) -> Dict:
"""simple docstring"""
super().__init__(**A_ )
lowerCamelCase_ = image_size
lowerCamelCase_ = patch_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = embed_dim
lowerCamelCase_ = depths
lowerCamelCase_ = len(A_ )
lowerCamelCase_ = num_heads
lowerCamelCase_ = window_size
lowerCamelCase_ = mlp_ratio
lowerCamelCase_ = qkv_bias
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = drop_path_rate
lowerCamelCase_ = hidden_act
lowerCamelCase_ = use_absolute_embeddings
lowerCamelCase_ = layer_norm_eps
lowerCamelCase_ = initializer_range
lowerCamelCase_ = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCamelCase_ = int(embed_dim * 2 ** (len(A_ ) - 1) )
lowerCamelCase_ = ['stem'] + [f"""stage{idx}""" for idx in range(1 , len(A_ ) + 1 )]
lowerCamelCase_ , lowerCamelCase_ = get_aligned_output_features_output_indices(
out_features=A_ , out_indices=A_ , stage_names=self.stage_names )
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = version.parse('''1.11''' )
@property
def a__ ( self : Any ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def a__ ( self : List[Any] ) -> float:
"""simple docstring"""
return 1E-4
| 204 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCamelCase : Union[str, Any] = "src/diffusers"
# Matches is_xxx_available()
lowerCamelCase : Dict = re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
lowerCamelCase : Union[str, Any] = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
lowerCamelCase : Any = "\n{0} = None\n"
lowerCamelCase : List[str] = "\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, {1})\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, {1})\n"
lowerCamelCase : str = "\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n"
def _SCREAMING_SNAKE_CASE ( lowercase : Optional[Any] ):
'''simple docstring'''
lowerCamelCase_ = _re_backend.findall(lowercase )
if len(lowercase ) == 0:
return None
return "_and_".join(lowercase )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
with open(os.path.join(lowercase , '__init__.py' ) , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCamelCase_ = f.readlines()
# Get to the point we do the actual imports for type checking
lowerCamelCase_ = 0
lowerCamelCase_ = {}
# Go through the end of the file
while line_index < len(lowercase ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
lowerCamelCase_ = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith('else:' ):
line_index += 1
line_index += 1
lowerCamelCase_ = []
# Until we unindent, add backend objects to the list
while line_index < len(lowercase ) and len(lines[line_index] ) > 1:
lowerCamelCase_ = lines[line_index]
lowerCamelCase_ = _re_single_line_import.search(lowercase )
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
if len(lowercase ) > 0:
lowerCamelCase_ = objects
else:
line_index += 1
return backend_specific_objects
def _SCREAMING_SNAKE_CASE ( lowercase : List[str] , lowercase : str ):
'''simple docstring'''
if name.isupper():
return DUMMY_CONSTANT.format(lowercase )
elif name.islower():
return DUMMY_FUNCTION.format(lowercase , lowercase )
else:
return DUMMY_CLASS.format(lowercase , lowercase )
def _SCREAMING_SNAKE_CASE ( lowercase : Optional[int]=None ):
'''simple docstring'''
if backend_specific_objects is None:
lowerCamelCase_ = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
lowerCamelCase_ = {}
for backend, objects in backend_specific_objects.items():
lowerCamelCase_ = '[' + ', '.join(f"""\"{b}\"""" for b in backend.split('_and_' ) ) + ']'
lowerCamelCase_ = '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(lowercase , lowercase ) for o in objects] )
lowerCamelCase_ = dummy_file
return dummy_files
def _SCREAMING_SNAKE_CASE ( lowercase : Optional[int]=False ):
'''simple docstring'''
lowerCamelCase_ = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
lowerCamelCase_ = {'torch': 'pt'}
# Locate actual dummy modules and read their content.
lowerCamelCase_ = os.path.join(lowercase , 'utils' )
lowerCamelCase_ = {
backend: os.path.join(lowercase , f"""dummy_{short_names.get(lowercase , lowercase )}_objects.py""" )
for backend in dummy_files.keys()
}
lowerCamelCase_ = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(lowercase ):
with open(lowercase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCamelCase_ = f.read()
else:
lowerCamelCase_ = ''
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
f"""Updating diffusers.utils.dummy_{short_names.get(lowercase , lowercase )}_objects.py as the main """
'__init__ has new objects.' )
with open(dummy_file_paths[backend] , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
'The main __init__ has objects that are not present in '
f"""diffusers.utils.dummy_{short_names.get(lowercase , lowercase )}_objects.py. Run `make fix-copies` """
'to fix this.' )
if __name__ == "__main__":
lowerCamelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
lowerCamelCase : Tuple = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 204 | 1 |
def _a ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
def get_matched_characters(SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str ) -> str:
UpperCamelCase__ : List[Any] = []
UpperCamelCase__ : Any = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
UpperCamelCase__ : str = int(max(0 , i - limit ) )
UpperCamelCase__ : Optional[Any] = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[str] = F"{_stra[0:_stra.index(SCREAMING_SNAKE_CASE )]} {_stra[_stra.index(SCREAMING_SNAKE_CASE ) + 1:]}"
return "".join(SCREAMING_SNAKE_CASE )
# matching characters
UpperCamelCase__ : List[Any] = get_matched_characters(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Union[str, Any] = get_matched_characters(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = len(SCREAMING_SNAKE_CASE )
# transposition
UpperCamelCase__ : List[str] = (
len([(ca, ca) for ca, ca in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) if ca != ca] ) // 2
)
if not match_count:
UpperCamelCase__ : Optional[int] = 0.0
else:
UpperCamelCase__ : Tuple = (
1
/ 3
* (
match_count / len(SCREAMING_SNAKE_CASE )
+ match_count / len(SCREAMING_SNAKE_CASE )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
UpperCamelCase__ : Optional[Any] = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler("hello", "world"))
| 362 |
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
__UpperCamelCase : List[Any] = data_utils.TransfoXLTokenizer
__UpperCamelCase : str = data_utils.TransfoXLCorpus
__UpperCamelCase : Dict = data_utils
__UpperCamelCase : List[Any] = data_utils
def _a ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(SCREAMING_SNAKE_CASE , '''rb''' ) as fp:
UpperCamelCase__ : str = pickle.load(SCREAMING_SNAKE_CASE , encoding='''latin1''' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
UpperCamelCase__ : Tuple = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
print(F"Save vocabulary to {pytorch_vocab_dump_path}" )
UpperCamelCase__ : List[str] = corpus.vocab.__dict__
torch.save(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[str] = corpus.__dict__
corpus_dict_no_vocab.pop('''vocab''' , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[int] = pytorch_dump_folder_path + '''/''' + CORPUS_NAME
print(F"Save dataset to {pytorch_dataset_dump_path}" )
torch.save(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
UpperCamelCase__ : List[Any] = os.path.abspath(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Union[str, Any] = os.path.abspath(SCREAMING_SNAKE_CASE )
print(F"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}." )
# Initialise PyTorch model
if transfo_xl_config_file == "":
UpperCamelCase__ : Any = TransfoXLConfig()
else:
UpperCamelCase__ : int = TransfoXLConfig.from_json_file(SCREAMING_SNAKE_CASE )
print(F"Building PyTorch model from configuration: {config}" )
UpperCamelCase__ : Dict = TransfoXLLMHeadModel(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : str = load_tf_weights_in_transfo_xl(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Save pytorch-model
UpperCamelCase__ : Union[str, Any] = os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[Any] = os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
print(F"Save PyTorch model to {os.path.abspath(SCREAMING_SNAKE_CASE )}" )
torch.save(model.state_dict() , SCREAMING_SNAKE_CASE )
print(F"Save configuration file to {os.path.abspath(SCREAMING_SNAKE_CASE )}" )
with open(SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = 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.",
)
__UpperCamelCase : int = 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,
)
| 51 | 0 |
def __UpperCAmelCase ( __a : Dict ) -> List[Any]:
"""simple docstring"""
_a : Union[str, Any] = [False] * len(_UpperCAmelCase )
_a : Tuple = [-1] * len(_UpperCAmelCase )
def dfs(__a : Tuple ,__a : Optional[int] ):
_a : List[Any] = True
_a : List[Any] = c
for u in graph[v]:
if not visited[u]:
dfs(_UpperCAmelCase ,1 - c )
for i in range(len(_UpperCAmelCase ) ):
if not visited[i]:
dfs(_UpperCAmelCase ,0 )
for i in range(len(_UpperCAmelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
a__ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 235 |
"""simple docstring"""
from copy import deepcopy
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self , snake_case_ = None , snake_case_ = None ):
"""simple docstring"""
if arr is None and size is not None:
A_ : Union[str, Any] = size
A_ : List[str] = [0] * size
elif arr is not None:
self.init(snake_case_ )
else:
raise ValueError('Either arr or size must be specified' )
def lowerCamelCase_ ( self , snake_case_ ):
"""simple docstring"""
A_ : Union[str, Any] = len(snake_case_ )
A_ : Optional[int] = deepcopy(snake_case_ )
for i in range(1 , self.size ):
A_ : Optional[Any] = self.next_(snake_case_ )
if j < self.size:
self.tree[j] += self.tree[i]
def lowerCamelCase_ ( self ):
"""simple docstring"""
A_ : int = self.tree[:]
for i in range(self.size - 1 , 0 , -1 ):
A_ : Optional[int] = self.next_(snake_case_ )
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def lowerCamelCase_ ( snake_case_ ):
"""simple docstring"""
return index + (index & (-index))
@staticmethod
def lowerCamelCase_ ( snake_case_ ):
"""simple docstring"""
return index - (index & (-index))
def lowerCamelCase_ ( self , snake_case_ , snake_case_ ):
"""simple docstring"""
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
A_ : List[str] = self.next_(snake_case_ )
def lowerCamelCase_ ( self , snake_case_ , snake_case_ ):
"""simple docstring"""
self.add(snake_case_ , value - self.get(snake_case_ ) )
def lowerCamelCase_ ( self , snake_case_ ):
"""simple docstring"""
if right == 0:
return 0
A_ : Any = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
A_ : Tuple = self.prev(snake_case_ )
return result
def lowerCamelCase_ ( self , snake_case_ , snake_case_ ):
"""simple docstring"""
return self.prefix(snake_case_ ) - self.prefix(snake_case_ )
def lowerCamelCase_ ( self , snake_case_ ):
"""simple docstring"""
return self.query(snake_case_ , index + 1 )
def lowerCamelCase_ ( self , snake_case_ ):
"""simple docstring"""
value -= self.tree[0]
if value < 0:
return -1
A_ : List[Any] = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
A_ : Tuple = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod() | 286 | 0 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __magic_name__ ( __lowerCAmelCase : Union[str, Any] ) -> Tuple:
__lowerCamelCase = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def __magic_name__ ( __lowerCAmelCase : List[str] ) -> Any:
__lowerCamelCase = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
__lowerCamelCase = s_dict.pop(__lowerCAmelCase )
elif "subsample" in key:
__lowerCamelCase = s_dict.pop(__lowerCAmelCase )
def __magic_name__ ( __lowerCAmelCase : Tuple ) -> Any:
__lowerCamelCase , __lowerCamelCase = emb.weight.shape
__lowerCamelCase = nn.Linear(__lowerCAmelCase , __lowerCAmelCase , bias=__lowerCAmelCase )
__lowerCamelCase = emb.weight.data
return lin_layer
def __magic_name__ ( __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] ) -> Optional[Any]:
__lowerCamelCase = torch.load(__lowerCAmelCase , map_location='''cpu''' )
__lowerCamelCase = mam_aaa['''args''']
__lowerCamelCase = mam_aaa['''model''']
__lowerCamelCase = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(__lowerCAmelCase )
rename_keys(__lowerCAmelCase )
__lowerCamelCase = state_dict['''decoder.embed_tokens.weight'''].shape[0]
__lowerCamelCase = args.share_decoder_input_output_embed
__lowerCamelCase = [int(__lowerCAmelCase ) for i in args.conv_kernel_sizes.split(''',''' )]
__lowerCamelCase = SpeechaTextConfig(
vocab_size=__lowerCAmelCase , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''relu''' , num_conv_layers=len(__lowerCAmelCase ) , conv_channels=args.conv_channels , conv_kernel_sizes=__lowerCAmelCase , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=__lowerCAmelCase , num_beams=5 , max_length=200 , use_cache=__lowerCAmelCase , decoder_start_token_id=2 , early_stopping=__lowerCAmelCase , )
__lowerCamelCase = SpeechaTextForConditionalGeneration(__lowerCAmelCase )
__lowerCamelCase , __lowerCamelCase = model.model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
if len(__lowerCAmelCase ) > 0 and not set(__lowerCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
f''' but all the following weights are missing {missing}''' )
if tie_embeds:
__lowerCamelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
__lowerCamelCase = lm_head_weights
model.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 350 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
return abs(__lowerCAmelCase ) if a == 0 else greatest_common_divisor(b % a , __lowerCAmelCase )
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
while y: # --> when y=0 then loop will terminate and return x as final GCD.
__lowerCamelCase , __lowerCamelCase = y, x % y
return abs(__lowerCAmelCase )
def __magic_name__ ( ) -> Tuple:
try:
__lowerCamelCase = input('''Enter two integers separated by comma (,): ''' ).split(''',''' )
__lowerCamelCase = int(nums[0] )
__lowerCamelCase = int(nums[1] )
print(
f'''greatest_common_divisor({num_a}, {num_a}) = '''
f'''{greatest_common_divisor(__lowerCAmelCase , __lowerCAmelCase )}''' )
print(f'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(__lowerCAmelCase , __lowerCAmelCase )}''' )
except (IndexError, UnboundLocalError, ValueError):
print('''Wrong input''' )
if __name__ == "__main__":
main()
| 339 | 0 |
from __future__ import annotations
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ ) -> None:
__UpperCamelCase =data
__UpperCamelCase =None
__UpperCamelCase =None
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Node | None ): # In Order traversal of the tree
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Node | None ):
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Node ):
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def _UpperCAmelCase ( ): # Main function for testing.
__UpperCamelCase =Node(1 )
__UpperCamelCase =Node(2 )
__UpperCamelCase =Node(3 )
__UpperCamelCase =Node(4 )
__UpperCamelCase =Node(5 )
__UpperCamelCase =Node(6 )
__UpperCamelCase =Node(7 )
__UpperCamelCase =Node(8 )
__UpperCamelCase =Node(9 )
print(is_full_binary_tree(SCREAMING_SNAKE_CASE__ ) )
print(depth_of_tree(SCREAMING_SNAKE_CASE__ ) )
print('Tree is: ' )
display(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
main()
| 62 | '''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
__a: Tuple = None
__a: Tuple = logging.get_logger(__name__)
__a: Optional[Any] = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
__a: Optional[Any] = {
"""vocab_file""": {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
"""t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
"""t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
"""t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
"""t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
},
"""tokenizer_file""": {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
"""t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
"""t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
"""t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
"""t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
},
}
# TODO(PVP) - this should be removed in Transformers v5
__a: Tuple = {
"""t5-small""": 5_12,
"""t5-base""": 5_12,
"""t5-large""": 5_12,
"""t5-3b""": 5_12,
"""t5-11b""": 5_12,
}
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = ["input_ids", "attention_mask"]
SCREAMING_SNAKE_CASE = TaTokenizer
SCREAMING_SNAKE_CASE = []
def __init__( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase="</s>" , __lowerCAmelCase="<unk>" , __lowerCAmelCase="<pad>" , __lowerCAmelCase=100 , __lowerCAmelCase=None , **__lowerCAmelCase , ) -> Union[str, Any]:
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
lowercase__ : Union[str, Any] = [F"""<extra_id_{i}>""" for i in range(__lowerCAmelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
lowercase__ : Dict = len(set(filter(lambda __lowerCAmelCase : bool('''extra_id_''' in str(__lowerCAmelCase ) ) , __lowerCAmelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
super().__init__(
__lowerCAmelCase , tokenizer_file=__lowerCAmelCase , eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , extra_ids=__lowerCAmelCase , additional_special_tokens=__lowerCAmelCase , **__lowerCAmelCase , )
lowercase__ : Union[str, Any] = vocab_file
lowercase__ : Optional[int] = False if not self.vocab_file else True
lowercase__ : Any = extra_ids
@staticmethod
def _lowerCAmelCase( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
lowercase__ : Any = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
F""" {pretrained_model_name_or_path} automatically truncating your input to"""
F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , __lowerCAmelCase , )
return max_model_length
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(__lowerCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase__ : List[Any] = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ):
copyfile(self.vocab_file , __lowerCAmelCase )
logger.info(F"""Copy vocab file to {out_vocab_file}""" )
return (out_vocab_file,)
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase = None ) -> List[int]:
lowercase__ : Any = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
lowercase__ : Dict = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase = None ) -> List[int]:
lowercase__ : Optional[int] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def _lowerCAmelCase( self ) -> List[Any]:
return list(
set(filter(lambda __lowerCAmelCase : bool(re.search(r'''<extra_id_\d+>''' , __lowerCAmelCase ) ) is not None , self.additional_special_tokens ) ) )
def _lowerCAmelCase( self ) -> Tuple:
return [self.convert_tokens_to_ids(__lowerCAmelCase ) for token in self.get_sentinel_tokens()]
| 198 | 0 |
"""simple docstring"""
A__ : List[str] = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.13.2',
'requests-mock': 'requests-mock==1.10.0',
'importlib_metadata': 'importlib_metadata',
'invisible-watermark': 'invisible-watermark',
'isort': 'isort>=5.5.4',
'jax': 'jax>=0.2.8,!=0.3.2',
'jaxlib': 'jaxlib>=0.1.65',
'Jinja2': 'Jinja2',
'k-diffusion': 'k-diffusion>=0.0.12',
'torchsde': 'torchsde',
'note_seq': 'note_seq',
'librosa': 'librosa',
'numpy': 'numpy',
'omegaconf': 'omegaconf',
'parameterized': 'parameterized',
'protobuf': 'protobuf>=3.20.3,<4',
'pytest': 'pytest',
'pytest-timeout': 'pytest-timeout',
'pytest-xdist': 'pytest-xdist',
'ruff': 'ruff>=0.0.241',
'safetensors': 'safetensors',
'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
'scipy': 'scipy',
'onnx': 'onnx',
'regex': 'regex!=2019.12.17',
'requests': 'requests',
'tensorboard': 'tensorboard',
'torch': 'torch>=1.4',
'torchvision': 'torchvision',
'transformers': 'transformers>=4.25.1',
'urllib3': 'urllib3<=2.0.0',
}
| 368 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def _snake_case ( ) -> Tuple:
lowerCamelCase_ : Optional[int] =ArgumentParser("Accelerate CLI tool" , usage="accelerate <command> [<args>]" , allow_abbrev=lowerCamelCase__ )
lowerCamelCase_ : Optional[int] =parser.add_subparsers(help="accelerate command helpers" )
# Register commands
get_config_parser(subparsers=lowerCamelCase__ )
env_command_parser(subparsers=lowerCamelCase__ )
launch_command_parser(subparsers=lowerCamelCase__ )
tpu_command_parser(subparsers=lowerCamelCase__ )
test_command_parser(subparsers=lowerCamelCase__ )
# Let's go
lowerCamelCase_ : int =parser.parse_args()
if not hasattr(lowerCamelCase__ , "func" ):
parser.print_help()
exit(1 )
# Run
args.func(lowerCamelCase__ )
if __name__ == "__main__":
main()
| 209 | 0 |
__A : Dict = '\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__A : Optional[int] = [{'type': 'code', 'content': INSTALL_CONTENT}]
__A : Dict = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 154 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
__A : Optional[int] = 'Run commands across TPU VMs for initial setup before running `accelerate launch`.'
def __UpperCamelCase ( _A : Dict=None ) ->Dict:
"""simple docstring"""
if subparsers is not None:
lowerCamelCase_ =subparsers.add_parser("""tpu-config""" , description=_description )
else:
lowerCamelCase_ =argparse.ArgumentParser("""Accelerate tpu-config command""" , description=_description )
# Core arguments
lowerCamelCase_ =parser.add_argument_group(
"""Config Arguments""" , """Arguments that can be configured through `accelerate config`.""" )
config_args.add_argument(
"""--config_file""" , type=_A , default=_A , help="""Path to the config file to use for accelerate.""" , )
config_args.add_argument(
"""--tpu_name""" , default=_A , help="""The name of the TPU to use. If not specified, will use the TPU specified in the config file.""" , )
config_args.add_argument(
"""--tpu_zone""" , default=_A , help="""The zone of the TPU to use. If not specified, will use the zone specified in the config file.""" , )
lowerCamelCase_ =parser.add_argument_group("""TPU Arguments""" , """Arguments for options ran inside the TPU.""" )
pod_args.add_argument(
"""--use_alpha""" , action="""store_true""" , help="""Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.""" , )
pod_args.add_argument(
"""--command_file""" , default=_A , help="""The path to the file containing the commands to run on the pod on startup.""" , )
pod_args.add_argument(
"""--command""" , action="""append""" , nargs="""+""" , help="""A command to run on the pod. Can be passed multiple times.""" , )
pod_args.add_argument(
"""--install_accelerate""" , action="""store_true""" , help="""Whether to install accelerate on the pod. Defaults to False.""" , )
pod_args.add_argument(
"""--accelerate_version""" , default="""latest""" , help="""The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub.""" , )
pod_args.add_argument(
"""--debug""" , action="""store_true""" , help="""If set, will print the command that would be run instead of running it.""" )
if subparsers is not None:
parser.set_defaults(func=_A )
return parser
def __UpperCamelCase ( _A : Tuple ) ->Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(_A ):
lowerCamelCase_ =load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
lowerCamelCase_ =defaults.command_file
if not args.command and defaults.commands is not None:
lowerCamelCase_ =defaults.commands
if not args.tpu_name:
lowerCamelCase_ =defaults.tpu_name
if not args.tpu_zone:
lowerCamelCase_ =defaults.tpu_zone
if args.accelerate_version == "dev":
lowerCamelCase_ ="""git+https://github.com/huggingface/accelerate.git"""
elif args.accelerate_version == "latest":
lowerCamelCase_ ="""accelerate -U"""
elif isinstance(parse(args.accelerate_version ) , _A ):
lowerCamelCase_ =f'accelerate=={args.accelerate_version}'
if not args.command_file and not args.command:
raise ValueError("""You must specify either a command file or a command to run on the pod.""" )
if args.command_file:
with open(args.command_file , """r""" ) as f:
lowerCamelCase_ =[f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , _A ):
lowerCamelCase_ =[line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
lowerCamelCase_ =["""cd /usr/share"""]
if args.install_accelerate:
new_cmd += [f'pip install {args.accelerate_version}']
new_cmd += args.command
lowerCamelCase_ ="""; """.join(_A )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
lowerCamelCase_ =["""gcloud"""]
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f'Running {" ".join(_A )}' )
return
subprocess.run(_A )
print("""Successfully setup pod.""" )
def __UpperCamelCase ( ) ->Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =tpu_command_parser()
lowerCamelCase_ =parser.parse_args()
tpu_command_launcher(_A )
| 154 | 1 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class lowerCamelCase__:
def __init__( self: str , UpperCamelCase_: List[Any] , UpperCamelCase_: Dict=13 , UpperCamelCase_: Tuple=7 , UpperCamelCase_: Tuple=True , UpperCamelCase_: List[str]=True , UpperCamelCase_: Dict=False , UpperCamelCase_: Optional[Any]=True , UpperCamelCase_: Union[str, Any]=99 , UpperCamelCase_: Dict=32 , UpperCamelCase_: Union[str, Any]=5 , UpperCamelCase_: int=4 , UpperCamelCase_: Dict=37 , UpperCamelCase_: Optional[int]="gelu" , UpperCamelCase_: List[Any]=0.1 , UpperCamelCase_: Union[str, Any]=0.1 , UpperCamelCase_: Any=5_12 , UpperCamelCase_: Union[str, Any]=16 , UpperCamelCase_: Dict=2 , UpperCamelCase_: Tuple=0.02 , UpperCamelCase_: List[Any]=3 , UpperCamelCase_: List[Any]=4 , UpperCamelCase_: Any=None , ):
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
def lowerCAmelCase__ ( self: Dict ):
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_input_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase__ ( self: Optional[int] ):
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCamelCase_ , initializer_range=self.initializer_range , use_stable_embedding=UpperCamelCase_ , )
def lowerCAmelCase__ ( self: Optional[int] , UpperCamelCase_: Dict , UpperCamelCase_: Any , UpperCamelCase_: int , UpperCamelCase_: List[str] , UpperCamelCase_: str , UpperCamelCase_: List[str] , UpperCamelCase_: Any ):
__lowerCamelCase = OpenLlamaModel(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__lowerCamelCase = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ )
__lowerCamelCase = model(UpperCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase__ ( self: Union[str, Any] , UpperCamelCase_: Any , UpperCamelCase_: Optional[int] , UpperCamelCase_: List[str] , UpperCamelCase_: Tuple , UpperCamelCase_: Tuple , UpperCamelCase_: str , UpperCamelCase_: Any , UpperCamelCase_: int , UpperCamelCase_: str , ):
__lowerCamelCase = True
__lowerCamelCase = OpenLlamaModel(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__lowerCamelCase = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , encoder_attention_mask=UpperCamelCase_ , )
__lowerCamelCase = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , )
__lowerCamelCase = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase__ ( self: List[Any] , UpperCamelCase_: Any , UpperCamelCase_: Optional[int] , UpperCamelCase_: int , UpperCamelCase_: Optional[int] , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Optional[int] , UpperCamelCase_: str , UpperCamelCase_: Dict , ):
__lowerCamelCase = OpenLlamaForCausalLM(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__lowerCamelCase = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase__ ( self: Optional[int] , UpperCamelCase_: List[str] , UpperCamelCase_: Optional[int] , UpperCamelCase_: List[Any] , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: int , UpperCamelCase_: Optional[Any] , UpperCamelCase_: List[Any] , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: Any , ):
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = OpenLlamaForCausalLM(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
# first forward pass
__lowerCamelCase = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , encoder_attention_mask=UpperCamelCase_ , use_cache=UpperCamelCase_ , )
__lowerCamelCase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__lowerCamelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
__lowerCamelCase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
__lowerCamelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
__lowerCamelCase = torch.cat([input_mask, next_mask] , dim=-1 )
__lowerCamelCase = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , encoder_attention_mask=UpperCamelCase_ , output_hidden_states=UpperCamelCase_ , )["""hidden_states"""][0]
__lowerCamelCase = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , encoder_attention_mask=UpperCamelCase_ , past_key_values=UpperCamelCase_ , output_hidden_states=UpperCamelCase_ , )["""hidden_states"""][0]
# select random slice
__lowerCamelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__lowerCamelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
__lowerCamelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1E-3 ) )
def lowerCAmelCase__ ( self: Dict ):
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
), (
__lowerCamelCase
), (
__lowerCamelCase
), (
__lowerCamelCase
), (
__lowerCamelCase
), (
__lowerCamelCase
), (
__lowerCamelCase
),
) = config_and_inputs
__lowerCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase__( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase):
UpperCAmelCase__ : int = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
UpperCAmelCase__ : Dict = (OpenLlamaForCausalLM,) if is_torch_available() else ()
UpperCAmelCase__ : int = (
{
'feature-extraction': OpenLlamaModel,
'text-classification': OpenLlamaForSequenceClassification,
'text-generation': OpenLlamaForCausalLM,
'zero-shot': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Optional[int] = False
UpperCAmelCase__ : Optional[Any] = False
def lowerCAmelCase__ ( self: List[Any] ):
__lowerCamelCase = OpenLlamaModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=UpperCamelCase_ , hidden_size=37 )
def lowerCAmelCase__ ( self: Optional[int] ):
self.config_tester.run_common_tests()
def lowerCAmelCase__ ( self: List[Any] ):
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_ )
def lowerCAmelCase__ ( self: Union[str, Any] ):
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCamelCase = type
self.model_tester.create_and_check_model(*UpperCamelCase_ )
def lowerCAmelCase__ ( self: Optional[Any] ):
__lowerCamelCase, __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = 3
__lowerCamelCase = input_dict["""input_ids"""]
__lowerCamelCase = input_ids.ne(1 ).to(UpperCamelCase_ )
__lowerCamelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__lowerCamelCase = OpenLlamaForSequenceClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__lowerCamelCase = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowerCAmelCase__ ( self: str ):
__lowerCamelCase, __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = 3
__lowerCamelCase = """single_label_classification"""
__lowerCamelCase = input_dict["""input_ids"""]
__lowerCamelCase = input_ids.ne(1 ).to(UpperCamelCase_ )
__lowerCamelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__lowerCamelCase = OpenLlamaForSequenceClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__lowerCamelCase = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowerCAmelCase__ ( self: Any ):
__lowerCamelCase, __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = 3
__lowerCamelCase = """multi_label_classification"""
__lowerCamelCase = input_dict["""input_ids"""]
__lowerCamelCase = input_ids.ne(1 ).to(UpperCamelCase_ )
__lowerCamelCase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
__lowerCamelCase = OpenLlamaForSequenceClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__lowerCamelCase = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test""" )
def lowerCAmelCase__ ( self: Dict ):
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)] )
def lowerCAmelCase__ ( self: Optional[Any] , UpperCamelCase_: str ):
__lowerCamelCase, __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = ids_tensor([1, 10] , config.vocab_size )
__lowerCamelCase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__lowerCamelCase = OpenLlamaModel(UpperCamelCase_ )
original_model.to(UpperCamelCase_ )
original_model.eval()
__lowerCamelCase = original_model(UpperCamelCase_ ).last_hidden_state
__lowerCamelCase = original_model(UpperCamelCase_ ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__lowerCamelCase = {"""type""": scaling_type, """factor""": 10.0}
__lowerCamelCase = OpenLlamaModel(UpperCamelCase_ )
scaled_model.to(UpperCamelCase_ )
scaled_model.eval()
__lowerCamelCase = scaled_model(UpperCamelCase_ ).last_hidden_state
__lowerCamelCase = scaled_model(UpperCamelCase_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1E-5 ) )
| 353 |
UpperCAmelCase_ = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
UpperCAmelCase_ = ['a', 'b', 'c', 'd', 'e']
def lowerCamelCase__ ( A__ : Union[str, Any] , A__ : Optional[int] , A__ : str ):
'''simple docstring'''
__lowerCamelCase = start
# add current to visited
visited.append(A__ )
__lowerCamelCase = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
__lowerCamelCase = topological_sort(A__ , A__ , A__ )
# if all neighbors visited add current to sort
sort.append(A__ )
# if all vertices haven't been visited select a new one to visit
if len(A__ ) != len(A__ ):
for vertice in vertices:
if vertice not in visited:
__lowerCamelCase = topological_sort(A__ , A__ , A__ )
# return sort
return sort
if __name__ == "__main__":
UpperCAmelCase_ = topological_sort('a', [], [])
print(sort)
| 29 | 0 |
"""simple docstring"""
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 UpperCAmelCase (unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase :Any = MODEL_FOR_CAUSAL_LM_MAPPING
_UpperCAmelCase :Dict = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def _snake_case ( self ):
lowercase__: Union[str, Any] = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
lowercase__: Any = 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@@'''
)
}
] , )
lowercase__: Any = 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@@'''
)
}
],
] , )
lowercase__: List[Any] = 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 )},
] , )
lowercase__: Optional[Any] = text_generator.model.config.eos_token_id
lowercase__: Tuple = '''<pad>'''
lowercase__: 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 _snake_case ( self ):
lowercase__: Union[str, Any] = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
lowercase__: 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,'''
)
}
] , )
lowercase__: Optional[Any] = 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 _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Optional[Any] = TextGenerationPipeline(model=_UpperCAmelCase , tokenizer=_UpperCAmelCase )
return text_generator, ["This is a test", "Another test"]
def _snake_case ( self ):
lowercase__: Dict = '''Hello I believe in'''
lowercase__: Tuple = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
lowercase__: 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'''}] , )
lowercase__: List[Any] = text_generator(_UpperCAmelCase , stop_sequence=''' fe''' )
self.assertEqual(_UpperCAmelCase , [{'''generated_text''': '''Hello I believe in fe'''}] )
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Any = text_generator.model
lowercase__: Any = text_generator.tokenizer
lowercase__: Optional[Any] = text_generator('''This is a test''' )
self.assertEqual(_UpperCAmelCase , [{'''generated_text''': ANY(_UpperCAmelCase )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
lowercase__: 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'''] )
lowercase__: Union[str, Any] = pipeline(task='''text-generation''' , model=_UpperCAmelCase , tokenizer=_UpperCAmelCase , return_full_text=_UpperCAmelCase )
lowercase__: Tuple = text_generator('''This is a test''' )
self.assertEqual(_UpperCAmelCase , [{'''generated_text''': ANY(_UpperCAmelCase )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
lowercase__: Tuple = 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''' ) )
lowercase__: str = 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:
lowercase__: List[Any] = 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 ):
lowercase__: Any = text_generator('''test''' , return_full_text=_UpperCAmelCase , return_text=_UpperCAmelCase )
with self.assertRaises(_UpperCAmelCase ):
lowercase__: int = text_generator('''test''' , return_full_text=_UpperCAmelCase , return_tensors=_UpperCAmelCase )
with self.assertRaises(_UpperCAmelCase ):
lowercase__: Optional[int] = 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__
):
lowercase__: Any = text_generator('''''' )
self.assertEqual(_UpperCAmelCase , [{'''generated_text''': ANY(_UpperCAmelCase )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
lowercase__: 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.
lowercase__: Optional[int] = ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
if (
tokenizer.model_max_length < 10000
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 )
lowercase__: List[str] = 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 _snake_case ( self ):
import torch
# Classic `model_kwargs`
lowercase__: Any = 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 )
lowercase__: str = 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.)
lowercase__: 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 )
lowercase__: List[str] = 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
lowercase__: Any = 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 )
lowercase__: 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 _snake_case ( self ):
import torch
lowercase__: Union[str, Any] = 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 _snake_case ( self ):
import torch
lowercase__: Union[str, 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 _snake_case ( self ):
lowercase__: Optional[Any] = '''Hello world'''
lowercase__: List[str] = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
lowercase__: List[str] = logging.get_logger('''transformers.generation.tf_utils''' )
else:
lowercase__: List[str] = logging.get_logger('''transformers.generation.utils''' )
lowercase__: List[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:
lowercase__: Any = 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:
lowercase__: List[Any] = text_generator(_UpperCAmelCase , max_new_tokens=1 )
self.assertNotIn(_UpperCAmelCase , cl.out )
with CaptureLogger(_UpperCAmelCase ) as cl:
lowercase__: Optional[Any] = text_generator(_UpperCAmelCase , max_length=10 )
self.assertNotIn(_UpperCAmelCase , cl.out )
| 177 | """simple docstring"""
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> int:
if n == 1 or not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
return 0
elif n == 2:
return 1
else:
lowercase__: List[Any] = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> int:
lowercase__: Union[str, Any] = 0
lowercase__: List[Any] = 2
while digits < n:
index += 1
lowercase__: Dict = len(str(fibonacci(__UpperCAmelCase ) ) )
return index
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase = 1_0_0_0 ) -> int:
return fibonacci_digits_index(__UpperCAmelCase )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 177 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
UpperCAmelCase_ : Union[str, Any] = None
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
UpperCAmelCase_ : List[Any] = {
'vocab_file': {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
),
},
'tokenizer_file': {
'google/bigbird-roberta-base': (
'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
),
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
),
},
}
UpperCAmelCase_ : str = {
'google/bigbird-roberta-base': 4096,
'google/bigbird-roberta-large': 4096,
'google/bigbird-base-trivia-itc': 4096,
}
UpperCAmelCase_ : Dict = '▁'
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : List[Any] = VOCAB_FILES_NAMES
snake_case__ : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
snake_case__ : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ : List[str] = BigBirdTokenizer
snake_case__ : Tuple = ['''input_ids''', '''attention_mask''']
snake_case__ : List[int] = []
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : int=None , SCREAMING_SNAKE_CASE__ : Optional[Any]=None , SCREAMING_SNAKE_CASE__ : Optional[Any]="<unk>" , SCREAMING_SNAKE_CASE__ : Dict="<s>" , SCREAMING_SNAKE_CASE__ : List[Any]="</s>" , SCREAMING_SNAKE_CASE__ : Optional[Any]="<pad>" , SCREAMING_SNAKE_CASE__ : str="[SEP]" , SCREAMING_SNAKE_CASE__ : Tuple="[MASK]" , SCREAMING_SNAKE_CASE__ : Optional[int]="[CLS]" , **SCREAMING_SNAKE_CASE__ : Dict , ) -> int:
a_ : List[str] = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else bos_token
a_ : List[Any] = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else eos_token
a_ : List[str] = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else unk_token
a_ : List[Any] = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else pad_token
a_ : Union[str, Any] = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else cls_token
a_ : List[Any] = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
a_ : Tuple = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else mask_token
super().__init__(
SCREAMING_SNAKE_CASE__ , tokenizer_file=SCREAMING_SNAKE_CASE__ , bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , mask_token=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
a_ : Any = vocab_file
a_ : Optional[int] = False if not self.vocab_file else True
def SCREAMING_SNAKE_CASE ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : Optional[List[int]] = None ) -> List[int]:
a_ : Union[str, Any] = [self.sep_token_id]
a_ : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self : List[Any] , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : Optional[List[int]] = None , SCREAMING_SNAKE_CASE__ : bool = False ) -> List[int]:
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : Optional[List[int]] = None ) -> List[int]:
a_ : Any = [self.sep_token_id]
a_ : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[str] = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
a_ : str = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE__ ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE__ )
return (out_vocab_file,)
| 120 |
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__A , __A ):
return 0
elif n == 2:
return 1
else:
a_ : int = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> int:
"""simple docstring"""
a_ : Any = 0
a_ : Optional[Any] = 2
while digits < n:
index += 1
a_ : List[Any] = len(str(fibonacci(__A ) ) )
return index
def SCREAMING_SNAKE_CASE_ ( __A : int = 10_00 ) -> int:
"""simple docstring"""
return fibonacci_digits_index(__A )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 120 | 1 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : int = 1_0 , _UpperCamelCase : int = 2_2 ) -> int:
'''simple docstring'''
__UpperCAmelCase : List[Any] = range(1 , _UpperCamelCase )
__UpperCAmelCase : Optional[int] = range(1 , _UpperCamelCase )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ) == power )
if __name__ == "__main__":
print(F"{solution(10, 22) = }")
| 115 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowerCamelCase__ :
"""simple docstring"""
@staticmethod
def lowerCamelCase__ ( *UpperCamelCase : List[str] , **UpperCamelCase : Any ):
'''simple docstring'''
pass
@is_pipeline_test
@require_vision
@require_torch
class lowerCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
__a = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def lowerCamelCase__ ( self : List[str] , UpperCamelCase : Tuple , UpperCamelCase : Optional[int] , UpperCamelCase : Tuple ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = pipeline(
"""zero-shot-object-detection""" , model="""hf-internal-testing/tiny-random-owlvit-object-detection""" )
__UpperCAmelCase : Tuple = [
{
"""image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
}
]
return object_detector, examples
def lowerCamelCase__ ( self : int , UpperCamelCase : Optional[int] , UpperCamelCase : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = object_detector(examples[0] , threshold=0.0 )
__UpperCAmelCase : Tuple = len(UpperCamelCase )
self.assertGreater(UpperCamelCase , 0 )
self.assertEqual(
UpperCamelCase , [
{
"""score""": ANY(UpperCamelCase ),
"""label""": ANY(UpperCamelCase ),
"""box""": {"""xmin""": ANY(UpperCamelCase ), """ymin""": ANY(UpperCamelCase ), """xmax""": ANY(UpperCamelCase ), """ymax""": ANY(UpperCamelCase )},
}
for i in range(UpperCamelCase )
] , )
@require_tf
@unittest.skip("""Zero Shot Object Detection not implemented in TF""" )
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
pass
@require_torch
def lowerCamelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : Tuple = pipeline(
"""zero-shot-object-detection""" , model="""hf-internal-testing/tiny-random-owlvit-object-detection""" )
__UpperCAmelCase : Tuple = object_detector(
"""./tests/fixtures/tests_samples/COCO/000000039769.png""" , candidate_labels=["""cat""", """remote""", """couch"""] , threshold=0.64 , )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"""score""": 0.7235, """label""": """cat""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
{"""score""": 0.7218, """label""": """remote""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
{"""score""": 0.7184, """label""": """couch""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
{"""score""": 0.6748, """label""": """remote""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
{"""score""": 0.6656, """label""": """cat""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
{"""score""": 0.6614, """label""": """couch""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
{"""score""": 0.6456, """label""": """remote""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
{"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 274, """xmax""": 93, """ymax""": 297}},
{"""score""": 0.6419, """label""": """cat""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
] , )
__UpperCAmelCase : Any = object_detector(
[
{
"""image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
}
] , threshold=0.64 , )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
[
{"""score""": 0.7235, """label""": """cat""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
{"""score""": 0.7218, """label""": """remote""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
{"""score""": 0.7184, """label""": """couch""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
{"""score""": 0.6748, """label""": """remote""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
{"""score""": 0.6656, """label""": """cat""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
{"""score""": 0.6614, """label""": """couch""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
{"""score""": 0.6456, """label""": """remote""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
{"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 274, """xmax""": 93, """ymax""": 297}},
{"""score""": 0.6419, """label""": """cat""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
]
] , )
@require_torch
@slow
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : Any = pipeline("""zero-shot-object-detection""" )
__UpperCAmelCase : int = object_detector(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" , candidate_labels=["""cat""", """remote""", """couch"""] , )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
{"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
{"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
{"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
{"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
] , )
__UpperCAmelCase : List[str] = object_detector(
[
{
"""image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
},
{
"""image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
},
] , )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
[
{"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
{"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
{"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
{"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
{"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
],
[
{"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
{"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
{"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
{"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
{"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
],
] , )
@require_tf
@unittest.skip("""Zero Shot Object Detection not implemented in TF""" )
def lowerCamelCase__ ( self : Any ):
'''simple docstring'''
pass
@require_torch
@slow
def lowerCamelCase__ ( self : str ):
'''simple docstring'''
__UpperCAmelCase : List[str] = 0.2
__UpperCAmelCase : List[Any] = pipeline("""zero-shot-object-detection""" )
__UpperCAmelCase : Any = object_detector(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" , candidate_labels=["""cat""", """remote""", """couch"""] , threshold=UpperCamelCase , )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
{"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
{"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
] , )
@require_torch
@slow
def lowerCamelCase__ ( self : int ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = 2
__UpperCAmelCase : Union[str, Any] = pipeline("""zero-shot-object-detection""" )
__UpperCAmelCase : List[str] = object_detector(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" , candidate_labels=["""cat""", """remote""", """couch"""] , top_k=UpperCamelCase , )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
{"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
] , )
| 115 | 1 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowercase (_A , _A , _A , _A , _A ):
"""simple docstring"""
with open(_A ) as metadata_file:
_lowerCAmelCase : List[Any] = json.load(_A )
_lowerCAmelCase : int = LukeConfig(use_entity_aware_attention=_A , **metadata['model_config'] )
# Load in the weights from the checkpoint_path
_lowerCAmelCase : Tuple = torch.load(_A , map_location='cpu' )
# Load the entity vocab file
_lowerCAmelCase : Any = load_entity_vocab(_A )
_lowerCAmelCase : List[Any] = RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )
# Add special tokens to the token vocabulary for downstream tasks
_lowerCAmelCase : Dict = AddedToken('<ent>' , lstrip=_A , rstrip=_A )
_lowerCAmelCase : Optional[Any] = AddedToken('<ent2>' , lstrip=_A , rstrip=_A )
tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(_A )
with open(os.path.join(_A , LukeTokenizer.vocab_files_names['entity_vocab_file'] ) , 'w' ) as f:
json.dump(_A , _A )
_lowerCAmelCase : str = LukeTokenizer.from_pretrained(_A )
# Initialize the embeddings of the special tokens
_lowerCAmelCase : int = state_dict['embeddings.word_embeddings.weight']
_lowerCAmelCase : Optional[Any] = word_emb[tokenizer.convert_tokens_to_ids(['@'] )[0]].unsqueeze(0 )
_lowerCAmelCase : List[str] = word_emb[tokenizer.convert_tokens_to_ids(['#'] )[0]].unsqueeze(0 )
_lowerCAmelCase : str = torch.cat([word_emb, ent_emb, enta_emb] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
_lowerCAmelCase : Optional[int] = f'encoder.layer.{layer_index}.attention.self.'
_lowerCAmelCase : Tuple = state_dict[prefix + matrix_name]
_lowerCAmelCase : int = state_dict[prefix + matrix_name]
_lowerCAmelCase : Tuple = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
_lowerCAmelCase : Any = state_dict['entity_embeddings.entity_embeddings.weight']
_lowerCAmelCase : Any = entity_emb[entity_vocab['[MASK]']]
_lowerCAmelCase : Optional[Any] = LukeModel(config=_A ).eval()
_lowerCAmelCase , _lowerCAmelCase : Dict = model.load_state_dict(_A , strict=_A )
if not (len(_A ) == 1 and missing_keys[0] == "embeddings.position_ids"):
raise ValueError(f'Missing keys {", ".join(_A )}. Expected only missing embeddings.position_ids' )
if not (all(key.startswith('entity_predictions' ) or key.startswith('lm_head' ) for key in unexpected_keys )):
raise ValueError(
'Unexpected keys'
f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}' )
# Check outputs
_lowerCAmelCase : Dict = LukeTokenizer.from_pretrained(_A , task='entity_classification' )
_lowerCAmelCase : Dict = (
'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
' new world number one avoid a humiliating second- round exit at Wimbledon .'
)
_lowerCAmelCase : Dict = (3_9, 4_2)
_lowerCAmelCase : List[str] = tokenizer(_A , entity_spans=[span] , add_prefix_space=_A , return_tensors='pt' )
_lowerCAmelCase : List[Any] = model(**_A )
# Verify word hidden states
if model_size == "large":
_lowerCAmelCase : Union[str, Any] = torch.Size((1, 4_2, 1_0_2_4) )
_lowerCAmelCase : Dict = torch.tensor(
[[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]] )
else: # base
_lowerCAmelCase : List[Any] = torch.Size((1, 4_2, 7_6_8) )
_lowerCAmelCase : Optional[Any] = torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , _A , atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
_lowerCAmelCase : str = torch.Size((1, 1, 1_0_2_4) )
_lowerCAmelCase : List[str] = torch.tensor([[0.0_466, -0.0_106, -0.0_179]] )
else: # base
_lowerCAmelCase : str = torch.Size((1, 1, 7_6_8) )
_lowerCAmelCase : Dict = torch.tensor([[0.1_457, 0.1_044, 0.0_174]] )
if not (outputs.entity_last_hidden_state.shape != expected_shape):
raise ValueError(
f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
f' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , _A , atol=1E-4 ):
raise ValueError
# Finally, save our PyTorch model and tokenizer
print('Saving PyTorch model to {}'.format(_A ) )
model.save_pretrained(_A )
def lowercase (_A ):
"""simple docstring"""
_lowerCAmelCase : Any = {}
with open(_A , 'r' , encoding='utf-8' ) as f:
for index, line in enumerate(_A ):
_lowerCAmelCase , _lowerCAmelCase : Tuple = line.rstrip().split('\t' )
_lowerCAmelCase : str = index
return entity_vocab
if __name__ == "__main__":
lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
parser.add_argument(
"""--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
)
parser.add_argument(
"""--entity_vocab_path""",
default=None,
type=str,
help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
)
parser.add_argument(
"""--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
)
lowerCAmelCase : Optional[int] = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 25 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Tuple = SMALL_MODEL_IDENTIFIER
_lowerCAmelCase : Optional[int] = 'pt'
_lowerCAmelCase : Tuple = 'tf'
def a ( self , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(snake_case__ )
def a ( self , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : Tuple = TFAutoModel.from_pretrained(self.test_model , from_pt=snake_case__ )
model_tf.save_pretrained(snake_case__ )
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Tuple = 'mock_framework'
# Framework provided - return whatever the user provides
_lowerCAmelCase : Any = FeaturesManager.determine_framework(self.test_model , snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(snake_case__ )
_lowerCAmelCase : Dict = FeaturesManager.determine_framework(snake_case__ , snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(snake_case__ )
_lowerCAmelCase : int = FeaturesManager.determine_framework(snake_case__ , snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
def a ( self ):
'''simple docstring'''
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(snake_case__ )
_lowerCAmelCase : Tuple = FeaturesManager.determine_framework(snake_case__ )
self.assertEqual(snake_case__ , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(snake_case__ )
_lowerCAmelCase : Optional[int] = FeaturesManager.determine_framework(snake_case__ )
self.assertEqual(snake_case__ , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(snake_case__ ):
_lowerCAmelCase : str = FeaturesManager.determine_framework(snake_case__ )
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = MagicMock(return_value=snake_case__ )
with patch('transformers.onnx.features.is_tf_available' , snake_case__ ):
_lowerCAmelCase : Any = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(snake_case__ , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
_lowerCAmelCase : Any = MagicMock(return_value=snake_case__ )
with patch('transformers.onnx.features.is_torch_available' , snake_case__ ):
_lowerCAmelCase : Union[str, Any] = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(snake_case__ , self.framework_tf )
# Both in environment -> use PyTorch
_lowerCAmelCase : int = MagicMock(return_value=snake_case__ )
_lowerCAmelCase : Optional[int] = MagicMock(return_value=snake_case__ )
with patch('transformers.onnx.features.is_tf_available' , snake_case__ ), patch(
'transformers.onnx.features.is_torch_available' , snake_case__ ):
_lowerCAmelCase : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(snake_case__ , self.framework_pt )
# Both not in environment -> raise error
_lowerCAmelCase : str = MagicMock(return_value=snake_case__ )
_lowerCAmelCase : Optional[Any] = MagicMock(return_value=snake_case__ )
with patch('transformers.onnx.features.is_tf_available' , snake_case__ ), patch(
'transformers.onnx.features.is_torch_available' , snake_case__ ):
with self.assertRaises(snake_case__ ):
_lowerCAmelCase : Any = FeaturesManager.determine_framework(self.test_model )
| 25 | 1 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowercase_ (A : List[str] , A : Optional[Any] , A : Dict ):
# Construct model
if gpta_config_file == "":
snake_case__ : str = GPTaConfig()
else:
snake_case__ : Any = GPTaConfig.from_json_file(A )
snake_case__ : str = GPTaModel(A )
# Load weights from numpy
load_tf_weights_in_gpta(A , A , A )
# Save pytorch-model
snake_case__ : str = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
snake_case__ : List[Any] = pytorch_dump_folder_path + '/' + CONFIG_NAME
print(F'''Save PyTorch model to {pytorch_weights_dump_path}''' )
torch.save(model.state_dict() , A )
print(F'''Save configuration file to {pytorch_config_dump_path}''' )
with open(A , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
a_ :Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--gpt2_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained OpenAI model. \n"
"This specifies the model architecture."
),
)
a_ :int = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 277 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
a_ :Optional[Any] = logging.getLogger(__name__)
def lowercase_ (A : List[Any] , A : List[Any] ):
# save results
if os.path.exists(A ):
if os.path.exists(os.path.join(A , 'config.json' ) ) and os.path.isfile(
os.path.join(A , 'config.json' ) ):
os.remove(os.path.join(A , 'config.json' ) )
if os.path.exists(os.path.join(A , 'pytorch_model.bin' ) ) and os.path.isfile(
os.path.join(A , 'pytorch_model.bin' ) ):
os.remove(os.path.join(A , 'pytorch_model.bin' ) )
else:
os.makedirs(A )
model.save_pretrained(A )
def lowercase_ (A : Any , A : Optional[Any]=False ):
snake_case__ : str = 2
if unlogit:
snake_case__ : Dict = torch.pow(A , A )
snake_case__ : Any = p * torch.log(A )
snake_case__ : Tuple = 0
return -plogp.sum(dim=-1 )
def lowercase_ (A : List[str] ):
logger.info('lv, h >\t' + '\t'.join(F'''{x + 1}''' for x in range(len(A ) ) ) )
for row in range(len(A ) ):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + '\t'.join(F'''{x:.5f}''' for x in tensor[row].cpu().data ) )
else:
logger.info(F'''layer {row + 1}:\t''' + '\t'.join(F'''{x:d}''' for x in tensor[row].cpu().data ) )
def lowercase_ (A : Tuple , A : Optional[Any] , A : str , A : int=True , A : Optional[int]=True , A : Any=None , A : int=False ):
snake_case__ , snake_case__ : Optional[Any] = model.config.num_hidden_layers, model.config.num_attention_heads
snake_case__ : int = torch.zeros(A , A ).to(args.device )
snake_case__ : Any = torch.zeros(A , A ).to(args.device )
if head_mask is None:
snake_case__ : Dict = torch.ones(A , A ).to(args.device )
head_mask.requires_grad_(requires_grad=A )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
snake_case__ : Optional[int] = None
snake_case__ : List[Any] = 0.0
snake_case__ : str = 0.0
for step, inputs in enumerate(tqdm(A , desc='Iteration' , disable=args.local_rank not in [-1, 0] ) ):
snake_case__ : Union[str, Any] = tuple(t.to(args.device ) for t in inputs )
((snake_case__) , ) : Optional[Any] = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
snake_case__ : Union[str, Any] = model(A , labels=A , head_mask=A )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
snake_case__ , snake_case__ , snake_case__ : Dict = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(A ):
snake_case__ : Optional[Any] = entropy(attn.detach() , A )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(A ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
snake_case__ : Union[str, Any] = 2
snake_case__ : List[Any] = torch.pow(torch.pow(A , A ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-20
if not args.dont_normalize_global_importance:
snake_case__ : Tuple = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('Attention entropies' )
print_ad_tensor(A )
if compute_importance:
logger.info('Head importance scores' )
print_ad_tensor(A )
logger.info('Head ranked by importance scores' )
snake_case__ : Tuple = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
snake_case__ : Union[str, Any] = torch.arange(
head_importance.numel() , device=args.device )
snake_case__ : str = head_ranks.view_as(A )
print_ad_tensor(A )
return attn_entropy, head_importance, total_loss
def lowercase_ (A : Optional[int] , A : Dict , A : Optional[int] ):
snake_case__ , snake_case__ , snake_case__ : Any = compute_heads_importance(A , A , A , compute_entropy=A )
snake_case__ : Tuple = 1 / loss # instead of downsteam score use the LM loss
logger.info('Pruning: original score: %f, threshold: %f' , A , original_score * args.masking_threshold )
snake_case__ : Optional[Any] = torch.ones_like(A )
snake_case__ : Union[str, Any] = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
snake_case__ : Dict = original_score
while current_score >= original_score * args.masking_threshold:
snake_case__ : int = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
snake_case__ : List[Any] = float('Inf' )
snake_case__ : Union[str, Any] = head_importance.view(-1 ).sort()[1]
if len(A ) <= num_to_mask:
print('BREAK BY num_to_mask' )
break
# mask heads
snake_case__ : int = current_heads_to_mask[:num_to_mask]
logger.info('Heads to mask: %s' , str(current_heads_to_mask.tolist() ) )
snake_case__ : int = new_head_mask.view(-1 )
snake_case__ : int = 0.0
snake_case__ : Union[str, Any] = new_head_mask.view_as(A )
snake_case__ : List[str] = new_head_mask.clone().detach()
print_ad_tensor(A )
# Compute metric and head importance again
snake_case__ , snake_case__ , snake_case__ : Any = compute_heads_importance(
A , A , A , compute_entropy=A , head_mask=A )
snake_case__ : Dict = 1 / loss
logger.info(
'Masking: current score: %f, remaining heads %d (%.1f percents)' , A , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 1_0_0 , )
logger.info('Final head mask' )
print_ad_tensor(A )
np.save(os.path.join(args.output_dir , 'head_mask.npy' ) , head_mask.detach().cpu().numpy() )
return head_mask
def lowercase_ (A : List[str] , A : Tuple , A : Optional[Any] , A : int ):
snake_case__ : Any = datetime.now()
snake_case__ , snake_case__ , snake_case__ : str = compute_heads_importance(
A , A , A , compute_entropy=A , compute_importance=A , head_mask=A )
snake_case__ : Tuple = 1 / loss
snake_case__ : Dict = datetime.now() - before_time
snake_case__ : Union[str, Any] = sum(p.numel() for p in model.parameters() )
snake_case__ : Optional[Any] = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(A ) )
}
for k, v in heads_to_prune.items():
if isinstance(A , A ):
snake_case__ : Any = [
v,
]
assert sum(len(A ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(A )
snake_case__ : Dict = sum(p.numel() for p in model.parameters() )
snake_case__ : Tuple = datetime.now()
snake_case__ , snake_case__ , snake_case__ : Dict = compute_heads_importance(
A , A , A , compute_entropy=A , compute_importance=A , head_mask=A , actually_pruned=A , )
snake_case__ : Any = 1 / loss
snake_case__ : int = datetime.now() - before_time
logger.info(
'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)' , A , A , pruned_num_params / original_num_params * 1_0_0 , )
logger.info('Pruning: score with masking: %f score with pruning: %f' , A , A )
logger.info('Pruning: speed ratio (original timing / new timing): %f percents' , original_time / new_time * 1_0_0 )
save_model(A , args.output_dir )
def lowercase_ ():
snake_case__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--data_dir' , default=A , type=A , required=A , help='The input data dir. Should contain the .tsv files (or other data files) for the task.' , )
parser.add_argument(
'--model_name_or_path' , default=A , type=A , required=A , help='Path to pretrained model or model identifier from huggingface.co/models' , )
parser.add_argument(
'--output_dir' , default=A , type=A , required=A , help='The output directory where the model predictions and checkpoints will be written.' , )
# Other parameters
parser.add_argument(
'--config_name' , default='' , type=A , help='Pretrained config name or path if not the same as model_name_or_path' , )
parser.add_argument(
'--tokenizer_name' , default='' , type=A , help='Pretrained tokenizer name or path if not the same as model_name_or_path' , )
parser.add_argument(
'--cache_dir' , default=A , type=A , help='Where do you want to store the pre-trained models downloaded from s3' , )
parser.add_argument(
'--data_subset' , type=A , default=-1 , help='If > 0: limit the data to a subset of data_subset instances.' )
parser.add_argument(
'--overwrite_output_dir' , action='store_true' , help='Whether to overwrite data in output directory' )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
parser.add_argument(
'--dont_normalize_importance_by_layer' , action='store_true' , help='Don\'t normalize importance score by layers' )
parser.add_argument(
'--dont_normalize_global_importance' , action='store_true' , help='Don\'t normalize all importance scores between 0 and 1' , )
parser.add_argument(
'--try_masking' , action='store_true' , help='Whether to try to mask head until a threshold of accuracy.' )
parser.add_argument(
'--masking_threshold' , default=0.9 , type=A , help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).' , )
parser.add_argument(
'--masking_amount' , default=0.1 , type=A , help='Amount to heads to masking at each masking step.' )
parser.add_argument('--metric_name' , default='acc' , type=A , help='Metric to use for head masking.' )
parser.add_argument(
'--max_seq_length' , default=1_2_8 , type=A , help=(
'The maximum total input sequence length after WordPiece tokenization. \n'
'Sequences longer than this will be truncated, sequences shorter padded.'
) , )
parser.add_argument('--batch_size' , default=1 , type=A , help='Batch size.' )
parser.add_argument('--seed' , type=A , default=4_2 )
parser.add_argument('--local_rank' , type=A , default=-1 , help='local_rank for distributed training on gpus' )
parser.add_argument('--no_cuda' , action='store_true' , help='Whether not to use CUDA when available' )
parser.add_argument('--server_ip' , type=A , default='' , help='Can be used for distant debugging.' )
parser.add_argument('--server_port' , type=A , default='' , help='Can be used for distant debugging.' )
snake_case__ : Optional[int] = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('Waiting for debugger attach' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=A )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
snake_case__ : List[Any] = torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu' )
snake_case__ : Optional[Any] = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
snake_case__ : int = torch.device('cuda' , args.local_rank )
snake_case__ : List[str] = 1
torch.distributed.init_process_group(backend='nccl' ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
snake_case__ : Any = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
snake_case__ : List[str] = nn.parallel.DistributedDataParallel(
A , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=A )
elif args.n_gpu > 1:
snake_case__ : Optional[int] = nn.DataParallel(A )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=A )
torch.save(A , os.path.join(args.output_dir , 'run_args.bin' ) )
logger.info('Training/evaluation parameters %s' , A )
# Prepare dataset
snake_case__ : Optional[Any] = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
snake_case__ : List[str] = (torch.from_numpy(A ),)
snake_case__ : int = TensorDataset(*A )
snake_case__ : Union[str, Any] = RandomSampler(A )
snake_case__ : Any = DataLoader(A , sampler=A , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(A , A , A )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
snake_case__ : Dict = mask_heads(A , A , A )
prune_heads(A , A , A , A )
if __name__ == "__main__":
main()
| 277 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : str , _lowerCAmelCase : Optional[int] , ):
A = parent
A = 13
A = 7
A = True
A = True
A = True
A = 99
A = 32
A = 2
A = 4
A = 37
A = """gelu"""
A = 0.1
A = 0.1
A = 512
A = 16
A = 2
A = 0.02
A = 3
A = 4
A = None
def A (self : Dict ):
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] )
A = None
A = None
A = None
if self.use_labels:
A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A = ids_tensor([self.batch_size] , self.num_choices )
A = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , 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, input_mask, sequence_labels, token_labels, choice_labels
def A (self : Dict ):
(
(
A
) , (
A
) , (
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,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def A (self : Any , _lowerCAmelCase : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : str ):
A = TFEsmModel(config=_lowerCAmelCase )
A = {"""input_ids""": input_ids, """attention_mask""": input_mask}
A = model(_lowerCAmelCase )
A = [input_ids, input_mask]
A = model(_lowerCAmelCase )
A = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A (self : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] , ):
A = True
A = TFEsmModel(config=_lowerCAmelCase )
A = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""encoder_hidden_states""": encoder_hidden_states,
"""encoder_attention_mask""": encoder_attention_mask,
}
A = model(_lowerCAmelCase )
A = [input_ids, input_mask]
A = model(_lowerCAmelCase , encoder_hidden_states=_lowerCAmelCase )
# Also check the case where encoder outputs are not passed
A = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A (self : Tuple , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple ):
A = TFEsmForMaskedLM(config=_lowerCAmelCase )
A = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A (self : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] ):
A = self.num_labels
A = TFEsmForTokenClassification(config=_lowerCAmelCase )
A = {"""input_ids""": input_ids, """attention_mask""": input_mask}
A = model(_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A (self : Union[str, Any] ):
A = self.prepare_config_and_inputs()
(
(
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) ,
) = config_and_inputs
A = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class __UpperCAmelCase ( A__ , A__ , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
__lowerCAmelCase = (
{
'''feature-extraction''': TFEsmModel,
'''fill-mask''': TFEsmForMaskedLM,
'''text-classification''': TFEsmForSequenceClassification,
'''token-classification''': TFEsmForTokenClassification,
'''zero-shot''': TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowerCAmelCase = False
__lowerCAmelCase = False
def A (self : List[str] ):
A = TFEsmModelTester(self )
A = ConfigTester(self , config_class=_lowerCAmelCase , hidden_size=37 )
def A (self : str ):
self.config_tester.run_common_tests()
def A (self : Dict ):
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def A (self : Union[str, Any] ):
A = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_lowerCAmelCase )
def A (self : Optional[int] ):
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase )
def A (self : Any ):
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase )
@slow
def A (self : Any ):
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A = TFEsmModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
@unittest.skip("""Protein models do not support embedding resizing.""" )
def A (self : Optional[int] ):
pass
@unittest.skip("""Protein models do not support embedding resizing.""" )
def A (self : int ):
pass
def A (self : List[str] ):
A , A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A = model_class(_lowerCAmelCase )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
A = model.get_bias()
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
for k, v in name.items():
assert isinstance(_lowerCAmelCase , tf.Variable )
else:
A = model.get_output_embeddings()
assert x is None
A = model.get_bias()
assert name is None
@require_tf
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def A (self : List[str] ):
A = TFEsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
A = tf.constant([[0, 1, 2, 3, 4, 5]] )
A = model(_lowerCAmelCase )[0]
A = [1, 6, 33]
self.assertEqual(list(output.numpy().shape ) , _lowerCAmelCase )
# compare the actual values for a slice.
A = tf.constant(
[
[
[8.921_518, -10.589_814, -6.4_671_307],
[-6.3_967_156, -13.911_377, -1.1_211_915],
[-7.781_247, -13.951_557, -3.740_592],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-2 ) )
@slow
def A (self : Dict ):
A = TFEsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
A = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
A = model(_lowerCAmelCase )[0]
# compare the actual values for a slice.
A = tf.constant(
[
[
[0.14_443_092, 0.54_125_327, 0.3_247_739],
[0.30_340_484, 0.00_526_676, 0.31_077_722],
[0.32_278_043, -0.24_987_096, 0.3_414_628],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 337 |
'''simple docstring'''
import os
def __a ( ) ->List[Any]:
"""simple docstring"""
A = os.path.join(os.path.dirname(UpperCAmelCase ) , """num.txt""" )
with open(UpperCAmelCase ) as file_hand:
return str(sum(int(UpperCAmelCase ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 337 | 1 |
import qiskit
def __lowerCamelCase ( UpperCAmelCase_ : int = 2 ):
"""simple docstring"""
a :Tuple = qubits
# Using Aer's simulator
a :Union[str, Any] = qiskit.Aer.get_backend('''aer_simulator''' )
# Creating a Quantum Circuit acting on the q register
a :str = qiskit.QuantumCircuit(UpperCAmelCase_ , UpperCAmelCase_ )
# Adding a H gate on qubit 0 (now q0 in superposition)
circuit.h(0 )
for i in range(1 , UpperCAmelCase_ ):
# Adding CX (CNOT) gate
circuit.cx(i - 1 , UpperCAmelCase_ )
# Mapping the quantum measurement to the classical bits
circuit.measure(list(range(UpperCAmelCase_ ) ) , list(range(UpperCAmelCase_ ) ) )
# Now measuring any one qubit would affect other qubits to collapse
# their super position and have same state as the measured one.
# Executing the circuit on the simulator
a :Union[str, Any] = qiskit.execute(UpperCAmelCase_ , UpperCAmelCase_ , shots=1000 )
return job.result().get_counts(UpperCAmelCase_ )
if __name__ == "__main__":
print(F"""Total count for various states are: {quantum_entanglement(3)}""")
| 94 | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('TEST_SAGEMAKER' , 'False' ) ) is not True , reason='Skipping test because should only be run when releasing minor transformers version' , )
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
[
{
'framework': 'pytorch',
'script': 'run_glue.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.g4dn.xlarge',
'results': {'train_runtime': 6_50, 'eval_accuracy': 0.6, 'eval_loss': 0.9},
},
{
'framework': 'tensorflow',
'script': 'run_tf.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.g4dn.xlarge',
'results': {'train_runtime': 6_00, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
},
] )
class A ( unittest.TestCase ):
def lowercase_ (self : int ) -> Optional[Any]:
"""simple docstring"""
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="utf-8" , check=__UpperCAmelCase , )
assert hasattr(self , "env" )
def lowercase_ (self : List[Any] , __UpperCAmelCase : Optional[int]=1 ) -> Dict:
"""simple docstring"""
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f"""{self.env.base_job_name}-single""" , instance_count=__UpperCAmelCase , instance_type=self.instance_type , debugger_hook_config=__UpperCAmelCase , hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path} , metric_definitions=self.env.metric_definitions , py_version="py36" , )
def lowercase_ (self : Optional[Any] , __UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
TrainingJobAnalytics(__UpperCAmelCase ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
def lowercase_ (self : Any ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = self.create_estimator()
# run training
estimator.fit()
# result dataframe
UpperCAmelCase__ = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
UpperCAmelCase__ = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"] )
UpperCAmelCase__ = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
UpperCAmelCase__ = (
Session().describe_training_job(estimator.latest_training_job.name ).get("TrainingTimeInSeconds" , 9_9_9_9_9_9 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy )
assert all(t <= self.results["eval_loss"] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , "w" ) as outfile:
json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss} , __UpperCAmelCase )
| 65 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : UNetaDModel
UpperCAmelCase__ : ScoreSdeVeScheduler
def __init__( self: Any , UpperCamelCase_: UNetaDModel , UpperCamelCase_: ScoreSdeVeScheduler ):
super().__init__()
self.register_modules(unet=UpperCamelCase_ , scheduler=UpperCamelCase_ )
@torch.no_grad()
def __call__( self: str , UpperCamelCase_: int = 1 , UpperCamelCase_: int = 20_00 , UpperCamelCase_: Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCamelCase_: Optional[str] = "pil" , UpperCamelCase_: bool = True , **UpperCamelCase_: List[str] , ):
__lowerCamelCase = self.unet.config.sample_size
__lowerCamelCase = (batch_size, 3, img_size, img_size)
__lowerCamelCase = self.unet
__lowerCamelCase = randn_tensor(UpperCamelCase_ , generator=UpperCamelCase_ ) * self.scheduler.init_noise_sigma
__lowerCamelCase = sample.to(self.device )
self.scheduler.set_timesteps(UpperCamelCase_ )
self.scheduler.set_sigmas(UpperCamelCase_ )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
__lowerCamelCase = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
__lowerCamelCase = self.unet(UpperCamelCase_ , UpperCamelCase_ ).sample
__lowerCamelCase = self.scheduler.step_correct(UpperCamelCase_ , UpperCamelCase_ , generator=UpperCamelCase_ ).prev_sample
# prediction step
__lowerCamelCase = model(UpperCamelCase_ , UpperCamelCase_ ).sample
__lowerCamelCase = self.scheduler.step_pred(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , generator=UpperCamelCase_ )
__lowerCamelCase, __lowerCamelCase = output.prev_sample, output.prev_sample_mean
__lowerCamelCase = sample_mean.clamp(0 , 1 )
__lowerCamelCase = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__lowerCamelCase = self.numpy_to_pil(UpperCamelCase_ )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=UpperCamelCase_ )
| 29 |
def lowerCamelCase__ ( A__ : list ):
'''simple docstring'''
for i in range(len(A__ ) - 1 , 0 , -1 ):
__lowerCamelCase = False
for j in range(A__ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
__lowerCamelCase, __lowerCamelCase = unsorted[j - 1], unsorted[j]
__lowerCamelCase = True
for j in range(A__ ):
if unsorted[j] > unsorted[j + 1]:
__lowerCamelCase, __lowerCamelCase = unsorted[j + 1], unsorted[j]
__lowerCamelCase = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ = input('Enter numbers separated by a comma:\n').strip()
UpperCAmelCase_ = [int(item) for item in user_input.split(',')]
print(f"""{cocktail_shaker_sort(unsorted) = }""")
| 29 | 1 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
__lowerCamelCase : int = "bert-base-cased"
__lowerCamelCase : List[Any] = "google/pegasus-xsum"
__lowerCamelCase : Optional[Any] = [" Sam ate lunch today.", "Sams lunch ingredients."]
__lowerCamelCase : Any = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__lowerCamelCase : List[str] = "patrickvonplaten/t5-tiny-random"
__lowerCamelCase : Tuple = "sshleifer/bart-tiny-random"
__lowerCamelCase : List[str] = "sshleifer/tiny-mbart"
__lowerCamelCase : List[Any] = "sshleifer/tiny-marian-en-de"
def _snake_case ( lowerCAmelCase : Path , lowerCAmelCase : list ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = "\n".join(__A )
Path(__A ).open("w" ).writelines(__A )
def _snake_case ( lowerCAmelCase : str ):
"""simple docstring"""
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(__A , f'{split}.source' ) , __A )
_dump_articles(os.path.join(__A , f'{split}.target' ) , __A )
return tmp_dir
class a__ ( A__ ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
],)
@slow
def __UpperCamelCase ( self : List[str],_A : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = AutoTokenizer.from_pretrained(_snake_case )
SCREAMING_SNAKE_CASE_ : Optional[Any] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
SCREAMING_SNAKE_CASE_ : Optional[Any] = max(len(tokenizer.encode(_snake_case ) ) for a in ARTICLES )
SCREAMING_SNAKE_CASE_ : Any = max(len(tokenizer.encode(_snake_case ) ) for a in SUMMARIES )
SCREAMING_SNAKE_CASE_ : str = 4
SCREAMING_SNAKE_CASE_ : List[str] = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = "ro_RO", "de_DE" # ignored for all but mbart, but never causes error.
SCREAMING_SNAKE_CASE_ : Optional[Any] = SeqaSeqDataset(
_snake_case,data_dir=_snake_case,type_path="train",max_source_length=_snake_case,max_target_length=_snake_case,src_lang=_snake_case,tgt_lang=_snake_case,)
SCREAMING_SNAKE_CASE_ : str = DataLoader(_snake_case,batch_size=2,collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(_snake_case,_snake_case )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
SCREAMING_SNAKE_CASE_ : Optional[int] = shift_tokens_right(batch["labels"],tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def __UpperCamelCase ( self : Optional[int],_A : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = AutoTokenizer.from_pretrained(_snake_case )
SCREAMING_SNAKE_CASE_ : List[Any] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
SCREAMING_SNAKE_CASE_ : Optional[Any] = max(len(tokenizer.encode(_snake_case ) ) for a in ARTICLES )
SCREAMING_SNAKE_CASE_ : List[Any] = max(len(tokenizer.encode(_snake_case ) ) for a in SUMMARIES )
SCREAMING_SNAKE_CASE_ : List[str] = 4
SCREAMING_SNAKE_CASE_ : Tuple = LegacySeqaSeqDataset(
_snake_case,data_dir=_snake_case,type_path="train",max_source_length=20,max_target_length=_snake_case,)
SCREAMING_SNAKE_CASE_ : List[Any] = DataLoader(_snake_case,batch_size=2,collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def __UpperCamelCase ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25" )
SCREAMING_SNAKE_CASE_ : List[str] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
SCREAMING_SNAKE_CASE_ : Dict = tmp_dir.joinpath("train.source" ).open().readlines()
SCREAMING_SNAKE_CASE_ : List[str] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(_snake_case,_snake_case,128,_snake_case )
SCREAMING_SNAKE_CASE_ : Dict = {x.name for x in tmp_dir.iterdir()}
SCREAMING_SNAKE_CASE_ : Tuple = {x.name for x in save_dir.iterdir()}
SCREAMING_SNAKE_CASE_ : List[Any] = save_dir.joinpath("train.source" ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(_snake_case ) < len(_snake_case )
assert len(_snake_case ) == 1
assert len(packed_examples[0] ) == sum(len(_snake_case ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE,reason="This test requires fairseq" )
def __UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
if not FAIRSEQ_AVAILABLE:
return
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = self._get_dataset(max_len=64 )
SCREAMING_SNAKE_CASE_ : Dict = 64
SCREAMING_SNAKE_CASE_ : Optional[Any] = ds.make_dynamic_sampler(_snake_case,required_batch_size_multiple=_snake_case )
SCREAMING_SNAKE_CASE_ : Optional[Any] = [len(_snake_case ) for x in batch_sampler]
assert len(set(_snake_case ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(_snake_case ) == len(_snake_case ) # no dropped or added examples
SCREAMING_SNAKE_CASE_ : Optional[Any] = DataLoader(_snake_case,batch_sampler=_snake_case,collate_fn=ds.collate_fn,num_workers=2 )
SCREAMING_SNAKE_CASE_ : Tuple = []
SCREAMING_SNAKE_CASE_ : str = []
for batch in data_loader:
SCREAMING_SNAKE_CASE_ : Optional[int] = batch["input_ids"].shape
SCREAMING_SNAKE_CASE_ : List[str] = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
SCREAMING_SNAKE_CASE_ : int = np.product(batch["input_ids"].shape )
num_src_per_batch.append(_snake_case )
if num_src_tokens > (max_tokens * 1.1):
failures.append(_snake_case )
assert num_src_per_batch[0] == max(_snake_case )
if failures:
raise AssertionError(F'too many tokens in {len(_snake_case )} batches' )
def __UpperCamelCase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Union[str, Any] = self._get_dataset(max_len=512 )
SCREAMING_SNAKE_CASE_ : List[Any] = 2
SCREAMING_SNAKE_CASE_ : int = ds.make_sortish_sampler(_snake_case,shuffle=_snake_case )
SCREAMING_SNAKE_CASE_ : List[str] = DataLoader(_snake_case,batch_size=_snake_case,collate_fn=ds.collate_fn,num_workers=2 )
SCREAMING_SNAKE_CASE_ : List[Any] = DataLoader(_snake_case,batch_size=_snake_case,collate_fn=ds.collate_fn,num_workers=2,sampler=_snake_case )
SCREAMING_SNAKE_CASE_ : Any = tokenizer.pad_token_id
def count_pad_tokens(_A : List[Any],_A : Tuple="input_ids" ):
return [batch[k].eq(_snake_case ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(_snake_case,k="labels" ) ) < sum(count_pad_tokens(_snake_case,k="labels" ) )
assert sum(count_pad_tokens(_snake_case ) ) < sum(count_pad_tokens(_snake_case ) )
assert len(_snake_case ) == len(_snake_case )
def __UpperCamelCase ( self : Optional[Any],_A : Dict=1000,_A : str=128 ):
"""simple docstring"""
if os.getenv("USE_REAL_DATA",_snake_case ):
SCREAMING_SNAKE_CASE_ : int = "examples/seq2seq/wmt_en_ro"
SCREAMING_SNAKE_CASE_ : Optional[int] = max_len * 2 * 64
if not Path(_snake_case ).joinpath("train.len" ).exists():
save_len_file(_snake_case,_snake_case )
else:
SCREAMING_SNAKE_CASE_ : Any = "examples/seq2seq/test_data/wmt_en_ro"
SCREAMING_SNAKE_CASE_ : int = max_len * 4
save_len_file(_snake_case,_snake_case )
SCREAMING_SNAKE_CASE_ : Dict = AutoTokenizer.from_pretrained(_snake_case )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = SeqaSeqDataset(
_snake_case,data_dir=_snake_case,type_path="train",max_source_length=_snake_case,max_target_length=_snake_case,n_obs=_snake_case,)
return ds, max_tokens, tokenizer
def __UpperCamelCase ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = self._get_dataset()
SCREAMING_SNAKE_CASE_ : Optional[int] = set(DistributedSortishSampler(_snake_case,256,num_replicas=2,rank=0,add_extra_examples=_snake_case ) )
SCREAMING_SNAKE_CASE_ : Dict = set(DistributedSortishSampler(_snake_case,256,num_replicas=2,rank=1,add_extra_examples=_snake_case ) )
assert idsa.intersection(_snake_case ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
],)
def __UpperCamelCase ( self : List[str],_A : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = AutoTokenizer.from_pretrained(_snake_case,use_fast=_snake_case )
if tok_name == MBART_TINY:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = SeqaSeqDataset(
_snake_case,data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ),type_path="train",max_source_length=4,max_target_length=8,src_lang="EN",tgt_lang="FR",)
SCREAMING_SNAKE_CASE_ : int = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
SCREAMING_SNAKE_CASE_ : Dict = SeqaSeqDataset(
_snake_case,data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ),type_path="train",max_source_length=4,max_target_length=8,)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(_snake_case ) == 1 if tok_name == BART_TINY else len(_snake_case ) == 0
| 18 |
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 __snake_case :
def __init__( self : List[str] , _snake_case : Union[str, Any] , _snake_case : List[str]=2 , _snake_case : Any=True , _snake_case : Any=False , _snake_case : List[str]=10 , _snake_case : Any=3 , _snake_case : Union[str, Any]=32 * 4 , _snake_case : List[Any]=32 * 6 , _snake_case : Tuple=4 , _snake_case : Dict=32 , ):
"""simple docstring"""
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = is_training
UpperCAmelCase_ = use_auxiliary_loss
UpperCAmelCase_ = num_queries
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = min_size
UpperCAmelCase_ = max_size
UpperCAmelCase_ = num_labels
UpperCAmelCase_ = mask_feature_size
def lowerCamelCase ( self : Any):
"""simple docstring"""
UpperCAmelCase_ = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
_snake_case)
UpperCAmelCase_ = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_snake_case)
UpperCAmelCase_ = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_snake_case) > 0.5
).float()
UpperCAmelCase_ = (torch.rand((self.batch_size, self.num_labels) , device=_snake_case) > 0.5).long()
UpperCAmelCase_ = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowerCamelCase ( self : 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 lowerCamelCase ( self : int):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = self.prepare_config_and_inputs()
UpperCAmelCase_ = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def lowerCamelCase ( self : str , _snake_case : List[Any] , _snake_case : List[str]):
"""simple docstring"""
UpperCAmelCase_ = output.encoder_hidden_states
UpperCAmelCase_ = output.pixel_decoder_hidden_states
UpperCAmelCase_ = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_snake_case) , len(config.backbone_config.depths))
self.parent.assertTrue(len(_snake_case) , len(config.backbone_config.depths))
self.parent.assertTrue(len(_snake_case) , config.decoder_config.decoder_layers)
def lowerCamelCase ( self : Union[str, Any] , _snake_case : List[str] , _snake_case : int , _snake_case : Optional[Any] , _snake_case : str=False):
"""simple docstring"""
with torch.no_grad():
UpperCAmelCase_ = MaskFormerModel(config=_snake_case)
model.to(_snake_case)
model.eval()
UpperCAmelCase_ = model(pixel_values=_snake_case , pixel_mask=_snake_case)
UpperCAmelCase_ = model(_snake_case , output_hidden_states=_snake_case)
# 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(_snake_case , _snake_case)
def lowerCamelCase ( self : List[Any] , _snake_case : List[Any] , _snake_case : List[Any] , _snake_case : str , _snake_case : Optional[int] , _snake_case : Union[str, Any]):
"""simple docstring"""
UpperCAmelCase_ = MaskFormerForInstanceSegmentation(config=_snake_case)
model.to(_snake_case)
model.eval()
def comm_check_on_output(_snake_case : 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():
UpperCAmelCase_ = model(pixel_values=_snake_case , pixel_mask=_snake_case)
UpperCAmelCase_ = model(_snake_case)
comm_check_on_output(_snake_case)
UpperCAmelCase_ = model(
pixel_values=_snake_case , pixel_mask=_snake_case , mask_labels=_snake_case , class_labels=_snake_case)
comm_check_on_output(_snake_case)
self.parent.assertTrue(result.loss is not None)
self.parent.assertEqual(result.loss.shape , torch.Size([1]))
@require_torch
class __snake_case ( a , a , unittest.TestCase ):
UpperCAmelCase__ : Union[str, Any] = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
UpperCAmelCase__ : Optional[Any] = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Dict = False
UpperCAmelCase__ : List[str] = False
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : Union[str, Any] = False
def lowerCamelCase ( self : List[Any]):
"""simple docstring"""
UpperCAmelCase_ = MaskFormerModelTester(self)
UpperCAmelCase_ = ConfigTester(self , config_class=_snake_case , has_text_modality=_snake_case)
def lowerCamelCase ( self : List[Any]):
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase ( self : str):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_snake_case , **_snake_case , output_hidden_states=_snake_case)
def lowerCamelCase ( self : str):
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_snake_case)
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''')
def lowerCamelCase ( self : Dict):
"""simple docstring"""
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''')
def lowerCamelCase ( self : int):
"""simple docstring"""
pass
@unittest.skip(reason='''MaskFormer is not a generative model''')
def lowerCamelCase ( self : str):
"""simple docstring"""
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''')
def lowerCamelCase ( self : int):
"""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 lowerCamelCase ( self : Any):
"""simple docstring"""
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
def lowerCamelCase ( self : str):
"""simple docstring"""
pass
def lowerCamelCase ( self : List[str]):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(_snake_case)
UpperCAmelCase_ = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ = [*signature.parameters.keys()]
UpperCAmelCase_ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _snake_case)
@slow
def lowerCamelCase ( self : Union[str, Any]):
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
UpperCAmelCase_ = MaskFormerModel.from_pretrained(_snake_case)
self.assertIsNotNone(_snake_case)
def lowerCamelCase ( self : str):
"""simple docstring"""
UpperCAmelCase_ = (self.model_tester.min_size,) * 2
UpperCAmelCase_ = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_snake_case),
'''mask_labels''': torch.randn((2, 10, *size) , device=_snake_case),
'''class_labels''': torch.zeros(2 , 10 , device=_snake_case).long(),
}
UpperCAmelCase_ = MaskFormerForInstanceSegmentation(MaskFormerConfig()).to(_snake_case)
UpperCAmelCase_ = model(**_snake_case)
self.assertTrue(outputs.loss is not None)
def lowerCamelCase ( self : Optional[Any]):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_snake_case , **_snake_case , output_hidden_states=_snake_case)
def lowerCamelCase ( self : str):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(_snake_case).to(_snake_case)
UpperCAmelCase_ = model(**_snake_case , output_attentions=_snake_case)
self.assertTrue(outputs.attentions is not None)
def lowerCamelCase ( self : int):
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
UpperCAmelCase_ = self.all_model_classes[1]
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
UpperCAmelCase_ = model_class(_snake_case)
model.to(_snake_case)
model.train()
UpperCAmelCase_ = model(_snake_case , mask_labels=_snake_case , class_labels=_snake_case).loss
loss.backward()
def lowerCamelCase ( self : Optional[int]):
"""simple docstring"""
UpperCAmelCase_ = self.all_model_classes[1]
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
UpperCAmelCase_ = True
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(_snake_case)
model.to(_snake_case)
model.train()
UpperCAmelCase_ = model(_snake_case , mask_labels=_snake_case , class_labels=_snake_case)
UpperCAmelCase_ = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
UpperCAmelCase_ = 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
UpperCAmelCase_ = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
UpperCAmelCase_ = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_snake_case)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(pixel_decoder_hidden_states.grad)
self.assertIsNotNone(transformer_decoder_hidden_states.grad)
self.assertIsNotNone(attentions.grad)
snake_case_ : Dict = 1e-4
def A () -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class __snake_case ( unittest.TestCase ):
@cached_property
def lowerCamelCase ( self : List[str]):
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''')
if is_vision_available()
else None
)
def lowerCamelCase ( self : List[Any]):
"""simple docstring"""
UpperCAmelCase_ = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''').to(_snake_case)
UpperCAmelCase_ = self.default_image_processor
UpperCAmelCase_ = prepare_img()
UpperCAmelCase_ = image_processor(_snake_case , return_tensors='''pt''').to(_snake_case)
UpperCAmelCase_ = 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(_snake_case , (1, 3, 800, 1088))
with torch.no_grad():
UpperCAmelCase_ = model(**_snake_case)
UpperCAmelCase_ = 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(_snake_case)
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _snake_case , atol=_snake_case))
UpperCAmelCase_ = 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(_snake_case)
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _snake_case , atol=_snake_case))
UpperCAmelCase_ = 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(_snake_case)
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _snake_case , atol=_snake_case))
def lowerCamelCase ( self : List[str]):
"""simple docstring"""
UpperCAmelCase_ = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''')
.to(_snake_case)
.eval()
)
UpperCAmelCase_ = self.default_image_processor
UpperCAmelCase_ = prepare_img()
UpperCAmelCase_ = image_processor(_snake_case , return_tensors='''pt''').to(_snake_case)
UpperCAmelCase_ = 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(_snake_case , (1, 3, 800, 1088))
with torch.no_grad():
UpperCAmelCase_ = model(**_snake_case)
# masks_queries_logits
UpperCAmelCase_ = 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) , )
UpperCAmelCase_ = [
[-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],
]
UpperCAmelCase_ = torch.tensor(_snake_case).to(_snake_case)
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _snake_case , atol=_snake_case))
# class_queries_logits
UpperCAmelCase_ = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
UpperCAmelCase_ = 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(_snake_case)
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _snake_case , atol=_snake_case))
def lowerCamelCase ( self : Optional[Any]):
"""simple docstring"""
UpperCAmelCase_ = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''')
.to(_snake_case)
.eval()
)
UpperCAmelCase_ = self.default_image_processor
UpperCAmelCase_ = prepare_img()
UpperCAmelCase_ = image_processor(_snake_case , return_tensors='''pt''').to(_snake_case)
UpperCAmelCase_ = 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(_snake_case , (1, 3, 800, 1088))
with torch.no_grad():
UpperCAmelCase_ = model(**_snake_case)
# masks_queries_logits
UpperCAmelCase_ = 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) , )
UpperCAmelCase_ = [[-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]]
UpperCAmelCase_ = torch.tensor(_snake_case).to(_snake_case)
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _snake_case , atol=_snake_case))
# class_queries_logits
UpperCAmelCase_ = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
UpperCAmelCase_ = 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(_snake_case)
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _snake_case , atol=_snake_case))
def lowerCamelCase ( self : Tuple):
"""simple docstring"""
UpperCAmelCase_ = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''')
.to(_snake_case)
.eval()
)
UpperCAmelCase_ = self.default_image_processor
UpperCAmelCase_ = 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''' , )
UpperCAmelCase_ = inputs['''pixel_values'''].to(_snake_case)
UpperCAmelCase_ = [el.to(_snake_case) for el in inputs['''mask_labels''']]
UpperCAmelCase_ = [el.to(_snake_case) for el in inputs['''class_labels''']]
with torch.no_grad():
UpperCAmelCase_ = model(**_snake_case)
self.assertTrue(outputs.loss is not None)
| 51 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : List[str] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[str] = [
"""PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""PegasusXForConditionalGeneration""",
"""PegasusXModel""",
"""PegasusXPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
a : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 356 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Any = {'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : int = ['''FNetTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = ['''FNetTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[int] = [
'''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FNetForMaskedLM''',
'''FNetForMultipleChoice''',
'''FNetForNextSentencePrediction''',
'''FNetForPreTraining''',
'''FNetForQuestionAnswering''',
'''FNetForSequenceClassification''',
'''FNetForTokenClassification''',
'''FNetLayer''',
'''FNetModel''',
'''FNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
a : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 79 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : int ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_SCREAMING_SNAKE_CASE =1
_SCREAMING_SNAKE_CASE =1
while repunit:
_SCREAMING_SNAKE_CASE =(10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _lowerCAmelCase ( _UpperCamelCase : int = 1_00_00_00 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(_UpperCAmelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'''{solution() = }''')
| 47 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase__ = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n"
UpperCAmelCase__ = "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"
UpperCAmelCase__ = "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n 'google_bleu': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : str) -> MetricInfo:
"""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 _lowerCamelCase ( self : Union[str, Any] , A : List[List[List[str]]] , A : List[List[str]] , A : int = 1 , A : int = 4 , ) -> Dict[str, float]:
"""simple docstring"""
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A , hypotheses=A , min_len=A , max_len=A)
}
| 339 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : List[str] = {
"configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
"tokenization_convbert": ["ConvBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Tuple = ["ConvBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Optional[int] = [
"CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConvBertForMaskedLM",
"ConvBertForMultipleChoice",
"ConvBertForQuestionAnswering",
"ConvBertForSequenceClassification",
"ConvBertForTokenClassification",
"ConvBertLayer",
"ConvBertModel",
"ConvBertPreTrainedModel",
"load_tf_weights_in_convbert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Optional[Any] = [
"TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFConvBertForMaskedLM",
"TFConvBertForMultipleChoice",
"TFConvBertForQuestionAnswering",
"TFConvBertForSequenceClassification",
"TFConvBertForTokenClassification",
"TFConvBertLayer",
"TFConvBertModel",
"TFConvBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 252 |
from __future__ import annotations
from random import random
class UpperCamelCase :
'''simple docstring'''
def __init__( self , UpperCamelCase_ = None ):
lowercase_ :Tuple = value
lowercase_ :Tuple = random()
lowercase_ :Node | None = None
lowercase_ :Node | None = None
def __repr__( self ):
from pprint import pformat
if self.left is None and self.right is None:
return f"'{self.value}: {self.prior:.5}'"
else:
return pformat(
{f"{self.value}: {self.prior:.5}": (self.left, self.right)} , indent=1 )
def __str__( self ):
lowercase_ :Optional[int] = str(self.value ) + ''' '''
lowercase_ :List[str] = str(self.left or '''''' )
lowercase_ :List[Any] = str(self.right or '''''' )
return value + left + right
def UpperCamelCase ( _a , _a ) -> tuple[Node | None, Node | None]:
'''simple docstring'''
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
lowercase_ , lowercase_ :List[Any] = split(root.left , _a )
return left, root
else:
lowercase_ , lowercase_ :Tuple = split(root.right , _a )
return root, right
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
lowercase_ :Tuple = merge(left.right , _a )
return left
else:
lowercase_ :Optional[int] = merge(_a , right.left )
return right
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
lowercase_ :str = Node(_a )
lowercase_ , lowercase_ :Dict = split(_a , _a )
return merge(merge(_a , _a ) , _a )
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
lowercase_ , lowercase_ :List[str] = split(_a , value - 1 )
lowercase_ , lowercase_ :Tuple = split(_a , _a )
return merge(_a , _a )
def UpperCamelCase ( _a ) -> None:
'''simple docstring'''
if not root: # None
return
else:
inorder(root.left )
print(root.value , end=''',''' )
inorder(root.right )
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
for arg in args.split():
if arg[0] == "+":
lowercase_ :Any = insert(_a , int(arg[1:] ) )
elif arg[0] == "-":
lowercase_ :Optional[int] = erase(_a , int(arg[1:] ) )
else:
print('''Unknown command''' )
return root
def UpperCamelCase ( ) -> None:
'''simple docstring'''
lowercase_ :List[Any] = None
print(
'''enter numbers to create a tree, + value to add value into treap, '''
'''- value to erase all nodes with value. \'q\' to quit. ''' )
lowercase_ :Optional[Any] = input()
while args != "q":
lowercase_ :Union[str, Any] = interact_treap(_a , _a )
print(_a )
lowercase_ :str = input()
print('''good by!''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 252 | 1 |
'''simple docstring'''
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : str = DownBlockaD # noqa F405
lowerCAmelCase_ : List[Any] = "down"
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ = [-0.0_2_3_2, -0.9_8_6_9, 0.8_0_5_4, -0.0_6_3_7, -0.1_6_8_8, -1.4_2_6_4, 0.4_4_7_0, -1.3_3_9_4, 0.0_9_0_4]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Optional[int] = ResnetDownsampleBlockaD # noqa F405
lowerCAmelCase_ : List[str] = "down"
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ = [0.0_7_1_0, 0.2_4_1_0, -0.7_3_2_0, -1.0_7_5_7, -1.1_3_4_3, 0.3_5_4_0, -0.0_1_3_3, -0.2_5_7_6, 0.0_9_4_8]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Union[str, Any] = AttnDownBlockaD # noqa F405
lowerCAmelCase_ : Union[str, Any] = "down"
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ = [0.0_6_3_6, 0.8_9_6_4, -0.6_2_3_4, -1.0_1_3_1, 0.0_8_4_4, 0.4_9_3_5, 0.3_4_3_7, 0.0_9_1_1, -0.2_9_5_7]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Optional[int] = CrossAttnDownBlockaD # noqa F405
lowerCAmelCase_ : str = "down"
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ , snake_case_ = super().prepare_init_args_and_inputs_for_common()
snake_case_ = 32
return init_dict, inputs_dict
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ = [0.2_2_3_8, -0.7_3_9_6, -0.2_2_5_5, -0.3_8_2_9, 0.1_9_2_5, 1.1_6_6_5, 0.0_6_0_3, -0.7_2_9_5, 0.1_9_8_3]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Tuple = SimpleCrossAttnDownBlockaD # noqa F405
lowerCAmelCase_ : List[Any] = "down"
@property
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
return super().get_dummy_input(include_encoder_hidden_states=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ , snake_case_ = super().prepare_init_args_and_inputs_for_common()
snake_case_ = 32
return init_dict, inputs_dict
@unittest.skipIf(torch_device == "mps" , "MPS result is not consistent" )
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ = [0.7_9_2_1, -0.0_9_9_2, -0.1_9_6_2, -0.7_6_9_5, -0.4_2_4_2, 0.7_8_0_4, 0.4_7_3_7, 0.2_7_6_5, 0.3_3_3_8]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Optional[Any] = SkipDownBlockaD # noqa F405
lowerCAmelCase_ : str = "down"
@property
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
return super().get_dummy_input(include_skip_sample=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ = [-0.0_8_4_5, -0.2_0_8_7, -0.2_4_6_5, 0.0_9_7_1, 0.1_9_0_0, -0.0_4_8_4, 0.2_6_6_4, 0.4_1_7_9, 0.5_0_6_9]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : List[Any] = AttnSkipDownBlockaD # noqa F405
lowerCAmelCase_ : Tuple = "down"
@property
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
return super().get_dummy_input(include_skip_sample=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ = [0.5_5_3_9, 0.1_6_0_9, 0.4_9_2_4, 0.0_5_3_7, -0.1_9_9_5, 0.4_0_5_0, 0.0_9_7_9, -0.2_7_2_1, -0.0_6_4_2]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Tuple = DownEncoderBlockaD # noqa F405
lowerCAmelCase_ : Any = "down"
@property
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
return super().get_dummy_input(include_temb=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = {
"in_channels": 32,
"out_channels": 32,
}
snake_case_ = self.dummy_input
return init_dict, inputs_dict
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ = [1.1_1_0_2, 0.5_3_0_2, 0.4_8_7_2, -0.0_0_2_3, -0.8_0_4_2, 0.0_4_8_3, -0.3_4_8_9, -0.5_6_3_2, 0.7_6_2_6]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Optional[int] = AttnDownEncoderBlockaD # noqa F405
lowerCAmelCase_ : Tuple = "down"
@property
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
return super().get_dummy_input(include_temb=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ = {
"in_channels": 32,
"out_channels": 32,
}
snake_case_ = self.dummy_input
return init_dict, inputs_dict
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ = [0.8_9_6_6, -0.1_4_8_6, 0.8_5_6_8, 0.8_1_4_1, -0.9_0_4_6, -0.1_3_4_2, -0.0_9_7_2, -0.7_4_1_7, 0.1_5_3_8]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : int = UNetMidBlockaD # noqa F405
lowerCAmelCase_ : Any = "mid"
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ = {
"in_channels": 32,
"temb_channels": 128,
}
snake_case_ = self.dummy_input
return init_dict, inputs_dict
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ = [-0.1_0_6_2, 1.7_2_4_8, 0.3_4_9_4, 1.4_5_6_9, -0.0_9_1_0, -1.2_4_2_1, -0.9_9_8_4, 0.6_7_3_6, 1.0_0_2_8]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Dict = UNetMidBlockaDCrossAttn # noqa F405
lowerCAmelCase_ : int = "mid"
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ , snake_case_ = super().prepare_init_args_and_inputs_for_common()
snake_case_ = 32
return init_dict, inputs_dict
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = [0.0_1_8_7, 2.4_2_2_0, 0.4_4_8_4, 1.1_2_0_3, -0.6_1_2_1, -1.5_1_2_2, -0.8_2_7_0, 0.7_8_5_1, 1.8_3_3_5]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Optional[Any] = UNetMidBlockaDSimpleCrossAttn # noqa F405
lowerCAmelCase_ : Optional[int] = "mid"
@property
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
return super().get_dummy_input(include_encoder_hidden_states=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ , snake_case_ = super().prepare_init_args_and_inputs_for_common()
snake_case_ = 32
return init_dict, inputs_dict
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ = [0.7_1_4_3, 1.9_9_7_4, 0.5_4_4_8, 1.3_9_7_7, 0.1_2_8_2, -1.1_2_3_7, -1.4_2_3_8, 0.5_5_3_0, 0.8_8_8_0]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Union[str, Any] = UpBlockaD # noqa F405
lowerCAmelCase_ : Union[str, Any] = "up"
@property
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ = [-0.2_0_4_1, -0.4_1_6_5, -0.3_0_2_2, 0.0_0_4_1, -0.6_6_2_8, -0.7_0_5_3, 0.1_9_2_8, -0.0_3_2_5, 0.0_5_2_3]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : List[str] = ResnetUpsampleBlockaD # noqa F405
lowerCAmelCase_ : Tuple = "up"
@property
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ = [0.2_2_8_7, 0.3_5_4_9, -0.1_3_4_6, 0.4_7_9_7, -0.1_7_1_5, -0.9_6_4_9, 0.7_3_0_5, -0.5_8_6_4, -0.6_2_4_4]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : int = CrossAttnUpBlockaD # noqa F405
lowerCAmelCase_ : Dict = "up"
@property
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ , snake_case_ = super().prepare_init_args_and_inputs_for_common()
snake_case_ = 32
return init_dict, inputs_dict
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ = [-0.1_4_0_3, -0.3_5_1_5, -0.0_4_2_0, -0.1_4_2_5, 0.3_1_6_7, 0.5_0_9_4, -0.2_1_8_1, 0.5_9_3_1, 0.5_5_8_2]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Any = SimpleCrossAttnUpBlockaD # noqa F405
lowerCAmelCase_ : Optional[Any] = "up"
@property
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase , include_encoder_hidden_states=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ , snake_case_ = super().prepare_init_args_and_inputs_for_common()
snake_case_ = 32
return init_dict, inputs_dict
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ = [0.2_6_4_5, 0.1_4_8_0, 0.0_9_0_9, 0.8_0_4_4, -0.9_7_5_8, -0.9_0_8_3, 0.0_9_9_4, -1.1_4_5_3, -0.7_4_0_2]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Dict = AttnUpBlockaD # noqa F405
lowerCAmelCase_ : Optional[Any] = "up"
@property
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase )
@unittest.skipIf(torch_device == "mps" , "MPS result is not consistent" )
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ = [0.0_9_7_9, 0.1_3_2_6, 0.0_0_2_1, 0.0_6_5_9, 0.2_2_4_9, 0.0_0_5_9, 0.1_1_3_2, 0.5_9_5_2, 0.1_0_3_3]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Any = SkipUpBlockaD # noqa F405
lowerCAmelCase_ : str = "up"
@property
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ = [-0.0_8_9_3, -0.1_2_3_4, -0.1_5_0_6, -0.0_3_3_2, 0.0_1_2_3, -0.0_2_1_1, 0.0_5_6_6, 0.0_1_4_3, 0.0_3_6_2]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : int = AttnSkipUpBlockaD # noqa F405
lowerCAmelCase_ : Union[str, Any] = "up"
@property
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ = [0.0_3_6_1, 0.0_6_1_7, 0.2_7_8_7, -0.0_3_5_0, 0.0_3_4_2, 0.3_4_2_1, -0.0_8_4_3, 0.0_9_1_3, 0.3_0_1_5]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Dict = UpDecoderBlockaD # noqa F405
lowerCAmelCase_ : Dict = "up"
@property
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
return super().get_dummy_input(include_temb=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ = {"in_channels": 32, "out_channels": 32}
snake_case_ = self.dummy_input
return init_dict, inputs_dict
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ = [0.4_4_0_4, 0.1_9_9_8, -0.9_8_8_6, -0.3_3_2_0, -0.3_1_2_8, -0.7_0_3_4, -0.6_9_5_5, -0.2_3_3_8, -0.3_1_3_7]
super().test_output(__lowerCAmelCase )
class _snake_case ( lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : List[str] = AttnUpDecoderBlockaD # noqa F405
lowerCAmelCase_ : str = "up"
@property
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
return super().get_dummy_input(include_temb=__lowerCAmelCase )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ = {"in_channels": 32, "out_channels": 32}
snake_case_ = self.dummy_input
return init_dict, inputs_dict
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ = [0.6_7_3_8, 0.4_4_9_1, 0.1_0_5_5, 1.0_7_1_0, 0.7_3_1_6, 0.3_3_3_9, 0.3_3_5_2, 0.1_0_2_3, 0.3_5_6_8]
super().test_output(__lowerCAmelCase )
| 85 |
import numpy as np
from transformers import Pipeline
def lowerCAmelCase__(__snake_case ) -> Tuple:
'''simple docstring'''
lowerCamelCase__ = np.max(__snake_case ,axis=-1 ,keepdims=__snake_case )
lowerCamelCase__ = np.exp(outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 ,keepdims=__snake_case )
class __A ( lowerCAmelCase ):
'''simple docstring'''
def __lowerCamelCase ( self , **__lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = {}
if "second_text" in kwargs:
lowerCamelCase__ = kwargs['''second_text''']
return preprocess_kwargs, {}, {}
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase=None ):
'''simple docstring'''
return self.tokenizer(__lowerCAmelCase , text_pair=__lowerCAmelCase , return_tensors=self.framework )
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
return self.model(**__lowerCAmelCase )
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = model_outputs.logits[0].numpy()
lowerCamelCase__ = softmax(__lowerCAmelCase )
lowerCamelCase__ = np.argmax(__lowerCAmelCase )
lowerCamelCase__ = self.model.config.idalabel[best_class]
lowerCamelCase__ = probabilities[best_class].item()
lowerCamelCase__ = logits.tolist()
return {"label": label, "score": score, "logits": logits}
| 209 | 0 |
"""simple docstring"""
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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################
lowerCAmelCase_ = 16
lowerCAmelCase_ = 32
def lowerCamelCase_ ( lowerCAmelCase: Accelerator , lowerCAmelCase: int = 16 )-> Tuple:
_snake_case : Any = AutoTokenizer.from_pretrained('bert-base-cased' )
_snake_case : Optional[int] = load_dataset('glue' , 'mrpc' )
def tokenize_function(lowerCAmelCase: Optional[int] ):
# max_length=None => use the model max length (it's actually the default)
_snake_case : Dict = 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
# starting with the main process first:
with accelerator.main_process_first():
_snake_case : Optional[int] = datasets.map(
__lowerCAmelCase , batched=__lowerCAmelCase , 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
_snake_case : Optional[Any] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowerCAmelCase: str ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_snake_case : Optional[Any] = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_snake_case : List[str] = 16
elif accelerator.mixed_precision != "no":
_snake_case : int = 8
else:
_snake_case : Optional[Any] = None
return tokenizer.pad(
__lowerCAmelCase , padding='longest' , max_length=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_tensors='pt' , )
# Instantiate dataloaders.
_snake_case : List[Any] = DataLoader(
tokenized_datasets['train'] , shuffle=__lowerCAmelCase , collate_fn=__lowerCAmelCase , batch_size=__lowerCAmelCase )
_snake_case : Optional[int] = DataLoader(
tokenized_datasets['validation'] , shuffle=__lowerCAmelCase , collate_fn=__lowerCAmelCase , batch_size=__lowerCAmelCase )
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
lowerCAmelCase_ = mocked_dataloaders # noqa: F811
def lowerCamelCase_ ( lowerCAmelCase: Optional[Any] , lowerCAmelCase: List[Any] )-> Tuple:
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS' , __lowerCAmelCase ) == "1":
_snake_case : int = 2
# Initialize accelerator
_snake_case : Dict = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_snake_case : List[str] = config['lr']
_snake_case : Tuple = int(config['num_epochs'] )
_snake_case : Optional[Any] = int(config['seed'] )
_snake_case : Tuple = int(config['batch_size'] )
_snake_case : List[str] = evaluate.load('glue' , 'mrpc' )
# If the batch size is too big we use gradient accumulation
_snake_case : str = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_snake_case : List[Any] = batch_size // MAX_GPU_BATCH_SIZE
_snake_case : Union[str, Any] = MAX_GPU_BATCH_SIZE
set_seed(__lowerCAmelCase )
_snake_case , _snake_case : Union[str, Any] = get_dataloaders(__lowerCAmelCase , __lowerCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_snake_case : Optional[int] = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=__lowerCAmelCase )
# 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).
_snake_case : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
_snake_case : Any = AdamW(params=model.parameters() , lr=__lowerCAmelCase )
# Instantiate scheduler
_snake_case : Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=__lowerCAmelCase , num_warmup_steps=1_00 , num_training_steps=(len(__lowerCAmelCase ) * 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.
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case : Union[str, Any] = accelerator.prepare(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Now we train the model
for epoch in range(__lowerCAmelCase ):
model.train()
for step, batch in enumerate(__lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_snake_case : List[str] = model(**__lowerCAmelCase )
_snake_case : Any = outputs.loss
_snake_case : Optional[Any] = loss / gradient_accumulation_steps
accelerator.backward(__lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
_snake_case : List[str] = 0
for step, batch in enumerate(__lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_snake_case : Optional[Any] = model(**__lowerCAmelCase )
_snake_case : List[Any] = outputs.logits.argmax(dim=-1 )
_snake_case , _snake_case : Dict = accelerator.gather((predictions, batch['labels']) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(__lowerCAmelCase ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
_snake_case : List[str] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_snake_case : List[str] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=__lowerCAmelCase , references=__lowerCAmelCase , )
_snake_case : List[str] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , __lowerCAmelCase )
def lowerCamelCase_ ( )-> Any:
_snake_case : Any = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=__lowerCAmelCase , default=__lowerCAmelCase , 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.' )
_snake_case : Any = parser.parse_args()
_snake_case : Union[str, Any] = {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
main()
| 371 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase_ ( lowerCAmelCase: List[str] , lowerCAmelCase: Dict , lowerCAmelCase: str )-> List[str]:
# Initialise PyTorch model
_snake_case : Optional[Any] = MobileBertConfig.from_json_file(lowerCAmelCase )
print(F"""Building PyTorch model from configuration: {config}""" )
_snake_case : Optional[int] = MobileBertForPreTraining(lowerCAmelCase )
# Load weights from tf checkpoint
_snake_case : Optional[int] = load_tf_weights_in_mobilebert(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCAmelCase )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--mobilebert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained MobileBERT 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."""
)
lowerCAmelCase_ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 260 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_snake_case ) , '''Tatoeba directory does not exist.''' )
class _lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
snake_case : Any = tempfile.mkdtemp()
return TatoebaConverter(save_dir=_UpperCamelCase )
@slow
def lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
self.resolver.convert_models(["heb-eng"] )
@slow
def lowerCamelCase ( self ) -> Tuple:
'''simple docstring'''
snake_case : Any = self.resolver.write_model_card("opus-mt-he-en" , dry_run=_UpperCamelCase )
assert mmeta["long_pair"] == "heb-eng"
| 203 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed
__UpperCAmelCase = logging.getLogger(__name__)
def lowercase__ ( __snake_case : List[Any]=2 , __snake_case : Union[str, Any]=3 , __snake_case : Any=16 , __snake_case : int = 10 , __snake_case : int = 2 ):
'''simple docstring'''
def get_dataset(__snake_case : Optional[Any] ):
UpperCAmelCase_ : Optional[Any] = torch.randn(batch_size * n_batches , 1 )
return TensorDataset(__snake_case , a * x + b + 0.1 * torch.randn(batch_size * n_batches , 1 ) )
UpperCAmelCase_ : Any = get_dataset(__snake_case )
UpperCAmelCase_ : str = get_dataset(__snake_case )
UpperCAmelCase_ : int = DataLoader(__snake_case , shuffle=__snake_case , batch_size=__snake_case , num_workers=4 )
UpperCAmelCase_ : int = DataLoader(__snake_case , shuffle=__snake_case , batch_size=__snake_case , num_workers=4 )
return (train_dataloader, valid_dataloader)
def lowercase__ ( __snake_case : Optional[int] , __snake_case : str , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : Any , __snake_case : Tuple=None ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = []
for epoch in range(__snake_case ):
# Train quickly
model.train()
for batch in dataloader:
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = batch
UpperCAmelCase_ : List[Any] = model(__snake_case )
UpperCAmelCase_ : int = torch.nn.functional.mse_loss(__snake_case , __snake_case )
accelerator.backward(__snake_case )
optimizer.step()
optimizer.zero_grad()
rands.append(random.random() ) # Introduce some randomness
if scheduler is not None:
scheduler.step()
return rands
class lowerCamelCase (nn.Module ):
'''simple docstring'''
def __init__( self ) -> Optional[Any]:
super().__init__()
UpperCAmelCase_ : List[Any] = nn.Parameter(torch.randn(1 ) )
UpperCAmelCase_ : Optional[int] = nn.Parameter(torch.randn(1 ) )
def __UpperCAmelCase ( self , _UpperCamelCase ) -> Optional[Any]:
return x * self.a + self.b
class lowerCamelCase (unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(4_2 )
UpperCAmelCase_ : Tuple = DummyModel()
UpperCAmelCase_ : List[str] = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = dummy_dataloaders()
UpperCAmelCase_ : Optional[int] = ProjectConfiguration(total_limit=1 , project_dir=_UpperCamelCase , automatic_checkpoint_naming=_UpperCamelCase )
# Train baseline
UpperCAmelCase_ : Dict = Accelerator(project_config=_UpperCamelCase )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : str = accelerator.prepare(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Save initial
accelerator.save_state()
# Save second state
accelerator.save_state()
self.assertEqual(len(os.listdir(accelerator.project_dir ) ) , 1 )
def __UpperCAmelCase ( self ) -> int:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(4_2 )
UpperCAmelCase_ : Optional[Any] = DummyModel()
UpperCAmelCase_ : str = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = dummy_dataloaders()
# Train baseline
UpperCAmelCase_ : Tuple = Accelerator()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = accelerator.prepare(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Save initial
UpperCAmelCase_ : Any = os.path.join(_UpperCamelCase , 'initial' )
accelerator.save_state(_UpperCamelCase )
((UpperCAmelCase_) , (UpperCAmelCase_)) : Optional[int] = model.a.item(), model.b.item()
UpperCAmelCase_ : Dict = optimizer.state_dict()
UpperCAmelCase_ : Union[str, Any] = train(3 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
((UpperCAmelCase_) , (UpperCAmelCase_)) : Union[str, Any] = model.a.item(), model.b.item()
UpperCAmelCase_ : Any = optimizer.state_dict()
# Train partially
set_seed(4_2 )
UpperCAmelCase_ : int = DummyModel()
UpperCAmelCase_ : int = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
UpperCAmelCase_ , UpperCAmelCase_ : str = dummy_dataloaders()
UpperCAmelCase_ : Optional[Any] = Accelerator()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Tuple = accelerator.prepare(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
accelerator.load_state(_UpperCamelCase )
((UpperCAmelCase_) , (UpperCAmelCase_)) : List[str] = model.a.item(), model.b.item()
UpperCAmelCase_ : Optional[Any] = optimizer.state_dict()
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
UpperCAmelCase_ : Dict = train(2 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Save everything
UpperCAmelCase_ : Union[str, Any] = os.path.join(_UpperCamelCase , 'checkpoint' )
accelerator.save_state(_UpperCamelCase )
# Load everything back in and make sure all states work
accelerator.load_state(_UpperCamelCase )
test_rands += train(1 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
((UpperCAmelCase_) , (UpperCAmelCase_)) : Optional[Any] = model.a.item(), model.b.item()
UpperCAmelCase_ : Union[str, Any] = optimizer.state_dict()
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
def __UpperCAmelCase ( self ) -> int:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(4_2 )
UpperCAmelCase_ : Tuple = DummyModel()
UpperCAmelCase_ : Optional[int] = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = dummy_dataloaders()
UpperCAmelCase_ : Any = ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase )
# Train baseline
UpperCAmelCase_ : str = Accelerator(project_dir=_UpperCamelCase , project_config=_UpperCamelCase )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Any = accelerator.prepare(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Save initial
accelerator.save_state()
((UpperCAmelCase_) , (UpperCAmelCase_)) : Optional[int] = model.a.item(), model.b.item()
UpperCAmelCase_ : Optional[int] = optimizer.state_dict()
UpperCAmelCase_ : Optional[Any] = train(3 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
((UpperCAmelCase_) , (UpperCAmelCase_)) : Tuple = model.a.item(), model.b.item()
UpperCAmelCase_ : Optional[int] = optimizer.state_dict()
# Train partially
set_seed(4_2 )
UpperCAmelCase_ : Any = DummyModel()
UpperCAmelCase_ : Any = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = dummy_dataloaders()
UpperCAmelCase_ : Tuple = ProjectConfiguration(iteration=1 , automatic_checkpoint_naming=_UpperCamelCase )
UpperCAmelCase_ : List[Any] = Accelerator(project_dir=_UpperCamelCase , project_config=_UpperCamelCase )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Any = accelerator.prepare(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
accelerator.load_state(os.path.join(_UpperCamelCase , 'checkpoints' , 'checkpoint_0' ) )
((UpperCAmelCase_) , (UpperCAmelCase_)) : str = model.a.item(), model.b.item()
UpperCAmelCase_ : List[Any] = optimizer.state_dict()
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
UpperCAmelCase_ : Union[str, Any] = train(2 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Save everything
accelerator.save_state()
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(_UpperCamelCase , 'checkpoints' , 'checkpoint_1' ) )
test_rands += train(1 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
((UpperCAmelCase_) , (UpperCAmelCase_)) : List[Any] = model.a.item(), model.b.item()
UpperCAmelCase_ : Dict = optimizer.state_dict()
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
def __UpperCAmelCase ( self ) -> Dict:
UpperCAmelCase_ : Optional[Any] = torch.tensor([1, 2, 3] )
UpperCAmelCase_ : Any = torch.tensor([2, 3, 4] )
UpperCAmelCase_ : Union[str, Any] = DummyModel()
UpperCAmelCase_ : List[str] = torch.optim.Adam(net.parameters() )
UpperCAmelCase_ : Any = Accelerator()
with self.assertRaises(_UpperCamelCase ) as ve:
accelerator.register_for_checkpointing(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
UpperCAmelCase_ : Optional[int] = str(ve.exception )
self.assertTrue('Item at index 0' in message )
self.assertTrue('Item at index 1' in message )
self.assertFalse('Item at index 2' in message )
self.assertFalse('Item at index 3' in message )
def __UpperCAmelCase ( self ) -> int:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(4_2 )
UpperCAmelCase_ : int = DummyModel()
UpperCAmelCase_ : Any = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
UpperCAmelCase_ : Dict = torch.optim.lr_scheduler.StepLR(_UpperCamelCase , step_size=1 , gamma=0.99 )
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = dummy_dataloaders()
UpperCAmelCase_ : Tuple = ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase )
# Train baseline
UpperCAmelCase_ : Tuple = Accelerator(project_dir=_UpperCamelCase , project_config=_UpperCamelCase )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Any = accelerator.prepare(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Save initial
accelerator.save_state()
UpperCAmelCase_ : Dict = scheduler.state_dict()
train(3 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
self.assertNotEqual(_UpperCamelCase , scheduler.state_dict() )
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(_UpperCamelCase , 'checkpoints' , 'checkpoint_0' ) )
self.assertEqual(_UpperCamelCase , scheduler.state_dict() )
def __UpperCAmelCase ( self ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(4_2 )
UpperCAmelCase_ : Optional[int] = DummyModel()
UpperCAmelCase_ : Dict = ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase , total_limit=2 )
# Train baseline
UpperCAmelCase_ : Optional[int] = Accelerator(project_dir=_UpperCamelCase , project_config=_UpperCamelCase )
UpperCAmelCase_ : str = accelerator.prepare(_UpperCamelCase )
# Save 3 states:
for _ in range(1_1 ):
accelerator.save_state()
self.assertTrue(not os.path.exists(os.path.join(_UpperCamelCase , 'checkpoints' , 'checkpoint_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , 'checkpoints' , 'checkpoint_9' ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase , 'checkpoints' , 'checkpoint_10' ) ) )
@require_cuda
def __UpperCAmelCase ( self ) -> str:
UpperCAmelCase_ : List[str] = ['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__ )]
execute_subprocess_async(_UpperCamelCase , env=os.environ.copy() )
if __name__ == "__main__":
__UpperCAmelCase = '/tmp/accelerate/state_checkpointing'
__UpperCAmelCase = DummyModel()
__UpperCAmelCase = torch.optim.Adam(params=model.parameters(), lr=1E-3)
__UpperCAmelCase = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
__UpperCAmelCase , __UpperCAmelCase = dummy_dataloaders()
__UpperCAmelCase = ProjectConfiguration(automatic_checkpoint_naming=True)
# Train baseline
__UpperCAmelCase = Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
if accelerator.process_index == 0:
if os.path.exists(savedir):
shutil.rmtree(savedir)
os.makedirs(savedir)
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader, scheduler
)
__UpperCAmelCase , __UpperCAmelCase = accelerator.prepare(model, optimizer)
train(3, model, train_dataloader, optimizer, accelerator, scheduler)
# Check that the intial optimizer is loaded on the GPU
for group in optimizer.param_groups:
__UpperCAmelCase = group['params'][0].device
break
assert param_device.type == accelerator.device.type
__UpperCAmelCase = model.cpu()
accelerator.wait_for_everyone()
accelerator.save_state()
accelerator.wait_for_everyone()
# Check CPU state
accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
for group in optimizer.param_groups:
__UpperCAmelCase = group['params'][0].device
break
assert (
param_device.type == torch.device('cpu').type
), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"
# Check device state
model.to(accelerator.device)
accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
for group in optimizer.param_groups:
__UpperCAmelCase = group['params'][0].device
break
assert (
param_device.type == accelerator.device.type
), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"
# Check error
with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
shutil.rmtree(savedir)
accelerator.wait_for_everyone()
| 29 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class __UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = IFPipeline
__lowerCAmelCase = TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
__lowerCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
__lowerCAmelCase = PipelineTesterMixin.required_optional_params - {"latents"}
def A (self : List[str] ):
return self._get_dummy_components()
def A (self : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Dict=0 ):
if str(_snake_case ).startswith("""mps""" ):
A = torch.manual_seed(_snake_case )
else:
A = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
A = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def A (self : List[Any] ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def A (self : Any ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def A (self : Any ):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def A (self : Optional[int] ):
self._test_save_load_local()
def A (self : List[str] ):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def A (self : Dict ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@slow
@require_torch_gpu
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def A (self : List[str] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A (self : Tuple ):
# if
A = IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa )
A = IFSuperResolutionPipeline.from_pretrained(
"""DeepFloyd/IF-II-L-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa , text_encoder=_snake_case , tokenizer=_snake_case )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to("""cuda""" )
A = pipe_a.encode_prompt("""anime turtle""" , device="""cuda""" )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
A = None
A = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(_snake_case , _snake_case , _snake_case , _snake_case )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
A = IFImgaImgPipeline(**pipe_a.components )
A = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(_snake_case , _snake_case , _snake_case , _snake_case )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
A = IFInpaintingPipeline(**pipe_a.components )
A = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(_snake_case , _snake_case , _snake_case , _snake_case )
def A (self : str , _lowerCAmelCase : Any , _lowerCAmelCase : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any] ):
# pipeline 1
_start_torch_memory_measurement()
A = torch.Generator(device="""cpu""" ).manual_seed(0 )
A = pipe_a(
prompt_embeds=_snake_case , negative_prompt_embeds=_snake_case , num_inference_steps=2 , generator=_snake_case , output_type="""np""" , )
A = output.images[0]
assert image.shape == (64, 64, 3)
A = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
A = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy""" )
assert_mean_pixel_difference(_snake_case , _snake_case )
# pipeline 2
_start_torch_memory_measurement()
A = torch.Generator(device="""cpu""" ).manual_seed(0 )
A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_snake_case )
A = pipe_a(
prompt_embeds=_snake_case , negative_prompt_embeds=_snake_case , image=_snake_case , generator=_snake_case , num_inference_steps=2 , output_type="""np""" , )
A = output.images[0]
assert image.shape == (256, 256, 3)
A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
A = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(_snake_case , _snake_case )
def A (self : str , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : List[str] ):
# pipeline 1
_start_torch_memory_measurement()
A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_snake_case )
A = torch.Generator(device="""cpu""" ).manual_seed(0 )
A = pipe_a(
prompt_embeds=_snake_case , negative_prompt_embeds=_snake_case , image=_snake_case , num_inference_steps=2 , generator=_snake_case , output_type="""np""" , )
A = output.images[0]
assert image.shape == (64, 64, 3)
A = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
A = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy""" )
assert_mean_pixel_difference(_snake_case , _snake_case )
# pipeline 2
_start_torch_memory_measurement()
A = torch.Generator(device="""cpu""" ).manual_seed(0 )
A = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(_snake_case )
A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_snake_case )
A = pipe_a(
prompt_embeds=_snake_case , negative_prompt_embeds=_snake_case , image=_snake_case , original_image=_snake_case , generator=_snake_case , num_inference_steps=2 , output_type="""np""" , )
A = output.images[0]
assert image.shape == (256, 256, 3)
A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
A = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(_snake_case , _snake_case )
def A (self : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : str , _lowerCAmelCase : int ):
# pipeline 1
_start_torch_memory_measurement()
A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_snake_case )
A = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(_snake_case )
A = torch.Generator(device="""cpu""" ).manual_seed(0 )
A = pipe_a(
prompt_embeds=_snake_case , negative_prompt_embeds=_snake_case , image=_snake_case , mask_image=_snake_case , num_inference_steps=2 , generator=_snake_case , output_type="""np""" , )
A = output.images[0]
assert image.shape == (64, 64, 3)
A = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
A = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy""" )
assert_mean_pixel_difference(_snake_case , _snake_case )
# pipeline 2
_start_torch_memory_measurement()
A = torch.Generator(device="""cpu""" ).manual_seed(0 )
A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_snake_case )
A = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(_snake_case )
A = floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(_snake_case )
A = pipe_a(
prompt_embeds=_snake_case , negative_prompt_embeds=_snake_case , image=_snake_case , mask_image=_snake_case , original_image=_snake_case , generator=_snake_case , num_inference_steps=2 , output_type="""np""" , )
A = output.images[0]
assert image.shape == (256, 256, 3)
A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
A = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(_snake_case , _snake_case )
def __a ( ) ->Optional[int]:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 351 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : List[str] = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
}
_lowerCamelCase : Dict = {
'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}
_lowerCamelCase : Optional[Any] = {
'ctrl': 256,
}
_lowerCamelCase : List[str] = {
'Pregnancy': 16_8629,
'Christianity': 7675,
'Explain': 10_6423,
'Fitness': 6_3440,
'Saving': 6_3163,
'Ask': 2_7171,
'Ass': 9_5985,
'Joke': 16_3509,
'Questions': 4_5622,
'Thoughts': 4_9605,
'Retail': 5_2342,
'Feminism': 16_4338,
'Writing': 1_1992,
'Atheism': 19_2263,
'Netflix': 4_8616,
'Computing': 3_9639,
'Opinion': 4_3213,
'Alone': 4_4967,
'Funny': 5_8917,
'Gaming': 4_0358,
'Human': 4088,
'India': 1331,
'Joker': 7_7138,
'Diet': 3_6206,
'Legal': 1_1859,
'Norman': 4939,
'Tip': 7_2689,
'Weight': 5_2343,
'Movies': 4_6273,
'Running': 2_3425,
'Science': 2090,
'Horror': 3_7793,
'Confession': 6_0572,
'Finance': 1_2250,
'Politics': 1_6360,
'Scary': 19_1985,
'Support': 1_2654,
'Technologies': 3_2516,
'Teenage': 6_6160,
'Event': 3_2769,
'Learned': 6_7460,
'Notion': 18_2770,
'Wikipedia': 3_7583,
'Books': 6665,
'Extract': 7_6050,
'Confessions': 10_2701,
'Conspiracy': 7_5932,
'Links': 6_3674,
'Narcissus': 15_0425,
'Relationship': 5_4766,
'Relationships': 13_4796,
'Reviews': 4_1671,
'News': 4256,
'Translation': 2_6820,
'multilingual': 12_8406,
}
def __a ( UpperCAmelCase ) ->Dict:
"""simple docstring"""
A = set()
A = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
A = char
A = set(UpperCAmelCase )
return pairs
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
__lowerCAmelCase = VOCAB_FILES_NAMES
__lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase = CONTROL_CODES
def __init__(self : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any]="<unk>" , **_lowerCAmelCase : Dict ):
super().__init__(unk_token=_lowerCAmelCase , **_lowerCAmelCase )
with open(_lowerCAmelCase , encoding="""utf-8""" ) as vocab_handle:
A = json.load(_lowerCAmelCase )
A = {v: k for k, v in self.encoder.items()}
with open(_lowerCAmelCase , encoding="""utf-8""" ) as merges_handle:
A = merges_handle.read().split("""\n""" )[1:-1]
A = [tuple(merge.split() ) for merge in merges]
A = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
A = {}
@property
def A (self : Tuple ):
return len(self.encoder )
def A (self : int ):
return dict(self.encoder , **self.added_tokens_encoder )
def A (self : Optional[int] , _lowerCAmelCase : Optional[int] ):
if token in self.cache:
return self.cache[token]
A = tuple(_lowerCAmelCase )
A = tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
A = get_pairs(_lowerCAmelCase )
if not pairs:
return token
while True:
A = min(_lowerCAmelCase , key=lambda _lowerCAmelCase : self.bpe_ranks.get(_lowerCAmelCase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
A , A = bigram
A = []
A = 0
while i < len(_lowerCAmelCase ):
try:
A = word.index(_lowerCAmelCase , _lowerCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
A = j
if word[i] == first and i < len(_lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
A = tuple(_lowerCAmelCase )
A = new_word
if len(_lowerCAmelCase ) == 1:
break
else:
A = get_pairs(_lowerCAmelCase )
A = """@@ """.join(_lowerCAmelCase )
A = word[:-4]
A = word
return word
def A (self : List[str] , _lowerCAmelCase : Dict ):
A = []
A = re.findall(r"""\S+\n?""" , _lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(_lowerCAmelCase ).split(""" """ ) ) )
return split_tokens
def A (self : str , _lowerCAmelCase : int ):
return self.encoder.get(_lowerCAmelCase , self.encoder.get(self.unk_token ) )
def A (self : Dict , _lowerCAmelCase : str ):
return self.decoder.get(_lowerCAmelCase , self.unk_token )
def A (self : List[str] , _lowerCAmelCase : List[Any] ):
A = """ """.join(_lowerCAmelCase ).replace("""@@ """ , """""" ).strip()
return out_string
def A (self : str , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ):
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
A = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
A = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowerCAmelCase , ensure_ascii=_lowerCAmelCase ) + """\n""" )
A = 0
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
""" Please check that the tokenizer is not corrupted!""" )
A = token_index
writer.write(""" """.join(_lowerCAmelCase ) + """\n""" )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
| 337 | 0 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : list[list[float]] ):
'''simple docstring'''
lowerCAmelCase_ : list[list[float]] = []
for data in source_data:
for i, el in enumerate(A__ ):
if len(A__ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(A__ ) )
return data_lists
def UpperCamelCase_ ( A__ : list[list[float]] , A__ : list[int] ):
'''simple docstring'''
lowerCAmelCase_ : list[list[float]] = []
for dlist, weight in zip(A__ , A__ ):
lowerCAmelCase_ : Tuple = min(A__ )
lowerCAmelCase_ : str = max(A__ )
lowerCAmelCase_ : list[float] = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
lowerCAmelCase_ : List[Any] = f'Invalid weight of {weight:f} provided'
raise ValueError(A__ )
score_lists.append(A__ )
return score_lists
def UpperCamelCase_ ( A__ : list[list[float]] ):
'''simple docstring'''
lowerCAmelCase_ : list[float] = [0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(A__ ):
lowerCAmelCase_ : List[Any] = final_scores[j] + ele
return final_scores
def UpperCamelCase_ ( A__ : list[list[float]] , A__ : list[int] ):
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = get_data(A__ )
lowerCAmelCase_ : Tuple = calculate_each_score(A__ , A__ )
lowerCAmelCase_ : Optional[int] = generate_final_scores(A__ )
# append scores to source data
for i, ele in enumerate(A__ ):
source_data[i].append(A__ )
return source_data
| 120 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : int = 1_00 ):
'''simple docstring'''
lowerCAmelCase_ : int = set()
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : str = n + 1 # maximum limit
for a in range(2 , A__ ):
for b in range(2 , A__ ):
lowerCAmelCase_ : str = a**b # calculates the current power
collect_powers.add(A__ ) # adds the result to the set
return len(A__ )
if __name__ == "__main__":
print("Number of terms ", solution(int(str(input()).strip())))
| 120 | 1 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_=[] ) -> str:
A__ = size[0] - overlap_pixels * 2
A__ = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
A__ = np.ones((size_y, size_x) , dtype=np.uinta ) * 2_55
A__ = np.pad(lowercase_ , mode="linear_ramp" , pad_width=lowercase_ , end_values=0 )
if "l" in remove_borders:
A__ = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
A__ = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
A__ = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
A__ = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> List[Any]:
return max(lowercase_ , min(lowercase_ , lowercase_ ) )
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> Union[str, Any]:
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> str:
A__ = list(lowercase_ )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
A__ = clamp_rect(lowercase_ , [0, 0] , [image_size[0], image_size[1]] )
return rect
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Optional[Any]:
A__ = Image.new("RGB" , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(lowercase_ , (original_slice, 0) )
return result
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[int]:
A__ = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
A__ = tile.crop(lowercase_ )
return tile
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
A__ = n % d
return n - divisor
class UpperCAmelCase_ ( A_ ):
def __init__( self : Optional[int] , snake_case_ : AutoencoderKL , snake_case_ : CLIPTextModel , snake_case_ : CLIPTokenizer , snake_case_ : UNetaDConditionModel , snake_case_ : DDPMScheduler , snake_case_ : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , snake_case_ : int = 350 , ) -> str:
'''simple docstring'''
super().__init__(
vae=snake_case_ , text_encoder=snake_case_ , tokenizer=snake_case_ , unet=snake_case_ , low_res_scheduler=snake_case_ , scheduler=snake_case_ , max_noise_level=snake_case_ , )
def __magic_name__ ( self : int , snake_case_ : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : List[Any] , snake_case_ : Optional[Any] , snake_case_ : Optional[Any] , snake_case_ : Any , snake_case_ : Any , **snake_case_ : str ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
A__ = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
A__ = add_overlap_rect(snake_case_ , snake_case_ , image.size )
A__ = image.crop(snake_case_ )
A__ = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
A__ = translated_slice_x - (original_image_slice / 2)
A__ = max(0 , snake_case_ )
A__ = squeeze_tile(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
A__ = to_input.size
A__ = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
A__ = super(snake_case_ , self ).__call__(image=snake_case_ , **snake_case_ ).images[0]
A__ = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
A__ = unsqueeze_tile(snake_case_ , snake_case_ )
A__ = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
A__ = []
if x == 0:
remove_borders.append("l" )
elif crop_rect[2] == image.size[0]:
remove_borders.append("r" )
if y == 0:
remove_borders.append("t" )
elif crop_rect[3] == image.size[1]:
remove_borders.append("b" )
A__ = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=snake_case_ ) , mode="L" , )
final_image.paste(
snake_case_ , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , snake_case_ )
@torch.no_grad()
def __call__( self : List[str] , snake_case_ : Union[str, List[str]] , snake_case_ : Union[PIL.Image.Image, List[PIL.Image.Image]] , snake_case_ : int = 75 , snake_case_ : float = 9.0 , snake_case_ : int = 50 , snake_case_ : Optional[Union[str, List[str]]] = None , snake_case_ : Optional[int] = 1 , snake_case_ : float = 0.0 , snake_case_ : Optional[torch.Generator] = None , snake_case_ : Optional[torch.FloatTensor] = None , snake_case_ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , snake_case_ : int = 1 , snake_case_ : int = 128 , snake_case_ : int = 32 , snake_case_ : int = 32 , ) -> List[str]:
'''simple docstring'''
A__ = Image.new("RGB" , (image.size[0] * 4, image.size[1] * 4) )
A__ = math.ceil(image.size[0] / tile_size )
A__ = math.ceil(image.size[1] / tile_size )
A__ = tcx * tcy
A__ = 0
for y in range(snake_case_ ):
for x in range(snake_case_ ):
self._process_tile(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , prompt=snake_case_ , num_inference_steps=snake_case_ , guidance_scale=snake_case_ , noise_level=snake_case_ , negative_prompt=snake_case_ , num_images_per_prompt=snake_case_ , eta=snake_case_ , generator=snake_case_ , latents=snake_case_ , )
current_count += 1
if callback is not None:
callback({"progress": current_count / total_tile_count, "image": final_image} )
return final_image
def _SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
# Run a demo
A__ = "stabilityai/stable-diffusion-x4-upscaler"
A__ = StableDiffusionTiledUpscalePipeline.from_pretrained(lowercase_ , revision="fp16" , torch_dtype=torch.floataa )
A__ = pipe.to("cuda" )
A__ = Image.open("../../docs/source/imgs/diffusers_library.jpg" )
def callback(lowercase_ ):
print(f"""progress: {obj["progress"]:.4f}""" )
obj["image"].save("diffusers_library_progress.jpg" )
A__ = pipe(image=lowercase_ , prompt="Black font, white background, vector" , noise_level=40 , callback=lowercase_ )
final_image.save("diffusers_library.jpg" )
if __name__ == "__main__":
main()
| 230 |
"""simple docstring"""
import baseaa
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> bytes:
return baseaa.aaaencode(string.encode("utf-8" ) )
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
return baseaa.aaadecode(lowercase_ ).decode("utf-8" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 230 | 1 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class lowerCAmelCase_ (datasets.BeamBasedBuilder ):
"""simple docstring"""
def __magic_name__ (self ) -> str:
"""simple docstring"""
return datasets.DatasetInfo(
features=datasets.Features({"""content""": datasets.Value("""string""" )} ) , supervised_keys=SCREAMING_SNAKE_CASE__ , )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Any:
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""examples""": get_test_dummy_examples()} )]
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(SCREAMING_SNAKE_CASE__ )
class lowerCAmelCase_ (datasets.BeamBasedBuilder ):
"""simple docstring"""
def __magic_name__ (self ) -> Optional[Any]:
"""simple docstring"""
return datasets.DatasetInfo(
features=datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} ) , supervised_keys=SCREAMING_SNAKE_CASE__ , )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Dict:
"""simple docstring"""
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""examples""": get_test_nested_examples()} )
]
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(SCREAMING_SNAKE_CASE__ )
def lowercase_ ( ):
return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""] )]
def lowercase_ ( ):
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""] )]
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
@require_beam
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
SCREAMING_SNAKE_CASE__ : Optional[Any] = DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE__ , beam_runner="""DirectRunner""" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(SCREAMING_SNAKE_CASE__ , builder.name , """default""" , """0.0.0""" , F'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"""content""": datasets.Value("""string""" )} ) )
SCREAMING_SNAKE_CASE__ : str = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , SCREAMING_SNAKE_CASE__ )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , SCREAMING_SNAKE_CASE__ )
self.assertDictEqual(dset["""train"""][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset["""train"""][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__ , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
@require_beam
def __magic_name__ (self ) -> Tuple:
"""simple docstring"""
import apache_beam as beam
SCREAMING_SNAKE_CASE__ : Optional[Any] = beam.io.parquetio.WriteToParquet
SCREAMING_SNAKE_CASE__ : int = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
SCREAMING_SNAKE_CASE__ : Dict = DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE__ , beam_runner="""DirectRunner""" )
with patch("""apache_beam.io.parquetio.WriteToParquet""" ) as write_parquet_mock:
SCREAMING_SNAKE_CASE__ : str = partial(SCREAMING_SNAKE_CASE__ , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
SCREAMING_SNAKE_CASE__ , builder.name , """default""" , """0.0.0""" , F'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
SCREAMING_SNAKE_CASE__ , builder.name , """default""" , """0.0.0""" , F'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"""content""": datasets.Value("""string""" )} ) )
SCREAMING_SNAKE_CASE__ : Optional[int] = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , SCREAMING_SNAKE_CASE__ )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , SCREAMING_SNAKE_CASE__ )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["""train"""]["""content"""] ) , sorted(["""foo""", """bar""", """foobar"""] ) )
self.assertTrue(
os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__ , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
@require_beam
def __magic_name__ (self ) -> Tuple:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_cache_dir:
SCREAMING_SNAKE_CASE__ : Tuple = DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE__ )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def __magic_name__ (self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
SCREAMING_SNAKE_CASE__ : Dict = NestedBeamDataset(cache_dir=SCREAMING_SNAKE_CASE__ , beam_runner="""DirectRunner""" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(SCREAMING_SNAKE_CASE__ , builder.name , """default""" , """0.0.0""" , F'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} ) )
SCREAMING_SNAKE_CASE__ : Any = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , SCREAMING_SNAKE_CASE__ )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , SCREAMING_SNAKE_CASE__ )
self.assertDictEqual(dset["""train"""][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset["""train"""][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__ , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
| 25 |
"""simple docstring"""
def lowercase_ ( _snake_case ):
SCREAMING_SNAKE_CASE__ : Optional[int] = [1]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = 0, 0, 0
SCREAMING_SNAKE_CASE__ : List[str] = ugly_nums[ia] * 2
SCREAMING_SNAKE_CASE__ : int = ugly_nums[ia] * 3
SCREAMING_SNAKE_CASE__ : Any = ugly_nums[ia] * 5
for _ in range(1 ,_snake_case ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = min(_snake_case ,_snake_case ,_snake_case )
ugly_nums.append(_snake_case )
if next_num == next_a:
ia += 1
SCREAMING_SNAKE_CASE__ : Optional[int] = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
SCREAMING_SNAKE_CASE__ : List[str] = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
SCREAMING_SNAKE_CASE__ : Tuple = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f"""{ugly_numbers(2_0_0) = }""")
| 25 | 1 |
"""simple docstring"""
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Dict:
"""simple docstring"""
lowerCAmelCase__ :Optional[int] = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ :Optional[Any] = FlaxAutoModelForSeqaSeqLM.from_config(config=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ :Tuple = checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ :Union[str, Any] = 'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']
if config.model_type == "t5":
lowerCAmelCase__ :Optional[Any] = 'SelfAttention'
if config.model_type == "longt5" and config.encoder_attention_type == "local":
lowerCAmelCase__ :Union[str, Any] = 'LocalSelfAttention'
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCAmelCase__ :List[str] = 'TransientGlobalSelfAttention'
else:
raise ValueError(
'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
' attribute with a value from [\'local\', \'transient-global].' )
# Encoder
for layer_index in range(config.num_layers ):
lowerCAmelCase__ :Dict = F"layers_{str(_SCREAMING_SNAKE_CASE )}"
# Self-Attention
lowerCAmelCase__ :Dict = tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
lowerCAmelCase__ :Optional[Any] = tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
lowerCAmelCase__ :Optional[Any] = tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
lowerCAmelCase__ :Any = tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCAmelCase__ :Tuple = tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']
# Layer Normalization
lowerCAmelCase__ :List[str] = tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']
if split_mlp_wi:
lowerCAmelCase__ :List[str] = tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
lowerCAmelCase__ :Any = tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
else:
lowerCAmelCase__ :List[Any] = tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']
lowerCAmelCase__ :Optional[int] = tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']
# Layer Normalization
lowerCAmelCase__ :Optional[Any] = tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']
# Assigning
lowerCAmelCase__ :Dict = flax_model.params['encoder']['block'][str(_SCREAMING_SNAKE_CASE )]['layer']
lowerCAmelCase__ :str = tax_attention_key
lowerCAmelCase__ :str = tax_attention_out
lowerCAmelCase__ :Any = tax_attention_query
lowerCAmelCase__ :Tuple = tax_attention_value
lowerCAmelCase__ :List[Any] = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCAmelCase__ :List[str] = tax_global_layer_norm
if split_mlp_wi:
lowerCAmelCase__ :Optional[int] = tax_mlp_wi_a
lowerCAmelCase__ :List[str] = tax_mlp_wi_a
else:
lowerCAmelCase__ :str = tax_mlp_wi
lowerCAmelCase__ :List[Any] = tax_mlp_wo
lowerCAmelCase__ :Union[str, Any] = tax_mlp_layer_norm
lowerCAmelCase__ :int = flax_model_encoder_layer_block
# Only for layer 0:
lowerCAmelCase__ :Dict = tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
lowerCAmelCase__ :Dict = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCAmelCase__ :Tuple = tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
lowerCAmelCase__ :Optional[Any] = tax_encoder_global_rel_embedding
# Assigning
lowerCAmelCase__ :Dict = tax_model['target']['encoder']['encoder_norm']['scale']
lowerCAmelCase__ :str = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
lowerCAmelCase__ :List[str] = F"layers_{str(_SCREAMING_SNAKE_CASE )}"
# Self-Attention
lowerCAmelCase__ :Optional[Any] = tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
lowerCAmelCase__ :Tuple = tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
lowerCAmelCase__ :Optional[Any] = tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
lowerCAmelCase__ :Union[str, Any] = tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']
# Layer Normalization
lowerCAmelCase__ :str = tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
'scale'
]
# Encoder-Decoder-Attention
lowerCAmelCase__ :Optional[int] = tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
lowerCAmelCase__ :Any = tax_enc_dec_attention_module['key']['kernel']
lowerCAmelCase__ :Union[str, Any] = tax_enc_dec_attention_module['out']['kernel']
lowerCAmelCase__ :Union[str, Any] = tax_enc_dec_attention_module['query']['kernel']
lowerCAmelCase__ :Tuple = tax_enc_dec_attention_module['value']['kernel']
# Layer Normalization
lowerCAmelCase__ :List[Any] = tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']
# MLP
if split_mlp_wi:
lowerCAmelCase__ :Optional[Any] = tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
lowerCAmelCase__ :Tuple = tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
else:
lowerCAmelCase__ :int = tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']
lowerCAmelCase__ :Union[str, Any] = tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']
# Layer Normalization
lowerCAmelCase__ :Dict = tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']
# Assigning
lowerCAmelCase__ :List[Any] = flax_model.params['decoder']['block'][str(_SCREAMING_SNAKE_CASE )]['layer']
lowerCAmelCase__ :Dict = tax_attention_key
lowerCAmelCase__ :List[Any] = tax_attention_out
lowerCAmelCase__ :Optional[Any] = tax_attention_query
lowerCAmelCase__ :Optional[int] = tax_attention_value
lowerCAmelCase__ :Optional[int] = tax_pre_attention_layer_norm
lowerCAmelCase__ :Tuple = tax_enc_dec_attention_key
lowerCAmelCase__ :Optional[Any] = tax_enc_dec_attention_out
lowerCAmelCase__ :Dict = tax_enc_dec_attention_query
lowerCAmelCase__ :List[str] = tax_enc_dec_attention_value
lowerCAmelCase__ :Optional[Any] = tax_cross_layer_norm
if split_mlp_wi:
lowerCAmelCase__ :Tuple = tax_mlp_wi_a
lowerCAmelCase__ :Optional[int] = tax_mlp_wi_a
else:
lowerCAmelCase__ :Union[str, Any] = tax_mlp_wi
lowerCAmelCase__ :List[str] = tax_mlp_wo
lowerCAmelCase__ :Optional[Any] = txa_mlp_layer_norm
lowerCAmelCase__ :Any = flax_model_decoder_layer_block
# Decoder Normalization
lowerCAmelCase__ :Union[str, Any] = tax_model['target']['decoder']['decoder_norm']['scale']
lowerCAmelCase__ :Any = txa_decoder_norm
# Only for layer 0:
lowerCAmelCase__ :Optional[int] = tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
lowerCAmelCase__ :Optional[Any] = tax_decoder_rel_embedding
# Token Embeddings
lowerCAmelCase__ :str = tax_model['target']['token_embedder']['embedding']
lowerCAmelCase__ :Optional[int] = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
lowerCAmelCase__ :Optional[int] = tax_model['target']['decoder']['logits_dense']['kernel']
flax_model.save_pretrained(_SCREAMING_SNAKE_CASE )
print('T5X Model was sucessfully converted!' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
)
parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
parser.add_argument(
"""--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
)
__a = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 356 |
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
__A = direct_transformers_import(PATH_TO_TRANSFORMERS)
__A = transformers.models.auto.configuration_auto.CONFIG_MAPPING
__A = {
# used to compute the property `self.chunk_length`
"""EncodecConfig""": ["""overlap"""],
# used as `self.bert_model = BertModel(config, ...)`
"""DPRConfig""": True,
# not used in modeling files, but it's an important information
"""FSMTConfig""": ["""langs"""],
# used internally in the configuration class file
"""GPTNeoConfig""": ["""attention_types"""],
# used internally in the configuration class file
"""EsmConfig""": ["""is_folding_model"""],
# used during training (despite we don't have training script for these models yet)
"""Mask2FormerConfig""": ["""ignore_value"""],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
"""OneFormerConfig""": ["""ignore_value""", """norm"""],
# used during preprocessing and collation, see `collating_graphormer.py`
"""GraphormerConfig""": ["""spatial_pos_max"""],
# used internally in the configuration class file
"""T5Config""": ["""feed_forward_proj"""],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
"""MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
"""UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
# used internally in the configuration class file
"""LongT5Config""": ["""feed_forward_proj"""],
# used internally in the configuration class file
"""SwitchTransformersConfig""": ["""feed_forward_proj"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""BioGptConfig""": ["""layer_norm_eps"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""GLPNConfig""": ["""layer_norm_eps"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""SegformerConfig""": ["""layer_norm_eps"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""CvtConfig""": ["""layer_norm_eps"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""PerceiverConfig""": ["""layer_norm_eps"""],
# used internally to calculate the feature size
"""InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
# used internally to calculate the feature size
"""TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
# used internally to calculate the feature size
"""AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
# used internally to calculate `mlp_dim`
"""SamVisionConfig""": ["""mlp_ratio"""],
# For (head) training, but so far not implemented
"""ClapAudioConfig""": ["""num_classes"""],
# Not used, but providing useful information to users
"""SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
"""CLIPSegConfig""": True,
"""DeformableDetrConfig""": True,
"""DetaConfig""": True,
"""DinatConfig""": True,
"""DonutSwinConfig""": True,
"""EfficientFormerConfig""": True,
"""FSMTConfig""": True,
"""JukeboxConfig""": True,
"""LayoutLMv2Config""": True,
"""MaskFormerSwinConfig""": True,
"""MT5Config""": True,
"""NatConfig""": True,
"""OneFormerConfig""": True,
"""PerceiverConfig""": True,
"""RagConfig""": True,
"""SpeechT5Config""": True,
"""SwinConfig""": True,
"""Swin2SRConfig""": True,
"""Swinv2Config""": True,
"""SwitchTransformersConfig""": True,
"""TableTransformerConfig""": True,
"""TapasConfig""": True,
"""TransfoXLConfig""": True,
"""UniSpeechConfig""": True,
"""UniSpeechSatConfig""": True,
"""WavLMConfig""": True,
"""WhisperConfig""": True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
"""JukeboxPriorConfig""": True,
# TODO: @Younes (for `is_decoder`)
"""Pix2StructTextConfig""": True,
}
)
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
"""simple docstring"""
lowerCAmelCase__ :List[str] = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
F"config.{attribute}" in modeling_source
or F"getattr(config, \"{attribute}\"" in modeling_source
or F"getattr(self.config, \"{attribute}\"" in modeling_source
):
lowerCAmelCase__ :List[str] = True
# Deal with multi-line cases
elif (
re.search(
rF"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"" , _SCREAMING_SNAKE_CASE , )
is not None
):
lowerCAmelCase__ :int = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
lowerCAmelCase__ :Any = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
lowerCAmelCase__ :Union[str, Any] = [
'bos_index',
'eos_index',
'pad_index',
'unk_index',
'mask_index',
'image_size',
'use_cache',
'out_features',
'out_indices',
]
lowerCAmelCase__ :Union[str, Any] = ['encoder_no_repeat_ngram_size']
# Special cases to be allowed
lowerCAmelCase__ :Any = True
if not attribute_used:
lowerCAmelCase__ :List[Any] = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
lowerCAmelCase__ :List[str] = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
lowerCAmelCase__ :Tuple = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
lowerCAmelCase__ :Optional[Any] = True
elif attribute.endswith('_token_id' ):
lowerCAmelCase__ :List[Any] = True
# configuration class specific cases
if not case_allowed:
lowerCAmelCase__ :List[str] = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
lowerCAmelCase__ :List[Any] = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def __A (_SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ :List[Any] = dict(inspect.signature(config_class.__init__ ).parameters )
lowerCAmelCase__ :List[Any] = [x for x in list(signature.keys() ) if x not in ['self', 'kwargs']]
lowerCAmelCase__ :List[Any] = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
lowerCAmelCase__ :Optional[Any] = {}
if len(config_class.attribute_map ) > 0:
lowerCAmelCase__ :Optional[int] = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
lowerCAmelCase__ :str = inspect.getsourcefile(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ :List[Any] = os.path.dirname(_SCREAMING_SNAKE_CASE )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
lowerCAmelCase__ :Dict = [os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for fn in os.listdir(_SCREAMING_SNAKE_CASE ) if fn.startswith('modeling_' )]
# Get the source code strings
lowerCAmelCase__ :Tuple = []
for path in modeling_paths:
if os.path.isfile(_SCREAMING_SNAKE_CASE ):
with open(_SCREAMING_SNAKE_CASE ) as fp:
modeling_sources.append(fp.read() )
lowerCAmelCase__ :Any = []
for config_param, default_value in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
# `attributes` here is all the variant names for `config_param`
lowerCAmelCase__ :Optional[int] = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
unused_attributes.append(attributes[0] )
return sorted(_SCREAMING_SNAKE_CASE )
def __A () ->List[Any]:
"""simple docstring"""
lowerCAmelCase__ :Optional[int] = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
lowerCAmelCase__ :List[str] = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda _SCREAMING_SNAKE_CASE : inspect.isclass(_SCREAMING_SNAKE_CASE )
and issubclass(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and inspect.getmodule(_SCREAMING_SNAKE_CASE ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
lowerCAmelCase__ :Union[str, Any] = check_config_attributes_being_used(_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 0:
lowerCAmelCase__ :int = unused_attributes
if len(_SCREAMING_SNAKE_CASE ) > 0:
lowerCAmelCase__ :Any = 'The following configuration classes contain unused attributes in the corresponding modeling files:\n'
for name, attributes in configs_with_unused_attributes.items():
error += F"{name}: {attributes}\n"
raise ValueError(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
check_config_attributes()
| 254 | 0 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__ , ):
lowercase : Optional[Any] = parent
lowercase : Any = 13
lowercase : List[str] = 7
lowercase : Union[str, Any] = True
lowercase : int = True
lowercase : int = True
lowercase : int = 99
lowercase : Optional[Any] = 32
lowercase : List[Any] = 2
lowercase : Any = 4
lowercase : List[str] = 37
lowercase : Any = '''gelu'''
lowercase : Optional[Any] = 0.1
lowercase : Optional[Any] = 0.1
lowercase : List[str] = 512
lowercase : List[str] = 16
lowercase : List[Any] = 2
lowercase : Tuple = 0.02
lowercase : List[Any] = 3
lowercase : str = 4
lowercase : Dict = None
def __lowerCamelCase ( self ):
lowercase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Union[str, Any] = None
if self.use_input_mask:
lowercase : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowercase : Any = None
lowercase : Tuple = None
lowercase : List[Any] = None
if self.use_labels:
lowercase : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase : int = ids_tensor([self.batch_size] , self.num_choices )
lowercase : Dict = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , 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, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCamelCase ( self ):
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) : str = self.prepare_config_and_inputs()
lowercase : List[str] = True
lowercase : str = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[int] = TFEsmModel(config=SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
lowercase : Optional[Any] = model(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = [input_ids, input_mask]
lowercase : List[str] = model(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = model(SCREAMING_SNAKE_CASE__ )
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__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ):
lowercase : List[str] = True
lowercase : List[Any] = TFEsmModel(config=SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''encoder_hidden_states''': encoder_hidden_states,
'''encoder_attention_mask''': encoder_attention_mask,
}
lowercase : Optional[Any] = model(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = [input_ids, input_mask]
lowercase : Optional[Any] = model(SCREAMING_SNAKE_CASE__ , encoder_hidden_states=SCREAMING_SNAKE_CASE__ )
# Also check the case where encoder outputs are not passed
lowercase : Optional[int] = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ )
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__ ):
lowercase : Optional[Any] = TFEsmForMaskedLM(config=SCREAMING_SNAKE_CASE__ )
lowercase : int = model([input_ids, input_mask] )
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__ ):
lowercase : Optional[int] = self.num_labels
lowercase : List[str] = TFEsmForTokenClassification(config=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
lowercase : List[Any] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) : List[str] = config_and_inputs
lowercase : List[str] = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __SCREAMING_SNAKE_CASE ( A__ , A__ , unittest.TestCase ):
A : Any = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
A : List[str] = (
{
'feature-extraction': TFEsmModel,
'fill-mask': TFEsmForMaskedLM,
'text-classification': TFEsmForSequenceClassification,
'token-classification': TFEsmForTokenClassification,
'zero-shot': TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
A : Optional[Any] = False
A : Optional[Any] = False
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = TFEsmModelTester(self )
lowercase : int = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , hidden_size=37 )
def __lowerCamelCase ( self ):
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ):
lowercase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : int = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__ )
@slow
def __lowerCamelCase ( self ):
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : str = TFEsmModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
@unittest.skip('''Protein models do not support embedding resizing.''' )
def __lowerCamelCase ( self ):
pass
@unittest.skip('''Protein models do not support embedding resizing.''' )
def __lowerCamelCase ( self ):
pass
def __lowerCamelCase ( self ):
lowercase , lowercase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : Tuple = model_class(SCREAMING_SNAKE_CASE__ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
lowercase : Optional[Any] = model.get_bias()
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
for k, v in name.items():
assert isinstance(SCREAMING_SNAKE_CASE__ , tf.Variable )
else:
lowercase : Optional[Any] = model.get_output_embeddings()
assert x is None
lowercase : Tuple = model.get_bias()
assert name is None
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )
lowercase : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowercase : int = model(SCREAMING_SNAKE_CASE__ )[0]
lowercase : int = [1, 6, 33]
self.assertEqual(list(output.numpy().shape ) , SCREAMING_SNAKE_CASE__ )
# compare the actual values for a slice.
lowercase : Optional[Any] = tf.constant(
[
[
[8.921518, -10.589814, -6.4671307],
[-6.3967156, -13.911377, -1.1211915],
[-7.781247, -13.951557, -3.740592],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-2 ) )
@slow
def __lowerCamelCase ( self ):
lowercase : Dict = TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )
lowercase : Any = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowercase : List[Any] = model(SCREAMING_SNAKE_CASE__ )[0]
# compare the actual values for a slice.
lowercase : int = tf.constant(
[
[
[0.14443092, 0.54125327, 0.3247739],
[0.30340484, 0.00526676, 0.31077722],
[0.32278043, -0.24987096, 0.3414628],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 337 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__=0 ): # a graph with Node 0,1,...,N-1
lowercase : List[Any] = n
lowercase : List[Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # adjacency matrix for weight
lowercase : Union[str, Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # dp[i][j] stores minimum distance from i to j
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : int = w
def __lowerCamelCase ( self ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
lowercase : Any = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return self.dp[u][v]
if __name__ == "__main__":
__a = 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)
| 337 | 1 |
def _lowerCamelCase( lowercase__ = 1_0_0_0 ) -> int:
'''simple docstring'''
return sum(e for e in range(3 , lowercase__ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F'{solution() = }')
| 304 |
lowerCAmelCase = [
9_9_9,
8_0_0,
7_9_9,
6_0_0,
5_9_9,
5_0_0,
4_0_0,
3_9_9,
3_7_7,
3_5_5,
3_3_3,
3_1_1,
2_8_8,
2_6_6,
2_4_4,
2_2_2,
2_0_0,
1_9_9,
1_7_7,
1_5_5,
1_3_3,
1_1_1,
8_8,
6_6,
4_4,
2_2,
0,
]
lowerCAmelCase = [
9_9_9,
9_7_6,
9_5_2,
9_2_8,
9_0_5,
8_8_2,
8_5_8,
8_5_7,
8_1_0,
7_6_2,
7_1_5,
7_1_4,
5_7_2,
4_2_9,
4_2_8,
2_8_6,
2_8_5,
2_3_8,
1_9_0,
1_4_3,
1_4_2,
1_1_8,
9_5,
7_1,
4_7,
2_4,
0,
]
lowerCAmelCase = [
9_9_9,
9_8_8,
9_7_7,
9_6_6,
9_5_5,
9_4_4,
9_3_3,
9_2_2,
9_1_1,
9_0_0,
8_9_9,
8_7_9,
8_5_9,
8_4_0,
8_2_0,
8_0_0,
7_9_9,
7_6_6,
7_3_3,
7_0_0,
6_9_9,
6_5_0,
6_0_0,
5_9_9,
5_0_0,
4_9_9,
4_0_0,
3_9_9,
3_5_0,
3_0_0,
2_9_9,
2_6_6,
2_3_3,
2_0_0,
1_9_9,
1_7_9,
1_5_9,
1_4_0,
1_2_0,
1_0_0,
9_9,
8_8,
7_7,
6_6,
5_5,
4_4,
3_3,
2_2,
1_1,
0,
]
lowerCAmelCase = [
9_9_9,
9_9_5,
9_9_2,
9_8_9,
9_8_5,
9_8_1,
9_7_8,
9_7_5,
9_7_1,
9_6_7,
9_6_4,
9_6_1,
9_5_7,
9_5_6,
9_5_1,
9_4_7,
9_4_2,
9_3_7,
9_3_3,
9_2_8,
9_2_3,
9_1_9,
9_1_4,
9_1_3,
9_0_8,
9_0_3,
8_9_7,
8_9_2,
8_8_7,
8_8_1,
8_7_6,
8_7_1,
8_7_0,
8_6_4,
8_5_8,
8_5_2,
8_4_6,
8_4_0,
8_3_4,
8_2_8,
8_2_7,
8_2_0,
8_1_3,
8_0_6,
7_9_9,
7_9_2,
7_8_5,
7_8_4,
7_7_7,
7_7_0,
7_6_3,
7_5_6,
7_4_9,
7_4_2,
7_4_1,
7_3_3,
7_2_4,
7_1_6,
7_0_7,
6_9_9,
6_9_8,
6_8_8,
6_7_7,
6_6_6,
6_5_6,
6_5_5,
6_4_5,
6_3_4,
6_2_3,
6_1_3,
6_1_2,
5_9_8,
5_8_4,
5_7_0,
5_6_9,
5_5_5,
5_4_1,
5_2_7,
5_2_6,
5_0_5,
4_8_4,
4_8_3,
4_6_2,
4_4_0,
4_3_9,
3_9_6,
3_9_5,
3_5_2,
3_5_1,
3_0_8,
3_0_7,
2_6_4,
2_6_3,
2_2_0,
2_1_9,
1_7_6,
1_3_2,
8_8,
4_4,
0,
]
lowerCAmelCase = [
9_9_9,
9_9_7,
9_9_5,
9_9_2,
9_9_0,
9_8_8,
9_8_6,
9_8_4,
9_8_1,
9_7_9,
9_7_7,
9_7_5,
9_7_2,
9_7_0,
9_6_8,
9_6_6,
9_6_4,
9_6_1,
9_5_9,
9_5_7,
9_5_6,
9_5_4,
9_5_1,
9_4_9,
9_4_6,
9_4_4,
9_4_1,
9_3_9,
9_3_6,
9_3_4,
9_3_1,
9_2_9,
9_2_6,
9_2_4,
9_2_1,
9_1_9,
9_1_6,
9_1_4,
9_1_3,
9_1_0,
9_0_7,
9_0_5,
9_0_2,
8_9_9,
8_9_6,
8_9_3,
8_9_1,
8_8_8,
8_8_5,
8_8_2,
8_7_9,
8_7_7,
8_7_4,
8_7_1,
8_7_0,
8_6_7,
8_6_4,
8_6_1,
8_5_8,
8_5_5,
8_5_2,
8_4_9,
8_4_6,
8_4_3,
8_4_0,
8_3_7,
8_3_4,
8_3_1,
8_2_8,
8_2_7,
8_2_4,
8_2_1,
8_1_7,
8_1_4,
8_1_1,
8_0_8,
8_0_4,
8_0_1,
7_9_8,
7_9_5,
7_9_1,
7_8_8,
7_8_5,
7_8_4,
7_8_0,
7_7_7,
7_7_4,
7_7_0,
7_6_6,
7_6_3,
7_6_0,
7_5_6,
7_5_2,
7_4_9,
7_4_6,
7_4_2,
7_4_1,
7_3_7,
7_3_3,
7_3_0,
7_2_6,
7_2_2,
7_1_8,
7_1_4,
7_1_0,
7_0_7,
7_0_3,
6_9_9,
6_9_8,
6_9_4,
6_9_0,
6_8_5,
6_8_1,
6_7_7,
6_7_3,
6_6_9,
6_6_4,
6_6_0,
6_5_6,
6_5_5,
6_5_0,
6_4_6,
6_4_1,
6_3_6,
6_3_2,
6_2_7,
6_2_2,
6_1_8,
6_1_3,
6_1_2,
6_0_7,
6_0_2,
5_9_6,
5_9_1,
5_8_6,
5_8_0,
5_7_5,
5_7_0,
5_6_9,
5_6_3,
5_5_7,
5_5_1,
5_4_5,
5_3_9,
5_3_3,
5_2_7,
5_2_6,
5_1_9,
5_1_2,
5_0_5,
4_9_8,
4_9_1,
4_8_4,
4_8_3,
4_7_4,
4_6_6,
4_5_7,
4_4_9,
4_4_0,
4_3_9,
4_2_8,
4_1_8,
4_0_7,
3_9_6,
3_9_5,
3_8_1,
3_6_6,
3_5_2,
3_5_1,
3_3_0,
3_0_8,
3_0_7,
2_8_6,
2_6_4,
2_6_3,
2_4_2,
2_2_0,
2_1_9,
1_7_6,
1_7_5,
1_3_2,
1_3_1,
8_8,
4_4,
0,
]
lowerCAmelCase = [
9_9_9,
9_9_1,
9_8_2,
9_7_4,
9_6_6,
9_5_8,
9_5_0,
9_4_1,
9_3_3,
9_2_5,
9_1_6,
9_0_8,
9_0_0,
8_9_9,
8_7_4,
8_5_0,
8_2_5,
8_0_0,
7_9_9,
7_0_0,
6_0_0,
5_0_0,
4_0_0,
3_0_0,
2_0_0,
1_0_0,
0,
]
lowerCAmelCase = [
9_9_9,
9_9_2,
9_8_5,
9_7_8,
9_7_1,
9_6_4,
9_5_7,
9_4_9,
9_4_2,
9_3_5,
9_2_8,
9_2_1,
9_1_4,
9_0_7,
9_0_0,
8_9_9,
8_7_9,
8_5_9,
8_4_0,
8_2_0,
8_0_0,
7_9_9,
7_6_6,
7_3_3,
7_0_0,
6_9_9,
6_5_0,
6_0_0,
5_9_9,
5_0_0,
4_9_9,
4_0_0,
3_9_9,
3_0_0,
2_9_9,
2_0_0,
1_9_9,
1_0_0,
9_9,
0,
]
lowerCAmelCase = [
9_9_9,
9_9_6,
9_9_2,
9_8_9,
9_8_5,
9_8_2,
9_7_9,
9_7_5,
9_7_2,
9_6_8,
9_6_5,
9_6_1,
9_5_8,
9_5_5,
9_5_1,
9_4_8,
9_4_4,
9_4_1,
9_3_8,
9_3_4,
9_3_1,
9_2_7,
9_2_4,
9_2_0,
9_1_7,
9_1_4,
9_1_0,
9_0_7,
9_0_3,
9_0_0,
8_9_9,
8_9_1,
8_8_4,
8_7_6,
8_6_9,
8_6_1,
8_5_3,
8_4_6,
8_3_8,
8_3_0,
8_2_3,
8_1_5,
8_0_8,
8_0_0,
7_9_9,
7_8_8,
7_7_7,
7_6_6,
7_5_5,
7_4_4,
7_3_3,
7_2_2,
7_1_1,
7_0_0,
6_9_9,
6_8_8,
6_7_7,
6_6_6,
6_5_5,
6_4_4,
6_3_3,
6_2_2,
6_1_1,
6_0_0,
5_9_9,
5_8_5,
5_7_1,
5_5_7,
5_4_2,
5_2_8,
5_1_4,
5_0_0,
4_9_9,
4_8_5,
4_7_1,
4_5_7,
4_4_2,
4_2_8,
4_1_4,
4_0_0,
3_9_9,
3_7_9,
3_5_9,
3_4_0,
3_2_0,
3_0_0,
2_9_9,
2_7_9,
2_5_9,
2_4_0,
2_2_0,
2_0_0,
1_9_9,
1_6_6,
1_3_3,
1_0_0,
9_9,
6_6,
3_3,
0,
]
| 304 | 1 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class lowerCamelCase (_snake_case ):
'''simple docstring'''
_snake_case : int = 0
_snake_case : bool = False
_snake_case : float = 3.0
class lowerCamelCase (unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self ) -> Dict:
# If no defaults are changed, `to_kwargs` returns an empty dict.
self.assertDictEqual(MockClass().to_kwargs() , {} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() , {'a': 2} )
self.assertDictEqual(MockClass(a=2 , b=_UpperCamelCase ).to_kwargs() , {'a': 2, 'b': True} )
self.assertDictEqual(MockClass(a=2 , c=2.25 ).to_kwargs() , {'a': 2, 'c': 2.25} )
@require_cuda
def __UpperCAmelCase ( self ) -> Optional[Any]:
# If no defaults are changed, `to_kwargs` returns an empty dict.
UpperCAmelCase_ : Optional[Any] = GradScalerKwargs(init_scale=1_0_2_4 , growth_factor=2 )
AcceleratorState._reset_state()
UpperCAmelCase_ : Union[str, Any] = Accelerator(mixed_precision='fp16' , kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
UpperCAmelCase_ : Any = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale , 10_24.0 )
self.assertEqual(scaler._growth_factor , 2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor , 0.5 )
self.assertEqual(scaler._growth_interval , 2_0_0_0 )
self.assertEqual(scaler._enabled , _UpperCamelCase )
@require_multi_gpu
def __UpperCAmelCase ( self ) -> str:
UpperCAmelCase_ : int = ['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__ )]
execute_subprocess_async(_UpperCamelCase , env=os.environ.copy() )
if __name__ == "__main__":
__UpperCAmelCase = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
__UpperCAmelCase = Accelerator(kwargs_handlers=[ddp_scaler])
__UpperCAmelCase = torch.nn.Linear(100, 200)
__UpperCAmelCase = accelerator.prepare(model)
# Check the values changed in kwargs
__UpperCAmelCase = ''
__UpperCAmelCase = model.bucket_bytes_cap // (1024 * 1024)
if observed_bucket_cap_map != 15:
error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 29 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def lowercase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = 10
UpperCAmelCase_ : Tuple = datasets.Features(
{
'tokens': datasets.Sequence(datasets.Value('string' ) ),
'labels': datasets.Sequence(datasets.ClassLabel(names=['negative', 'positive'] ) ),
'answers': datasets.Sequence(
{
'text': datasets.Value('string' ),
'answer_start': datasets.Value('int32' ),
} ),
'id': datasets.Value('int64' ),
} )
UpperCAmelCase_ : Tuple = datasets.Dataset.from_dict(
{
'tokens': [['foo'] * 5] * n,
'labels': [[1] * 5] * n,
'answers': [{'answer_start': [97], 'text': ['1976']}] * 10,
'id': list(range(__snake_case ) ),
} , features=__snake_case , )
return dataset
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Optional[Any] , __snake_case : List[str] ):
'''simple docstring'''
UpperCAmelCase_ : str = str(tmp_path_factory.mktemp('data' ) / 'file.arrow' )
dataset.map(cache_file_name=__snake_case )
return filename
# FILE_CONTENT + files
__UpperCAmelCase = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = tmp_path_factory.mktemp('data' ) / 'file.txt'
UpperCAmelCase_ : Tuple = FILE_CONTENT
with open(__snake_case , 'w' ) as f:
f.write(__snake_case )
return filename
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : List[str] ):
'''simple docstring'''
import bza
UpperCAmelCase_ : Optional[int] = tmp_path_factory.mktemp('data' ) / 'file.txt.bz2'
UpperCAmelCase_ : str = bytes(__snake_case , 'utf-8' )
with bza.open(__snake_case , 'wb' ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Any ):
'''simple docstring'''
import gzip
UpperCAmelCase_ : Optional[Any] = str(tmp_path_factory.mktemp('data' ) / 'file.txt.gz' )
UpperCAmelCase_ : Dict = bytes(__snake_case , 'utf-8' )
with gzip.open(__snake_case , 'wb' ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Dict ):
'''simple docstring'''
if datasets.config.LZ4_AVAILABLE:
import lza.frame
UpperCAmelCase_ : Any = tmp_path_factory.mktemp('data' ) / 'file.txt.lz4'
UpperCAmelCase_ : Any = bytes(__snake_case , 'utf-8' )
with lza.frame.open(__snake_case , 'wb' ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Tuple , __snake_case : List[Any] ):
'''simple docstring'''
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
UpperCAmelCase_ : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'file.txt.7z'
with pyazr.SevenZipFile(__snake_case , 'w' ) as archive:
archive.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : List[str] , __snake_case : List[Any] ):
'''simple docstring'''
import tarfile
UpperCAmelCase_ : Any = tmp_path_factory.mktemp('data' ) / 'file.txt.tar'
with tarfile.TarFile(__snake_case , 'w' ) as f:
f.add(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : str ):
'''simple docstring'''
import lzma
UpperCAmelCase_ : Union[str, Any] = tmp_path_factory.mktemp('data' ) / 'file.txt.xz'
UpperCAmelCase_ : Any = bytes(__snake_case , 'utf-8' )
with lzma.open(__snake_case , 'wb' ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Optional[int] , __snake_case : Optional[Any] ):
'''simple docstring'''
import zipfile
UpperCAmelCase_ : int = tmp_path_factory.mktemp('data' ) / 'file.txt.zip'
with zipfile.ZipFile(__snake_case , 'w' ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Optional[Any] ):
'''simple docstring'''
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
UpperCAmelCase_ : Tuple = tmp_path_factory.mktemp('data' ) / 'file.txt.zst'
UpperCAmelCase_ : List[str] = bytes(__snake_case , 'utf-8' )
with zstd.open(__snake_case , 'wb' ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Any ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = tmp_path_factory.mktemp('data' ) / 'file.xml'
UpperCAmelCase_ : List[Any] = textwrap.dedent(
'\\n <?xml version="1.0" encoding="UTF-8" ?>\n <tmx version="1.4">\n <header segtype="sentence" srclang="ca" />\n <body>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang="en"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang="en"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang="en"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang="en"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang="en"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>' )
with open(__snake_case , 'w' ) as f:
f.write(__snake_case )
return filename
__UpperCAmelCase = [
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
__UpperCAmelCase = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
__UpperCAmelCase = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
__UpperCAmelCase = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
__UpperCAmelCase = [
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope='session' )
def lowercase__ ( ):
'''simple docstring'''
return DATA_DICT_OF_LISTS
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = datasets.Dataset.from_dict(__snake_case )
UpperCAmelCase_ : Optional[Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset.arrow' )
dataset.map(cache_file_name=__snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = str(tmp_path_factory.mktemp('data' ) / 'dataset.sqlite' )
with contextlib.closing(sqlitea.connect(__snake_case ) ) as con:
UpperCAmelCase_ : List[Any] = con.cursor()
cur.execute('CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)' )
for item in DATA:
cur.execute('INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)' , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset.csv' )
with open(__snake_case , 'w' , newline='' ) as f:
UpperCAmelCase_ : Tuple = csv.DictWriter(__snake_case , fieldnames=['col_1', 'col_2', 'col_3'] )
writer.writeheader()
for item in DATA:
writer.writerow(__snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset2.csv' )
with open(__snake_case , 'w' , newline='' ) as f:
UpperCAmelCase_ : Optional[Any] = csv.DictWriter(__snake_case , fieldnames=['col_1', 'col_2', 'col_3'] )
writer.writeheader()
for item in DATA:
writer.writerow(__snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : str , __snake_case : Any ):
'''simple docstring'''
import bza
UpperCAmelCase_ : int = tmp_path_factory.mktemp('data' ) / 'dataset.csv.bz2'
with open(__snake_case , 'rb' ) as f:
UpperCAmelCase_ : int = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(__snake_case , 'wb' ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : List[str] , __snake_case : Tuple , __snake_case : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = tmp_path_factory.mktemp('data' ) / 'dataset.csv.zip'
with zipfile.ZipFile(__snake_case , 'w' ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : str , __snake_case : Optional[int] , __snake_case : Tuple ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = tmp_path_factory.mktemp('data' ) / 'dataset.csv.zip'
with zipfile.ZipFile(__snake_case , 'w' ) as f:
f.write(__snake_case , arcname=os.path.basename(csv_path.replace('.csv' , '.CSV' ) ) )
f.write(__snake_case , arcname=os.path.basename(csva_path.replace('.csv' , '.CSV' ) ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Tuple , __snake_case : int , __snake_case : str ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.csv.zip'
with zipfile.ZipFile(__snake_case , 'w' ) as f:
f.write(__snake_case , arcname=os.path.join('main_dir' , os.path.basename(__snake_case ) ) )
f.write(__snake_case , arcname=os.path.join('main_dir' , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ : int = str(tmp_path_factory.mktemp('data' ) / 'dataset.parquet' )
UpperCAmelCase_ : Dict = pa.schema(
{
'col_1': pa.string(),
'col_2': pa.intaa(),
'col_3': pa.floataa(),
} )
with open(__snake_case , 'wb' ) as f:
UpperCAmelCase_ : List[Any] = pq.ParquetWriter(__snake_case , schema=__snake_case )
UpperCAmelCase_ : Any = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(__snake_case ) )] for k in DATA[0]} , schema=__snake_case )
writer.write_table(__snake_case )
writer.close()
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset.json' )
UpperCAmelCase_ : Optional[int] = {'data': DATA}
with open(__snake_case , 'w' ) as f:
json.dump(__snake_case , __snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset.json' )
UpperCAmelCase_ : Tuple = {'data': DATA_DICT_OF_LISTS}
with open(__snake_case , 'w' ) as f:
json.dump(__snake_case , __snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase_ : Dict = str(tmp_path_factory.mktemp('data' ) / 'dataset.jsonl' )
with open(__snake_case , 'w' ) as f:
for item in DATA:
f.write(json.dumps(__snake_case ) + '\n' )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : str ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset2.jsonl' )
with open(__snake_case , 'w' ) as f:
for item in DATA:
f.write(json.dumps(__snake_case ) + '\n' )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ : int = str(tmp_path_factory.mktemp('data' ) / 'dataset_312.jsonl' )
with open(__snake_case , 'w' ) as f:
for item in DATA_312:
f.write(json.dumps(__snake_case ) + '\n' )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : str ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = str(tmp_path_factory.mktemp('data' ) / 'dataset-str.jsonl' )
with open(__snake_case , 'w' ) as f:
for item in DATA_STR:
f.write(json.dumps(__snake_case ) + '\n' )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Dict , __snake_case : Dict ):
'''simple docstring'''
import gzip
UpperCAmelCase_ : Union[str, Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset.txt.gz' )
with open(__snake_case , 'rb' ) as orig_file:
with gzip.open(__snake_case , 'wb' ) as zipped_file:
zipped_file.writelines(__snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : int , __snake_case : Any ):
'''simple docstring'''
import gzip
UpperCAmelCase_ : Dict = str(tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.gz' )
with open(__snake_case , 'rb' ) as orig_file:
with gzip.open(__snake_case , 'wb' ) as zipped_file:
zipped_file.writelines(__snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : Optional[int] ):
'''simple docstring'''
UpperCAmelCase_ : int = tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.zip'
with zipfile.ZipFile(__snake_case , 'w' ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Optional[Any] , __snake_case : str , __snake_case : Dict , __snake_case : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ : str = tmp_path_factory.mktemp('data' ) / 'dataset_nested.jsonl.zip'
with zipfile.ZipFile(__snake_case , 'w' ) as f:
f.write(__snake_case , arcname=os.path.join('nested' , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : Tuple ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.jsonl.zip'
with zipfile.ZipFile(__snake_case , 'w' ) as f:
f.write(__snake_case , arcname=os.path.join('main_dir' , os.path.basename(__snake_case ) ) )
f.write(__snake_case , arcname=os.path.join('main_dir' , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Tuple , __snake_case : str , __snake_case : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.tar'
with tarfile.TarFile(__snake_case , 'w' ) as f:
f.add(__snake_case , arcname=os.path.basename(__snake_case ) )
f.add(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : str , __snake_case : Any , __snake_case : Any , __snake_case : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = tmp_path_factory.mktemp('data' ) / 'dataset_nested.jsonl.tar'
with tarfile.TarFile(__snake_case , 'w' ) as f:
f.add(__snake_case , arcname=os.path.join('nested' , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ : Any = ['0', '1', '2', '3']
UpperCAmelCase_ : Dict = str(tmp_path_factory.mktemp('data' ) / 'dataset.txt' )
with open(__snake_case , 'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = ['0', '1', '2', '3']
UpperCAmelCase_ : Optional[int] = str(tmp_path_factory.mktemp('data' ) / 'dataset2.txt' )
with open(__snake_case , 'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Tuple ):
'''simple docstring'''
UpperCAmelCase_ : Dict = ['0', '1', '2', '3']
UpperCAmelCase_ : List[str] = tmp_path_factory.mktemp('data' ) / 'dataset.abc'
with open(__snake_case , 'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Any , __snake_case : Union[str, Any] , __snake_case : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = tmp_path_factory.mktemp('data' ) / 'dataset.text.zip'
with zipfile.ZipFile(__snake_case , 'w' ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Dict , __snake_case : str , __snake_case : Any ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.text.zip'
with zipfile.ZipFile(__snake_case , 'w' ) as f:
f.write(__snake_case , arcname=os.path.join('main_dir' , os.path.basename(__snake_case ) ) )
f.write(__snake_case , arcname=os.path.join('main_dir' , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Union[str, Any] , __snake_case : str , __snake_case : Optional[int] ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = tmp_path_factory.mktemp('data' ) / 'dataset.ext.zip'
with zipfile.ZipFile(__snake_case , 'w' ) as f:
f.write(__snake_case , arcname=os.path.basename('unsupported.ext' ) )
f.write(__snake_case , arcname=os.path.basename('unsupported_2.ext' ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Dict ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = '\n'.join(['First', 'Second\u2029with Unicode new line', 'Third'] )
UpperCAmelCase_ : Dict = str(tmp_path_factory.mktemp('data' ) / 'dataset_with_unicode_new_lines.txt' )
with open(__snake_case , 'w' , encoding='utf-8' ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( ):
'''simple docstring'''
return os.path.join('tests' , 'features' , 'data' , 'test_image_rgb.jpg' )
@pytest.fixture(scope='session' )
def lowercase__ ( ):
'''simple docstring'''
return os.path.join('tests' , 'features' , 'data' , 'test_audio_44100.wav' )
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : str , __snake_case : List[str] ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'dataset.img.zip'
with zipfile.ZipFile(__snake_case , 'w' ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
f.write(__snake_case , arcname=os.path.basename(__snake_case ).replace('.jpg' , '2.jpg' ) )
return path
@pytest.fixture(scope='session' )
def lowercase__ ( __snake_case : Any ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = tmp_path_factory.mktemp('data_dir' )
(data_dir / "subdir").mkdir()
with open(data_dir / 'subdir' / 'train.txt' , 'w' ) as f:
f.write('foo\n' * 10 )
with open(data_dir / 'subdir' / 'test.txt' , 'w' ) as f:
f.write('bar\n' * 10 )
# hidden file
with open(data_dir / 'subdir' / '.test.txt' , 'w' ) as f:
f.write('bar\n' * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / '.subdir' / 'train.txt' , 'w' ) as f:
f.write('foo\n' * 10 )
with open(data_dir / '.subdir' / 'test.txt' , 'w' ) as f:
f.write('bar\n' * 10 )
return data_dir
| 29 | 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 __lowercase ( a__ ) -> List[Any]: # picklable for multiprocessing
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __lowercase ( ) -> Any:
with parallel_backend('spark' ):
assert ParallelBackendConfig.backend_name == "spark"
__SCREAMING_SNAKE_CASE = [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 __lowercase ( a__ ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = [1, 2]
__SCREAMING_SNAKE_CASE = {'a': 1, 'b': 2}
__SCREAMING_SNAKE_CASE = {'a': [1, 2], 'b': [3, 4]}
__SCREAMING_SNAKE_CASE = {'a': {'1': 1}, 'b': 2}
__SCREAMING_SNAKE_CASE = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
__SCREAMING_SNAKE_CASE = [2, 3]
__SCREAMING_SNAKE_CASE = {'a': 2, 'b': 3}
__SCREAMING_SNAKE_CASE = {'a': [2, 3], 'b': [4, 5]}
__SCREAMING_SNAKE_CASE = {'a': {'1': 2}, 'b': 3}
__SCREAMING_SNAKE_CASE = {'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
| 118 |
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 __lowercase ( a__ ) -> List[Any]: # picklable for multiprocessing
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __lowercase ( ) -> Any:
with parallel_backend('spark' ):
assert ParallelBackendConfig.backend_name == "spark"
__SCREAMING_SNAKE_CASE = [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 __lowercase ( a__ ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = [1, 2]
__SCREAMING_SNAKE_CASE = {'a': 1, 'b': 2}
__SCREAMING_SNAKE_CASE = {'a': [1, 2], 'b': [3, 4]}
__SCREAMING_SNAKE_CASE = {'a': {'1': 1}, 'b': 2}
__SCREAMING_SNAKE_CASE = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
__SCREAMING_SNAKE_CASE = [2, 3]
__SCREAMING_SNAKE_CASE = {'a': 2, 'b': 3}
__SCREAMING_SNAKE_CASE = {'a': [2, 3], 'b': [4, 5]}
__SCREAMING_SNAKE_CASE = {'a': {'1': 2}, 'b': 3}
__SCREAMING_SNAKE_CASE = {'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
| 118 | 1 |
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