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"""simple docstring"""
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def A_ ( _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :Tuple = StableDiffusionPipeline.from_pretrained(_lowercase, torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
snake_case_ :List[Any] = load_file(_lowercase )
snake_case_ :Any = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
snake_case_ :List[str] = key.split(""".""" )[0].split(LORA_PREFIX_TEXT_ENCODER + """_""" )[-1].split("""_""" )
snake_case_ :str = pipeline.text_encoder
else:
snake_case_ :List[str] = key.split(""".""" )[0].split(LORA_PREFIX_UNET + """_""" )[-1].split("""_""" )
snake_case_ :Dict = pipeline.unet
# find the target layer
snake_case_ :List[Any] = layer_infos.pop(0 )
while len(_lowercase ) > -1:
try:
snake_case_ :List[Any] = curr_layer.__getattr__(_lowercase )
if len(_lowercase ) > 0:
snake_case_ :Dict = layer_infos.pop(0 )
elif len(_lowercase ) == 0:
break
except Exception:
if len(_lowercase ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
snake_case_ :Tuple = layer_infos.pop(0 )
snake_case_ :Optional[int] = []
if "lora_down" in key:
pair_keys.append(key.replace("""lora_down""", """lora_up""" ) )
pair_keys.append(_lowercase )
else:
pair_keys.append(_lowercase )
pair_keys.append(key.replace("""lora_up""", """lora_down""" ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
snake_case_ :str = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
snake_case_ :Tuple = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(_lowercase, _lowercase ).unsqueeze(2 ).unsqueeze(3 )
else:
snake_case_ :List[str] = state_dict[pair_keys[0]].to(torch.floataa )
snake_case_ :List[Any] = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(_lowercase, _lowercase )
# update visited list
for item in pair_keys:
visited.append(_lowercase )
return pipeline
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument(
"--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
)
parser.add_argument(
"--lora_prefix_text_encoder",
default="lora_te",
type=str,
help="The prefix of text encoder weight in safetensors",
)
parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
parser.add_argument(
"--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
)
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
__a = parser.parse_args()
__a = args.base_model_path
__a = args.checkpoint_path
__a = args.dump_path
__a = args.lora_prefix_unet
__a = args.lora_prefix_text_encoder
__a = args.alpha
__a = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
__a = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ["Mask2FormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
"MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"Mask2FormerForUniversalSegmentation",
"Mask2FormerModel",
"Mask2FormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_maskaformer import MaskaFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskaformer import (
MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskaFormerForUniversalSegmentation,
MaskaFormerModel,
MaskaFormerPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 66 | 1 |
"""simple docstring"""
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
lowerCAmelCase__ : Dict ='''Usage of script: script_name <size_of_canvas:int>'''
lowerCAmelCase__ : Dict =[0] * 100 + [1] * 10
random.shuffle(choice)
def __lowercase ( a__ ) -> list[list[bool]]:
__SCREAMING_SNAKE_CASE = [[False for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )]
return canvas
def __lowercase ( a__ ) -> None:
for i, row in enumerate(__lowerCAmelCase ):
for j, _ in enumerate(__lowerCAmelCase ):
__SCREAMING_SNAKE_CASE = bool(random.getrandbits(1 ) )
def __lowercase ( a__ ) -> list[list[bool]]:
__SCREAMING_SNAKE_CASE = np.array(__lowerCAmelCase )
__SCREAMING_SNAKE_CASE = np.array(create_canvas(current_canvas.shape[0] ) )
for r, row in enumerate(__lowerCAmelCase ):
for c, pt in enumerate(__lowerCAmelCase ):
__SCREAMING_SNAKE_CASE = __judge_point(
__lowerCAmelCase , current_canvas[r - 1 : r + 2, c - 1 : c + 2] )
__SCREAMING_SNAKE_CASE = next_gen_canvas
del next_gen_canvas # cleaning memory as we move on.
__SCREAMING_SNAKE_CASE = current_canvas.tolist()
return return_canvas
def __lowercase ( a__ , a__ ) -> bool:
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
# finding dead or alive neighbours count.
for i in neighbours:
for status in i:
if status:
alive += 1
else:
dead += 1
# handling duplicate entry for focus pt.
if pt:
alive -= 1
else:
dead -= 1
# running the rules of game here.
__SCREAMING_SNAKE_CASE = pt
if pt:
if alive < 2:
__SCREAMING_SNAKE_CASE = False
elif alive == 2 or alive == 3:
__SCREAMING_SNAKE_CASE = True
elif alive > 3:
__SCREAMING_SNAKE_CASE = False
else:
if alive == 3:
__SCREAMING_SNAKE_CASE = True
return state
if __name__ == "__main__":
if len(sys.argv) != 2:
raise Exception(usage_doc)
lowerCAmelCase__ : str =int(sys.argv[1])
# main working structure of this module.
lowerCAmelCase__ : List[str] =create_canvas(canvas_size)
seed(c)
lowerCAmelCase__ , lowerCAmelCase__ : Tuple =plt.subplots()
fig.show()
lowerCAmelCase__ : Optional[int] =ListedColormap(['''w''', '''k'''])
try:
while True:
lowerCAmelCase__ : str =run(c)
ax.matshow(c, cmap=cmap)
fig.canvas.draw()
ax.cla()
except KeyboardInterrupt:
# do nothing.
pass
| 353 |
def __lowercase ( a__ = 10_00 ) -> int:
__SCREAMING_SNAKE_CASE = -1
__SCREAMING_SNAKE_CASE = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
__SCREAMING_SNAKE_CASE = (n * n - 2 * a * n) // (2 * n - 2 * a)
__SCREAMING_SNAKE_CASE = n - a - b
if c * c == (a * a + b * b):
__SCREAMING_SNAKE_CASE = a * b * c
if candidate >= product:
__SCREAMING_SNAKE_CASE = candidate
return product
if __name__ == "__main__":
print(F'''{solution() = }''')
| 118 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
_UpperCamelCase = TypeVar('''T''')
_UpperCamelCase = TypeVar('''U''')
class _A ( Generic[T, U] ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__UpperCAmelCase : Tuple = key
__UpperCAmelCase : List[str] = val
__UpperCAmelCase : DoubleLinkedListNode[T, U] | None = None
__UpperCAmelCase : DoubleLinkedListNode[T, U] | None = None
def __repr__( self ) -> str:
'''simple docstring'''
return (
f'Node: key: {self.key}, val: {self.val}, '
f'has next: {bool(self.next )}, has prev: {bool(self.prev )}'
)
class _A ( Generic[T, U] ):
def __init__( self ) -> None:
'''simple docstring'''
__UpperCAmelCase : DoubleLinkedListNode[T, U] = DoubleLinkedListNode(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : DoubleLinkedListNode[T, U] = DoubleLinkedListNode(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = self.rear, self.head
def __repr__( self ) -> str:
'''simple docstring'''
__UpperCAmelCase : int = ["""DoubleLinkedList"""]
__UpperCAmelCase : Dict = self.head
while node.next is not None:
rep.append(str(__UpperCAmelCase ) )
__UpperCAmelCase : str = node.next
rep.append(str(self.rear ) )
return ",\n ".join(__UpperCAmelCase )
def __A ( self , __UpperCAmelCase ) -> None:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
__UpperCAmelCase : Dict = node
__UpperCAmelCase : str = previous
__UpperCAmelCase : Dict = node
__UpperCAmelCase : Any = self.rear
def __A ( self , __UpperCAmelCase ) -> DoubleLinkedListNode[T, U] | None:
'''simple docstring'''
if node.prev is None or node.next is None:
return None
__UpperCAmelCase : Optional[int] = node.next
__UpperCAmelCase : List[Any] = node.prev
__UpperCAmelCase : Any = None
__UpperCAmelCase : str = None
return node
class _A ( Generic[T, U] ):
_SCREAMING_SNAKE_CASE : dict[Callable[[T], U], LRUCache[T, U]] = {}
def __init__( self , __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase : DoubleLinkedList[T, U] = DoubleLinkedList()
__UpperCAmelCase : List[str] = capacity
__UpperCAmelCase : int = 0
__UpperCAmelCase : Any = 0
__UpperCAmelCase : Dict = 0
__UpperCAmelCase : dict[T, DoubleLinkedListNode[T, U]] = {}
def __repr__( self ) -> str:
'''simple docstring'''
return (
f'CacheInfo(hits={self.hits}, misses={self.miss}, '
f'capacity={self.capacity}, current size={self.num_keys})'
)
def __contains__( self , __UpperCAmelCase ) -> bool:
'''simple docstring'''
return key in self.cache
def __A ( self , __UpperCAmelCase ) -> U | None:
'''simple docstring'''
# Note: pythonic interface would throw KeyError rather than return None
if key in self.cache:
self.hits += 1
__UpperCAmelCase : DoubleLinkedListNode[T, U] = self.cache[key]
__UpperCAmelCase : Tuple = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(__UpperCAmelCase )
return node.val
self.miss += 1
return None
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> None:
'''simple docstring'''
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
__UpperCAmelCase : List[Any] = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(__UpperCAmelCase ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
__UpperCAmelCase : str = DoubleLinkedListNode(__UpperCAmelCase , __UpperCAmelCase )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
__UpperCAmelCase : Tuple = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
__UpperCAmelCase : List[str] = value
self.list.add(__UpperCAmelCase )
@classmethod
def __A ( cls , __UpperCAmelCase = 128 ) -> Callable[[Callable[[T], U]], Callable[..., U]]:
'''simple docstring'''
def cache_decorator_inner(__UpperCAmelCase ) -> Callable[..., U]:
def cache_decorator_wrapper(*__UpperCAmelCase ) -> U:
if func not in cls.decorator_function_to_instance_map:
__UpperCAmelCase : Optional[Any] = LRUCache(__UpperCAmelCase )
__UpperCAmelCase : int = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
__UpperCAmelCase : Tuple = func(*__UpperCAmelCase )
cls.decorator_function_to_instance_map[func].put(args[0] , __UpperCAmelCase )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(__UpperCAmelCase , """cache_info""" , __UpperCAmelCase ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 254 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
_UpperCamelCase = get_logger(__name__)
class _A :
_SCREAMING_SNAKE_CASE : Dict = "dummy_data"
_SCREAMING_SNAKE_CASE : int = "datasets"
_SCREAMING_SNAKE_CASE : Union[str, Any] = False
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = True , __UpperCAmelCase = None , ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : int = 0
__UpperCAmelCase : Tuple = dataset_name
__UpperCAmelCase : List[Any] = cache_dir
__UpperCAmelCase : List[Any] = use_local_dummy_data
__UpperCAmelCase : str = config
# download_callbacks take a single url as input
__UpperCAmelCase : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
__UpperCAmelCase : Tuple = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
__UpperCAmelCase : Dict = str(__UpperCAmelCase )
# to be downloaded
__UpperCAmelCase : Any = None
__UpperCAmelCase : List[str] = None
@property
def __A ( self ) -> Dict:
'''simple docstring'''
if self._dummy_file is None:
__UpperCAmelCase : Dict = self.download_dummy_data()
return self._dummy_file
@property
def __A ( self ) -> Any:
'''simple docstring'''
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("""dummy""" , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join("""dummy""" , self.version_name )
@property
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
return os.path.join(self.dummy_data_folder , """dummy_data.zip""" )
def __A ( self ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : Tuple = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
__UpperCAmelCase : Tuple = cached_path(
__UpperCAmelCase , cache_dir=self.cache_dir , extract_compressed_file=__UpperCAmelCase , force_extract=__UpperCAmelCase )
return os.path.join(__UpperCAmelCase , self.dummy_file_name )
@property
def __A ( self ) -> Any:
'''simple docstring'''
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def __A ( self ) -> List[Any]:
'''simple docstring'''
if self._bucket_url is None:
__UpperCAmelCase : Optional[Any] = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , """/""" ) )
return self._bucket_url
@property
def __A ( self ) -> Dict:
'''simple docstring'''
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , """/""" ).split("""/""" )[:-1] )
def __A ( self , __UpperCAmelCase , *__UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
__UpperCAmelCase : Dict = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
__UpperCAmelCase : Optional[int] = self.dummy_file_name
# special case when data_url is a dict
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
return self.create_dummy_data_dict(__UpperCAmelCase , __UpperCAmelCase )
elif isinstance(__UpperCAmelCase , (list, tuple) ):
return self.create_dummy_data_list(__UpperCAmelCase , __UpperCAmelCase )
else:
return self.create_dummy_data_single(__UpperCAmelCase , __UpperCAmelCase )
def __A ( self , __UpperCAmelCase , *__UpperCAmelCase ) -> Dict:
'''simple docstring'''
return self.download_and_extract(__UpperCAmelCase )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Any:
'''simple docstring'''
return self.download_and_extract(__UpperCAmelCase )
def __A ( self , __UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
return path
def __A ( self ) -> List[Any]:
'''simple docstring'''
return {}
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : str = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
for single_url in single_urls:
download_callback(__UpperCAmelCase )
else:
__UpperCAmelCase : Union[str, Any] = single_urls
download_callback(__UpperCAmelCase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
__UpperCAmelCase : str = [os.path.join(__UpperCAmelCase , urllib.parse.quote_plus(Path(__UpperCAmelCase ).name ) ) for x in single_urls]
else:
__UpperCAmelCase : List[str] = single_urls
__UpperCAmelCase : Any = os.path.join(__UpperCAmelCase , urllib.parse.quote_plus(Path(__UpperCAmelCase ).name ) )
__UpperCAmelCase : Union[str, Any] = value
# make sure that values are unique
if all(isinstance(__UpperCAmelCase , __UpperCAmelCase ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
__UpperCAmelCase : Tuple = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
__UpperCAmelCase : Tuple = all(bool(re.findall("""[0-9]{3,}-of-[0-9]{3,}""" , __UpperCAmelCase ) ) for url in data_url )
__UpperCAmelCase : str = all(
url.startswith("""https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed""" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
__UpperCAmelCase : int = [data_url[0]] * len(__UpperCAmelCase )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(__UpperCAmelCase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__UpperCAmelCase : List[str] = os.path.join(__UpperCAmelCase , urllib.parse.quote_plus(single_url.split("""/""" )[-1] ) )
dummy_data_list.append(__UpperCAmelCase )
return dummy_data_list
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
for download_callback in self.download_callbacks:
download_callback(__UpperCAmelCase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__UpperCAmelCase : List[str] = os.path.join(__UpperCAmelCase , urllib.parse.quote_plus(data_url.split("""/""" )[-1] ) )
if os.path.exists(__UpperCAmelCase ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def __A ( self ) -> Tuple:
'''simple docstring'''
pass
def __A ( self ) -> int:
'''simple docstring'''
pass
def __A ( self , __UpperCAmelCase ) -> Any:
'''simple docstring'''
def _iter_archive_members(__UpperCAmelCase ):
# this preserves the order of the members inside the ZIP archive
__UpperCAmelCase : Dict = Path(self.dummy_file ).parent
__UpperCAmelCase : Dict = path.relative_to(__UpperCAmelCase )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
__UpperCAmelCase : List[str] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(__UpperCAmelCase )
__UpperCAmelCase : Any = Path(__UpperCAmelCase )
__UpperCAmelCase : int = _iter_archive_members(__UpperCAmelCase ) if self.use_local_dummy_data else path.rglob("""*""" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((""".""", """__""") ):
yield file_path.relative_to(__UpperCAmelCase ).as_posix(), file_path.open("""rb""" )
def __A ( self , __UpperCAmelCase ) -> Dict:
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
__UpperCAmelCase : List[Any] = [paths]
for path in paths:
if os.path.isfile(__UpperCAmelCase ):
if os.path.basename(__UpperCAmelCase ).startswith((""".""", """__""") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(__UpperCAmelCase ):
if os.path.basename(__UpperCAmelCase ).startswith((""".""", """__""") ):
continue
dirnames.sort()
for filename in sorted(__UpperCAmelCase ):
if filename.startswith((""".""", """__""") ):
continue
yield os.path.join(__UpperCAmelCase , __UpperCAmelCase )
| 254 | 1 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowerCamelCase = logging.get_logger(__name__)
class _a ( _lowercase):
_a : int = ['''input_values''', '''padding_mask''']
def __init__( self : Union[str, Any] , _SCREAMING_SNAKE_CASE : int = 1 , _SCREAMING_SNAKE_CASE : int = 2_4000 , _SCREAMING_SNAKE_CASE : float = 0.0 , _SCREAMING_SNAKE_CASE : float = None , _SCREAMING_SNAKE_CASE : float = None , **_SCREAMING_SNAKE_CASE : str , )-> Dict:
super().__init__(feature_size=_SCREAMING_SNAKE_CASE , sampling_rate=_SCREAMING_SNAKE_CASE , padding_value=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Dict = chunk_length_s
lowerCAmelCase__ : int = overlap
@property
def UpperCAmelCase__( self : int )-> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def UpperCAmelCase__( self : Union[str, Any] )-> Optional[int]:
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
def __call__( self : Tuple , _SCREAMING_SNAKE_CASE : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _SCREAMING_SNAKE_CASE : Optional[Union[bool, str, PaddingStrategy]] = None , _SCREAMING_SNAKE_CASE : Optional[bool] = False , _SCREAMING_SNAKE_CASE : Optional[int] = None , _SCREAMING_SNAKE_CASE : Optional[Union[str, TensorType]] = None , _SCREAMING_SNAKE_CASE : Optional[int] = None , )-> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
F' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
if padding and truncation:
raise ValueError('''Both padding and truncation were set. Make sure you only set one.''' )
elif padding is None:
# by default let's pad the inputs
lowerCAmelCase__ : Union[str, Any] = True
lowerCAmelCase__ : Any = bool(
isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) )
if is_batched:
lowerCAmelCase__ : str = [np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ):
lowerCAmelCase__ : str = np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa )
elif isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
lowerCAmelCase__ : Any = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase__ : Any = [np.asarray(_SCREAMING_SNAKE_CASE ).T]
# verify inputs are valid
for idx, example in enumerate(_SCREAMING_SNAKE_CASE ):
if example.ndim > 2:
raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels' )
lowerCAmelCase__ : Any = None
lowerCAmelCase__ : Dict = BatchFeature({'''input_values''': raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
lowerCAmelCase__ : str = min(array.shape[0] for array in raw_audio )
lowerCAmelCase__ : Optional[Any] = int(np.floor(max_length / self.chunk_stride ) )
lowerCAmelCase__ : Any = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
lowerCAmelCase__ : Union[str, Any] = max(array.shape[0] for array in raw_audio )
lowerCAmelCase__ : Optional[Any] = int(np.ceil(max_length / self.chunk_stride ) )
lowerCAmelCase__ : Any = (nb_step - 1) * self.chunk_stride + self.chunk_length
lowerCAmelCase__ : str = '''max_length'''
else:
lowerCAmelCase__ : Tuple = input_values
# normal padding on batch
if padded_inputs is None:
lowerCAmelCase__ : Union[str, Any] = self.pad(
_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , )
if padding:
lowerCAmelCase__ : Optional[Any] = padded_inputs.pop('''attention_mask''' )
lowerCAmelCase__ : Union[str, Any] = []
for example in padded_inputs.pop('''input_values''' ):
if self.feature_size == 1:
lowerCAmelCase__ : Optional[int] = example[..., None]
input_values.append(example.T )
lowerCAmelCase__ : Dict = input_values
if return_tensors is not None:
lowerCAmelCase__ : Any = padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE )
return padded_inputs
| 211 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = OrderedDict(
[
# Base model mapping
('''albert''', '''FlaxAlbertModel'''),
('''bart''', '''FlaxBartModel'''),
('''beit''', '''FlaxBeitModel'''),
('''bert''', '''FlaxBertModel'''),
('''big_bird''', '''FlaxBigBirdModel'''),
('''blenderbot''', '''FlaxBlenderbotModel'''),
('''blenderbot-small''', '''FlaxBlenderbotSmallModel'''),
('''clip''', '''FlaxCLIPModel'''),
('''distilbert''', '''FlaxDistilBertModel'''),
('''electra''', '''FlaxElectraModel'''),
('''gpt-sw3''', '''FlaxGPT2Model'''),
('''gpt2''', '''FlaxGPT2Model'''),
('''gpt_neo''', '''FlaxGPTNeoModel'''),
('''gptj''', '''FlaxGPTJModel'''),
('''longt5''', '''FlaxLongT5Model'''),
('''marian''', '''FlaxMarianModel'''),
('''mbart''', '''FlaxMBartModel'''),
('''mt5''', '''FlaxMT5Model'''),
('''opt''', '''FlaxOPTModel'''),
('''pegasus''', '''FlaxPegasusModel'''),
('''regnet''', '''FlaxRegNetModel'''),
('''resnet''', '''FlaxResNetModel'''),
('''roberta''', '''FlaxRobertaModel'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormModel'''),
('''roformer''', '''FlaxRoFormerModel'''),
('''t5''', '''FlaxT5Model'''),
('''vision-text-dual-encoder''', '''FlaxVisionTextDualEncoderModel'''),
('''vit''', '''FlaxViTModel'''),
('''wav2vec2''', '''FlaxWav2Vec2Model'''),
('''whisper''', '''FlaxWhisperModel'''),
('''xglm''', '''FlaxXGLMModel'''),
('''xlm-roberta''', '''FlaxXLMRobertaModel'''),
]
)
lowerCamelCase = OrderedDict(
[
# Model for pre-training mapping
('''albert''', '''FlaxAlbertForPreTraining'''),
('''bart''', '''FlaxBartForConditionalGeneration'''),
('''bert''', '''FlaxBertForPreTraining'''),
('''big_bird''', '''FlaxBigBirdForPreTraining'''),
('''electra''', '''FlaxElectraForPreTraining'''),
('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
('''mbart''', '''FlaxMBartForConditionalGeneration'''),
('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
('''roberta''', '''FlaxRobertaForMaskedLM'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
('''roformer''', '''FlaxRoFormerForMaskedLM'''),
('''t5''', '''FlaxT5ForConditionalGeneration'''),
('''wav2vec2''', '''FlaxWav2Vec2ForPreTraining'''),
('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
]
)
lowerCamelCase = OrderedDict(
[
# Model for Masked LM mapping
('''albert''', '''FlaxAlbertForMaskedLM'''),
('''bart''', '''FlaxBartForConditionalGeneration'''),
('''bert''', '''FlaxBertForMaskedLM'''),
('''big_bird''', '''FlaxBigBirdForMaskedLM'''),
('''distilbert''', '''FlaxDistilBertForMaskedLM'''),
('''electra''', '''FlaxElectraForMaskedLM'''),
('''mbart''', '''FlaxMBartForConditionalGeneration'''),
('''roberta''', '''FlaxRobertaForMaskedLM'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
('''roformer''', '''FlaxRoFormerForMaskedLM'''),
('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
]
)
lowerCamelCase = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
('''bart''', '''FlaxBartForConditionalGeneration'''),
('''blenderbot''', '''FlaxBlenderbotForConditionalGeneration'''),
('''blenderbot-small''', '''FlaxBlenderbotSmallForConditionalGeneration'''),
('''encoder-decoder''', '''FlaxEncoderDecoderModel'''),
('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
('''marian''', '''FlaxMarianMTModel'''),
('''mbart''', '''FlaxMBartForConditionalGeneration'''),
('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
('''pegasus''', '''FlaxPegasusForConditionalGeneration'''),
('''t5''', '''FlaxT5ForConditionalGeneration'''),
]
)
lowerCamelCase = OrderedDict(
[
# Model for Image-classsification
('''beit''', '''FlaxBeitForImageClassification'''),
('''regnet''', '''FlaxRegNetForImageClassification'''),
('''resnet''', '''FlaxResNetForImageClassification'''),
('''vit''', '''FlaxViTForImageClassification'''),
]
)
lowerCamelCase = OrderedDict(
[
('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
]
)
lowerCamelCase = OrderedDict(
[
# Model for Causal LM mapping
('''bart''', '''FlaxBartForCausalLM'''),
('''bert''', '''FlaxBertForCausalLM'''),
('''big_bird''', '''FlaxBigBirdForCausalLM'''),
('''electra''', '''FlaxElectraForCausalLM'''),
('''gpt-sw3''', '''FlaxGPT2LMHeadModel'''),
('''gpt2''', '''FlaxGPT2LMHeadModel'''),
('''gpt_neo''', '''FlaxGPTNeoForCausalLM'''),
('''gptj''', '''FlaxGPTJForCausalLM'''),
('''opt''', '''FlaxOPTForCausalLM'''),
('''roberta''', '''FlaxRobertaForCausalLM'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForCausalLM'''),
('''xglm''', '''FlaxXGLMForCausalLM'''),
('''xlm-roberta''', '''FlaxXLMRobertaForCausalLM'''),
]
)
lowerCamelCase = OrderedDict(
[
# Model for Sequence Classification mapping
('''albert''', '''FlaxAlbertForSequenceClassification'''),
('''bart''', '''FlaxBartForSequenceClassification'''),
('''bert''', '''FlaxBertForSequenceClassification'''),
('''big_bird''', '''FlaxBigBirdForSequenceClassification'''),
('''distilbert''', '''FlaxDistilBertForSequenceClassification'''),
('''electra''', '''FlaxElectraForSequenceClassification'''),
('''mbart''', '''FlaxMBartForSequenceClassification'''),
('''roberta''', '''FlaxRobertaForSequenceClassification'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForSequenceClassification'''),
('''roformer''', '''FlaxRoFormerForSequenceClassification'''),
('''xlm-roberta''', '''FlaxXLMRobertaForSequenceClassification'''),
]
)
lowerCamelCase = OrderedDict(
[
# Model for Question Answering mapping
('''albert''', '''FlaxAlbertForQuestionAnswering'''),
('''bart''', '''FlaxBartForQuestionAnswering'''),
('''bert''', '''FlaxBertForQuestionAnswering'''),
('''big_bird''', '''FlaxBigBirdForQuestionAnswering'''),
('''distilbert''', '''FlaxDistilBertForQuestionAnswering'''),
('''electra''', '''FlaxElectraForQuestionAnswering'''),
('''mbart''', '''FlaxMBartForQuestionAnswering'''),
('''roberta''', '''FlaxRobertaForQuestionAnswering'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForQuestionAnswering'''),
('''roformer''', '''FlaxRoFormerForQuestionAnswering'''),
('''xlm-roberta''', '''FlaxXLMRobertaForQuestionAnswering'''),
]
)
lowerCamelCase = OrderedDict(
[
# Model for Token Classification mapping
('''albert''', '''FlaxAlbertForTokenClassification'''),
('''bert''', '''FlaxBertForTokenClassification'''),
('''big_bird''', '''FlaxBigBirdForTokenClassification'''),
('''distilbert''', '''FlaxDistilBertForTokenClassification'''),
('''electra''', '''FlaxElectraForTokenClassification'''),
('''roberta''', '''FlaxRobertaForTokenClassification'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForTokenClassification'''),
('''roformer''', '''FlaxRoFormerForTokenClassification'''),
('''xlm-roberta''', '''FlaxXLMRobertaForTokenClassification'''),
]
)
lowerCamelCase = OrderedDict(
[
# Model for Multiple Choice mapping
('''albert''', '''FlaxAlbertForMultipleChoice'''),
('''bert''', '''FlaxBertForMultipleChoice'''),
('''big_bird''', '''FlaxBigBirdForMultipleChoice'''),
('''distilbert''', '''FlaxDistilBertForMultipleChoice'''),
('''electra''', '''FlaxElectraForMultipleChoice'''),
('''roberta''', '''FlaxRobertaForMultipleChoice'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMultipleChoice'''),
('''roformer''', '''FlaxRoFormerForMultipleChoice'''),
('''xlm-roberta''', '''FlaxXLMRobertaForMultipleChoice'''),
]
)
lowerCamelCase = OrderedDict(
[
('''bert''', '''FlaxBertForNextSentencePrediction'''),
]
)
lowerCamelCase = OrderedDict(
[
('''speech-encoder-decoder''', '''FlaxSpeechEncoderDecoderModel'''),
('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
]
)
lowerCamelCase = OrderedDict(
[
('''whisper''', '''FlaxWhisperForAudioClassification'''),
]
)
lowerCamelCase = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowerCamelCase = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowerCamelCase = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
lowerCamelCase = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
lowerCamelCase = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowerCamelCase = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowerCamelCase = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowerCamelCase = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowerCamelCase = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowerCamelCase = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
lowerCamelCase = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
lowerCamelCase = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
lowerCamelCase = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowerCamelCase = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class _a ( _BaseAutoModelClass):
_a : List[str] = FLAX_MODEL_MAPPING
lowerCamelCase = auto_class_update(FlaxAutoModel)
class _a ( _BaseAutoModelClass):
_a : Union[str, Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
lowerCamelCase = auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')
class _a ( _BaseAutoModelClass):
_a : Optional[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
lowerCamelCase = auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')
class _a ( _BaseAutoModelClass):
_a : Tuple = FLAX_MODEL_FOR_MASKED_LM_MAPPING
lowerCamelCase = auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')
class _a ( _BaseAutoModelClass):
_a : Dict = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
lowerCamelCase = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)
class _a ( _BaseAutoModelClass):
_a : Union[str, Any] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowerCamelCase = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)
class _a ( _BaseAutoModelClass):
_a : str = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
lowerCamelCase = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')
class _a ( _BaseAutoModelClass):
_a : Union[str, Any] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowerCamelCase = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)
class _a ( _BaseAutoModelClass):
_a : List[Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
lowerCamelCase = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')
class _a ( _BaseAutoModelClass):
_a : Any = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
lowerCamelCase = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)
class _a ( _BaseAutoModelClass):
_a : List[Any] = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
lowerCamelCase = auto_class_update(
FlaxAutoModelForImageClassification, head_doc='''image classification'''
)
class _a ( _BaseAutoModelClass):
_a : List[Any] = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
lowerCamelCase = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')
class _a ( _BaseAutoModelClass):
_a : Optional[Any] = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
lowerCamelCase = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)
| 211 | 1 |
def lowercase ( SCREAMING_SNAKE_CASE__ : str ) -> str:
_snake_case : int = 1
_snake_case : Dict = 2
while i * i <= n:
_snake_case : Union[str, Any] = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_divisors
def lowercase ( ) -> List[str]:
_snake_case : Tuple = 1
_snake_case : Dict = 1
while True:
i += 1
t_num += i
if count_divisors(__A ) > 500:
break
return t_num
if __name__ == "__main__":
print(solution())
| 317 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def SCREAMING_SNAKE_CASE__ ( __A = 1_500_000 ) -> int:
_snake_case = defaultdict(__A )
_snake_case = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , __A , 2 ):
if gcd(__A , __A ) > 1:
continue
_snake_case = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(__A , limit + 1 , __A ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 42 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class _SCREAMING_SNAKE_CASE ( snake_case__ ):
snake_case__ : Tuple = """fnet"""
def __init__( self : Tuple , __lowerCamelCase : str=32_000 , __lowerCamelCase : List[str]=768 , __lowerCamelCase : List[str]=12 , __lowerCamelCase : str=3_072 , __lowerCamelCase : List[str]="gelu_new" , __lowerCamelCase : str=0.1 , __lowerCamelCase : Optional[Any]=512 , __lowerCamelCase : Any=4 , __lowerCamelCase : str=0.02 , __lowerCamelCase : List[Any]=1E-12 , __lowerCamelCase : int=False , __lowerCamelCase : Tuple=512 , __lowerCamelCase : int=3 , __lowerCamelCase : Dict=1 , __lowerCamelCase : List[Any]=2 , **__lowerCamelCase : Tuple , ):
super().__init__(pad_token_id=UpperCAmelCase_ , bos_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , **UpperCAmelCase_ )
UpperCamelCase :Optional[Any] = vocab_size
UpperCamelCase :List[str] = max_position_embeddings
UpperCamelCase :List[Any] = hidden_size
UpperCamelCase :Optional[int] = num_hidden_layers
UpperCamelCase :Optional[int] = intermediate_size
UpperCamelCase :List[str] = hidden_act
UpperCamelCase :List[str] = hidden_dropout_prob
UpperCamelCase :Tuple = initializer_range
UpperCamelCase :Union[str, Any] = type_vocab_size
UpperCamelCase :List[Any] = layer_norm_eps
UpperCamelCase :Optional[Any] = use_tpu_fourier_optimizations
UpperCamelCase :List[Any] = tpu_short_seq_length
| 365 |
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Optional[int] ) -> Any:
"""simple docstring"""
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
2: [4, 5],
3: [4],
4: [2, 3],
5: [2],
6: [0, 7],
7: [6],
8: [],
9: [1],
},
{
0: [4],
1: [6],
2: [],
3: [5, 6, 7],
4: [0, 6],
5: [3, 8, 9],
6: [1, 3, 4, 7],
7: [3, 6, 8, 9],
8: [5, 7],
9: [5, 7],
},
{
0: [1, 3],
1: [0, 2, 4],
2: [1, 3, 4],
3: [0, 2, 4],
4: [1, 2, 3],
},
][index]
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : dict[int, list[int]] ) -> list[tuple[int, int]]:
"""simple docstring"""
UpperCamelCase :Any = 0
UpperCamelCase :int = len(__magic_name__ ) # No of vertices in graph
UpperCamelCase :int = [0] * n
UpperCamelCase :Union[str, Any] = [False] * n
def dfs(__magic_name__ : str , __magic_name__ : Dict , __magic_name__ : List[Any] , __magic_name__ : Union[str, Any] ):
UpperCamelCase :Any = True
UpperCamelCase :str = id_
id_ += 1
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
dfs(__magic_name__ , __magic_name__ , __magic_name__ , id_ )
UpperCamelCase :Dict = min(low[at] , low[to] )
if id_ <= low[to]:
bridges.append((at, to) if at < to else (to, at) )
else:
# This edge is a back edge and cannot be a bridge
UpperCamelCase :int = min(low[at] , low[to] )
UpperCamelCase :list[tuple[int, int]] = []
for i in range(__magic_name__ ):
if not visited[i]:
dfs(__magic_name__ , -1 , __magic_name__ , id_ )
return bridges
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase = logging.get_logger(__name__)
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = SwinConfig(
embed_dim=1_92 , depths=(2, 2, 18, 2) , num_heads=(6, 12, 24, 48) , window_size=12 , out_features=['''stage2''', '''stage3''', '''stage4'''] , )
lowercase__ = DetaConfig(
backbone_config=SCREAMING_SNAKE_CASE , num_queries=9_00 , encoder_ffn_dim=20_48 , decoder_ffn_dim=20_48 , num_feature_levels=5 , assign_first_stage=SCREAMING_SNAKE_CASE , with_box_refine=SCREAMING_SNAKE_CASE , two_stage=SCREAMING_SNAKE_CASE , )
# set labels
lowercase__ = '''huggingface/label-files'''
if "o365" in model_name:
lowercase__ = 3_66
lowercase__ = '''object365-id2label.json'''
else:
lowercase__ = 91
lowercase__ = '''coco-detection-id2label.json'''
lowercase__ = num_labels
lowercase__ = json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , repo_type='''dataset''' ) ) , '''r''' ) )
lowercase__ = {int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
return config
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = []
# stem
# fmt: off
rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''') )
rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((f'backbone.0.body.layers.{i}.downsample.reduction.weight', f'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.weight', f'model.backbone.model.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.bias', f'model.backbone.model.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''') )
rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''') )
rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''') )
rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''') )
rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''') )
rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''') )
# transformer encoder
for i in range(config.encoder_layers ):
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias') )
# transformer decoder
for i in range(config.decoder_layers ):
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias') )
# fmt: on
return rename_keys
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = dct.pop(SCREAMING_SNAKE_CASE )
lowercase__ = val
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
lowercase__ = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
lowercase__ = state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight' )
lowercase__ = state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[:dim, :]
lowercase__ = in_proj_bias[: dim]
lowercase__ = in_proj_weight[
dim : dim * 2, :
]
lowercase__ = in_proj_bias[
dim : dim * 2
]
lowercase__ = in_proj_weight[
-dim :, :
]
lowercase__ = in_proj_bias[-dim :]
# fmt: on
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = config.d_model
for i in range(config.decoder_layers ):
# read in weights + bias of input projection layer of self-attention
lowercase__ = state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
lowercase__ = state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[:hidden_size, :]
lowercase__ = in_proj_bias[:hidden_size]
lowercase__ = in_proj_weight[
hidden_size : hidden_size * 2, :
]
lowercase__ = in_proj_bias[hidden_size : hidden_size * 2]
lowercase__ = in_proj_weight[-hidden_size:, :]
lowercase__ = in_proj_bias[-hidden_size:]
def _a ( ):
"""simple docstring"""
lowercase__ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase__ = Image.open(requests.get(SCREAMING_SNAKE_CASE , stream=SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = get_deta_config(SCREAMING_SNAKE_CASE )
# load original state dict
if model_name == "deta-swin-large":
lowercase__ = hf_hub_download(repo_id='''nielsr/deta-checkpoints''' , filename='''adet_swin_ft.pth''' )
elif model_name == "deta-swin-large-o365":
lowercase__ = hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365''' , filename='''deta_swin_pt_o365.pth''' )
else:
raise ValueError(f'Model name {model_name} not supported' )
lowercase__ = torch.load(SCREAMING_SNAKE_CASE , map_location='''cpu''' )['''model''']
# original state dict
for name, param in state_dict.items():
print(SCREAMING_SNAKE_CASE , param.shape )
# rename keys
lowercase__ = create_rename_keys(SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
read_in_swin_q_k_v(SCREAMING_SNAKE_CASE , config.backbone_config )
read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# fix some prefixes
for key in state_dict.copy().keys():
if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
lowercase__ = state_dict.pop(SCREAMING_SNAKE_CASE )
lowercase__ = val
if "input_proj" in key:
lowercase__ = state_dict.pop(SCREAMING_SNAKE_CASE )
lowercase__ = val
if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
lowercase__ = state_dict.pop(SCREAMING_SNAKE_CASE )
lowercase__ = val
# finally, create HuggingFace model and load state dict
lowercase__ = DetaForObjectDetection(SCREAMING_SNAKE_CASE )
model.load_state_dict(SCREAMING_SNAKE_CASE )
model.eval()
lowercase__ = '''cuda''' if torch.cuda.is_available() else '''cpu'''
model.to(SCREAMING_SNAKE_CASE )
# load image processor
lowercase__ = DetaImageProcessor(format='''coco_detection''' )
# verify our conversion on image
lowercase__ = prepare_img()
lowercase__ = processor(images=SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
lowercase__ = encoding['''pixel_values''']
lowercase__ = model(pixel_values.to(SCREAMING_SNAKE_CASE ) )
# verify logits
print('''Logits:''' , outputs.logits[0, :3, :3] )
print('''Boxes:''' , outputs.pred_boxes[0, :3, :3] )
if model_name == "deta-swin-large":
lowercase__ = torch.tensor(
[[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]] )
lowercase__ = torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]] )
elif model_name == "deta-swin-large-o365":
lowercase__ = torch.tensor(
[[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]] )
lowercase__ = torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]] )
assert torch.allclose(outputs.logits[0, :3, :3] , expected_logits.to(SCREAMING_SNAKE_CASE ) , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , expected_boxes.to(SCREAMING_SNAKE_CASE ) , atol=1E-4 )
print('''Everything ok!''' )
if pytorch_dump_folder_path:
# Save model and processor
logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...' )
Path(SCREAMING_SNAKE_CASE ).mkdir(exist_ok=SCREAMING_SNAKE_CASE )
model.save_pretrained(SCREAMING_SNAKE_CASE )
processor.save_pretrained(SCREAMING_SNAKE_CASE )
# Push to hub
if push_to_hub:
print('''Pushing model and processor to hub...''' )
model.push_to_hub(f'jozhang97/{model_name}' )
processor.push_to_hub(f'jozhang97/{model_name}' )
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
type=str,
default='deta-swin-large',
choices=['deta-swin-large', 'deta-swin-large-o365'],
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
help='Path to the folder to output PyTorch model.',
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
lowerCAmelCase = parser.parse_args()
convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 110 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __snake_case( _lowerCAmelCase ) -> List[Any]:
snake_case__ : Dict = SwinConfig()
snake_case__ : Optional[Any] = swin_name.split("""_""" )
snake_case__ : Any = name_split[1]
snake_case__ : List[Any] = int(name_split[4] )
snake_case__ : int = int(name_split[3][-1] )
if model_size == "tiny":
snake_case__ : List[Any] = 96
snake_case__ : int = (2, 2, 6, 2)
snake_case__ : int = (3, 6, 12, 24)
elif model_size == "small":
snake_case__ : Union[str, Any] = 96
snake_case__ : Optional[Any] = (2, 2, 18, 2)
snake_case__ : str = (3, 6, 12, 24)
elif model_size == "base":
snake_case__ : Dict = 128
snake_case__ : str = (2, 2, 18, 2)
snake_case__ : Dict = (4, 8, 16, 32)
else:
snake_case__ : List[str] = 192
snake_case__ : str = (2, 2, 18, 2)
snake_case__ : List[Any] = (6, 12, 24, 48)
if "in22k" in swin_name:
snake_case__ : str = 21_841
else:
snake_case__ : List[str] = 1_000
snake_case__ : int = """huggingface/label-files"""
snake_case__ : Any = """imagenet-1k-id2label.json"""
snake_case__ : List[Any] = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="""dataset""" ) , """r""" ) )
snake_case__ : Dict = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
snake_case__ : Optional[int] = idalabel
snake_case__ : List[Any] = {v: k for k, v in idalabel.items()}
snake_case__ : List[Any] = img_size
snake_case__ : Dict = num_classes
snake_case__ : Dict = embed_dim
snake_case__ : Optional[int] = depths
snake_case__ : int = num_heads
snake_case__ : Optional[int] = window_size
return config
def __snake_case( _lowerCAmelCase ) -> Dict:
if "patch_embed.proj" in name:
snake_case__ : List[str] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
snake_case__ : int = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if "layers" in name:
snake_case__ : str = """encoder.""" + name
if "attn.proj" in name:
snake_case__ : List[str] = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
snake_case__ : Tuple = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
snake_case__ : List[str] = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
snake_case__ : Optional[Any] = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
snake_case__ : Union[str, Any] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
snake_case__ : Dict = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "norm.weight":
snake_case__ : Tuple = """layernorm.weight"""
if name == "norm.bias":
snake_case__ : Union[str, Any] = """layernorm.bias"""
if "head" in name:
snake_case__ : Optional[int] = name.replace("""head""" , """classifier""" )
else:
snake_case__ : List[str] = """swin.""" + name
return name
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
for key in orig_state_dict.copy().keys():
snake_case__ : Optional[int] = orig_state_dict.pop(_lowerCAmelCase )
if "mask" in key:
continue
elif "qkv" in key:
snake_case__ : Dict = key.split(""".""" )
snake_case__ : Optional[int] = int(key_split[1] )
snake_case__ : Union[str, Any] = int(key_split[3] )
snake_case__ : List[Any] = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
snake_case__ : Optional[Any] = val[:dim, :]
snake_case__ : Tuple = val[
dim : dim * 2, :
]
snake_case__ : Dict = val[-dim:, :]
else:
snake_case__ : Tuple = val[
:dim
]
snake_case__ : int = val[
dim : dim * 2
]
snake_case__ : int = val[
-dim:
]
else:
snake_case__ : Union[str, Any] = val
return orig_state_dict
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> int:
snake_case__ : Optional[int] = timm.create_model(_lowerCAmelCase , pretrained=_lowerCAmelCase )
timm_model.eval()
snake_case__ : Optional[int] = get_swin_config(_lowerCAmelCase )
snake_case__ : Optional[Any] = SwinForImageClassification(_lowerCAmelCase )
model.eval()
snake_case__ : str = convert_state_dict(timm_model.state_dict() , _lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase )
snake_case__ : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case__ : Dict = AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" , """-""" ) ) )
snake_case__ : Dict = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
snake_case__ : Optional[int] = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" )
snake_case__ : Optional[Any] = timm_model(inputs["""pixel_values"""] )
snake_case__ : str = model(**_lowerCAmelCase ).logits
assert torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=1e-3 )
print(f"Saving model {swin_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCAmelCase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swin_name",
default="swin_tiny_patch4_window7_224",
type=str,
help="Name of the Swin timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
__a = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 35 | 0 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
A__ : Any = 'platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class lowercase__ :
_UpperCAmelCase :int = PegasusConfig
_UpperCAmelCase :Optional[Any] = {}
_UpperCAmelCase :Tuple = "gelu"
def __init__( self : Optional[int] , snake_case__ : Optional[int] , snake_case__ : Any=13 , snake_case__ : int=7 , snake_case__ : Optional[Any]=True , snake_case__ : str=False , snake_case__ : Dict=99 , snake_case__ : Union[str, Any]=32 , snake_case__ : List[Any]=5 , snake_case__ : Optional[Any]=4 , snake_case__ : Optional[int]=37 , snake_case__ : Optional[int]=0.1 , snake_case__ : List[Any]=0.1 , snake_case__ : Union[str, Any]=20 , snake_case__ : Tuple=2 , snake_case__ : Union[str, Any]=1 , snake_case__ : Any=0 , ):
lowerCamelCase_ : Optional[int] =parent
lowerCamelCase_ : Any =batch_size
lowerCamelCase_ : Dict =seq_length
lowerCamelCase_ : Dict =is_training
lowerCamelCase_ : int =use_labels
lowerCamelCase_ : Optional[int] =vocab_size
lowerCamelCase_ : Dict =hidden_size
lowerCamelCase_ : Optional[Any] =num_hidden_layers
lowerCamelCase_ : Dict =num_attention_heads
lowerCamelCase_ : Optional[Any] =intermediate_size
lowerCamelCase_ : str =hidden_dropout_prob
lowerCamelCase_ : int =attention_probs_dropout_prob
lowerCamelCase_ : Tuple =max_position_embeddings
lowerCamelCase_ : List[str] =eos_token_id
lowerCamelCase_ : str =pad_token_id
lowerCamelCase_ : List[str] =bos_token_id
def UpperCAmelCase__ ( self : Optional[int] ):
lowerCamelCase_ : Union[str, Any] =ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
lowerCamelCase_ : str =np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
lowerCamelCase_ : str =np.concatenate([input_ids, eos_tensor] , axis=1 )
lowerCamelCase_ : List[Any] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ : List[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 , )
lowerCamelCase_ : Optional[int] =prepare_pegasus_inputs_dict(snake_case__ , snake_case__ , snake_case__ )
return config, inputs_dict
def UpperCAmelCase__ ( self : List[str] , snake_case__ : List[Any] , snake_case__ : Tuple , snake_case__ : int ):
lowerCamelCase_ : Dict =20
lowerCamelCase_ : Optional[Any] =model_class_name(snake_case__ )
lowerCamelCase_ : Optional[Any] =model.encode(inputs_dict["input_ids"] )
lowerCamelCase_ , lowerCamelCase_ : Optional[Any] =(
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
lowerCamelCase_ : List[str] =model.init_cache(decoder_input_ids.shape[0] , snake_case__ , snake_case__ )
lowerCamelCase_ : Optional[int] =jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="i4" )
lowerCamelCase_ : Any =jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowerCamelCase_ : List[str] =model.decode(
decoder_input_ids[:, :-1] , snake_case__ , decoder_attention_mask=snake_case__ , past_key_values=snake_case__ , decoder_position_ids=snake_case__ , )
lowerCamelCase_ : Union[str, Any] =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
lowerCamelCase_ : str =model.decode(
decoder_input_ids[:, -1:] , snake_case__ , decoder_attention_mask=snake_case__ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=snake_case__ , )
lowerCamelCase_ : Tuple =model.decode(snake_case__ , snake_case__ )
lowerCamelCase_ : List[Any] =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F"""Max diff is {diff}""" )
def UpperCAmelCase__ ( self : List[Any] , snake_case__ : str , snake_case__ : int , snake_case__ : Optional[int] ):
lowerCamelCase_ : str =20
lowerCamelCase_ : str =model_class_name(snake_case__ )
lowerCamelCase_ : Optional[int] =model.encode(inputs_dict["input_ids"] )
lowerCamelCase_ , lowerCamelCase_ : Optional[Any] =(
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
lowerCamelCase_ : List[str] =jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
lowerCamelCase_ : str =model.init_cache(decoder_input_ids.shape[0] , snake_case__ , snake_case__ )
lowerCamelCase_ : List[str] =jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowerCamelCase_ : List[Any] =model.decode(
decoder_input_ids[:, :-1] , snake_case__ , decoder_attention_mask=snake_case__ , past_key_values=snake_case__ , decoder_position_ids=snake_case__ , )
lowerCamelCase_ : Union[str, Any] =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
lowerCamelCase_ : Dict =model.decode(
decoder_input_ids[:, -1:] , snake_case__ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=snake_case__ , decoder_position_ids=snake_case__ , )
lowerCamelCase_ : str =model.decode(snake_case__ , snake_case__ , decoder_attention_mask=snake_case__ )
lowerCamelCase_ : Union[str, Any] =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F"""Max diff is {diff}""" )
def _snake_case ( lowerCamelCase__ : Tuple , lowerCamelCase__ : List[str] , lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[str]=None , lowerCamelCase__ : List[str]=None , ) -> Union[str, Any]:
if attention_mask is None:
lowerCamelCase_ : int =np.not_equal(lowerCamelCase__ , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
lowerCamelCase_ : Optional[Any] =np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class lowercase__ ( snake_case__, unittest.TestCase ):
_UpperCAmelCase :Any = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
_UpperCAmelCase :Optional[Any] = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
_UpperCAmelCase :Tuple = True
_UpperCAmelCase :List[Any] = False
_UpperCAmelCase :List[str] = False
_UpperCAmelCase :int = False
def UpperCAmelCase__ ( self : List[Any] ):
lowerCamelCase_ : List[str] =FlaxPegasusModelTester(self )
lowerCamelCase_ : int =ConfigTester(self , config_class=snake_case__ )
def UpperCAmelCase__ ( self : Optional[int] ):
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self : Tuple ):
lowerCamelCase_ , lowerCamelCase_ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(snake_case__ , snake_case__ , snake_case__ )
def UpperCAmelCase__ ( self : List[Any] ):
lowerCamelCase_ , lowerCamelCase_ : Dict =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(snake_case__ , snake_case__ , snake_case__ )
def UpperCAmelCase__ ( self : Any ):
lowerCamelCase_ , lowerCamelCase_ : Any =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCamelCase_ : Any =self._prepare_for_class(snake_case__ , snake_case__ )
lowerCamelCase_ : List[Any] =model_class(snake_case__ )
@jax.jit
def encode_jitted(snake_case__ : Any , snake_case__ : Any=None , **snake_case__ : int ):
return model.encode(input_ids=snake_case__ , attention_mask=snake_case__ )
with self.subTest("JIT Enabled" ):
lowerCamelCase_ : Any =encode_jitted(**snake_case__ ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
lowerCamelCase_ : List[Any] =encode_jitted(**snake_case__ ).to_tuple()
self.assertEqual(len(snake_case__ ) , len(snake_case__ ) )
for jitted_output, output in zip(snake_case__ , snake_case__ ):
self.assertEqual(jitted_output.shape , output.shape )
def UpperCAmelCase__ ( self : Optional[int] ):
lowerCamelCase_ , lowerCamelCase_ : Optional[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCamelCase_ : List[str] =model_class(snake_case__ )
lowerCamelCase_ : Union[str, Any] =model.encode(inputs_dict["input_ids"] , inputs_dict["attention_mask"] )
lowerCamelCase_ : List[str] ={
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(snake_case__ : Optional[int] , snake_case__ : str , snake_case__ : Any ):
return model.decode(
decoder_input_ids=snake_case__ , decoder_attention_mask=snake_case__ , encoder_outputs=snake_case__ , )
with self.subTest("JIT Enabled" ):
lowerCamelCase_ : Any =decode_jitted(**snake_case__ ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
lowerCamelCase_ : Any =decode_jitted(**snake_case__ ).to_tuple()
self.assertEqual(len(snake_case__ ) , len(snake_case__ ) )
for jitted_output, output in zip(snake_case__ , snake_case__ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def UpperCAmelCase__ ( self : str ):
for model_class_name in self.all_model_classes:
lowerCamelCase_ : List[Any] =model_class_name.from_pretrained("google/pegasus-large" , from_pt=snake_case__ )
lowerCamelCase_ : List[str] =np.ones((1, 1) )
lowerCamelCase_ : List[Any] =model(snake_case__ )
self.assertIsNotNone(snake_case__ )
@slow
def UpperCAmelCase__ ( self : Optional[int] ):
lowerCamelCase_ : Any =FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum" )
lowerCamelCase_ : Any =PegasusTokenizer.from_pretrained("google/pegasus-xsum" )
lowerCamelCase_ : int =[
" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
]
lowerCamelCase_ : int =[
"California's largest electricity provider has turned off power to hundreds of thousands of customers.",
"Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
]
lowerCamelCase_ : List[str] =tokenizer(snake_case__ , return_tensors="np" , truncation=snake_case__ , max_length=512 , padding=snake_case__ )
lowerCamelCase_ : Optional[Any] =model.generate(**snake_case__ , num_beams=2 ).sequences
lowerCamelCase_ : Union[str, Any] =tokenizer.batch_decode(snake_case__ , skip_special_tokens=snake_case__ )
assert tgt_text == decoded
| 209 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def _snake_case ( lowerCamelCase__ : int=None ) -> Union[str, Any]:
if subparsers is not None:
lowerCamelCase_ : List[Any] =subparsers.add_parser("test" )
else:
lowerCamelCase_ : List[str] =argparse.ArgumentParser("Accelerate test command" )
parser.add_argument(
"--config_file" , default=lowerCamelCase__ , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=lowerCamelCase__ )
return parser
def _snake_case ( lowerCamelCase__ : List[Any] ) -> Any:
lowerCamelCase_ : Optional[Any] =os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["test_utils", "scripts", "test_script.py"] )
if args.config_file is None:
lowerCamelCase_ : List[Any] =script_name
else:
lowerCamelCase_ : Union[str, Any] =F"""--config_file={args.config_file} {script_name}"""
lowerCamelCase_ : List[str] =["accelerate-launch"] + test_args.split()
lowerCamelCase_ : Tuple =execute_subprocess_async(lowerCamelCase__ , env=os.environ.copy() )
if result.returncode == 0:
print("Test is a success! You are ready for your distributed training!" )
def _snake_case ( ) -> Tuple:
lowerCamelCase_ : Any =test_command_parser()
lowerCamelCase_ : List[Any] =parser.parse_args()
test_command(lowerCamelCase__ )
if __name__ == "__main__":
main()
| 209 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = filter(lambda SCREAMING_SNAKE_CASE_ : p.requires_grad , model.parameters() )
__lowerCAmelCase = sum([np.prod(p.size() ) for p in model_parameters] )
return params
UpperCamelCase__ = logging.getLogger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any ):
if metric == "rouge2":
__lowerCAmelCase = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
__lowerCAmelCase = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
__lowerCAmelCase = "{val_avg_em:.4f}-{step_count}"
else:
raise NotImplementedError(
F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
" function." )
__lowerCAmelCase = ModelCheckpoint(
dirpath=SCREAMING_SNAKE_CASE_ , filename=SCREAMING_SNAKE_CASE_ , monitor=F"""val_{metric}""" , mode="max" , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
return EarlyStopping(
monitor=F"""val_{metric}""" , mode="min" if "loss" in metric else "max" , patience=SCREAMING_SNAKE_CASE_ , verbose=SCREAMING_SNAKE_CASE_ , )
class a__ ( pl.Callback ):
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A=True ):
"""simple docstring"""
logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
__lowerCAmelCase = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
# Log results
__lowerCAmelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
__lowerCAmelCase = od / "test_results.txt"
__lowerCAmelCase = od / "test_generations.txt"
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
__lowerCAmelCase = od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
__lowerCAmelCase = od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=_A )
generations_file.parent.mkdir(exist_ok=_A )
with open(_A , "a+" ) as writer:
for key in sorted(_A ):
if key in ["log", "progress_bar", "preds"]:
continue
__lowerCAmelCase = metrics[key]
if isinstance(_A , torch.Tensor ):
__lowerCAmelCase = val.item()
__lowerCAmelCase = f"""{key}: {val:.6f}\n"""
writer.write(_A )
if not save_generations:
return
if "preds" in metrics:
__lowerCAmelCase = "\n".join(metrics["preds"] )
generations_file.open("w+" ).write(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
try:
__lowerCAmelCase = pl_module.model.model.num_parameters()
except AttributeError:
__lowerCAmelCase = pl_module.model.num_parameters()
__lowerCAmelCase = count_trainable_parameters(_A )
# mp stands for million parameters
trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1E6, "grad_mp": n_trainable_pars / 1E6} )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_A , _A , "test" )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 92 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece.model""")
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")
UpperCamelCase__ = """pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case__ , unittest.TestCase ):
_a : int = CamembertTokenizer
_a : Dict = CamembertTokenizerFast
_a : Tuple = True
_a : List[Any] = True
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>NOTUSED" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(_A ) , 1_0_0_4 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_5 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
__lowerCAmelCase = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.tokenize(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = {"input_ids": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
__lowerCAmelCase = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name="camembert-base" , revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf" , sequences=_A , )
| 92 | 1 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger('transformers.models.encodec')
__UpperCAmelCase = {
'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
__UpperCAmelCase = {
'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
'encoder.model.13.lstm': 'encoder.layers.13.lstm',
'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
__UpperCAmelCase = {
'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
__UpperCAmelCase = {
'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
'decoder.model.1.lstm': 'decoder.layers.1.lstm',
'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
__UpperCAmelCase = {
'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
__UpperCAmelCase = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
__UpperCAmelCase = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
__UpperCAmelCase = []
__UpperCAmelCase = []
def __UpperCamelCase ( lowercase__ : Optional[Any] , lowercase__ : Tuple , lowercase__ : Optional[int] , lowercase__ : List[str] , lowercase__ : int ) -> List[Any]:
'''simple docstring'''
for attribute in key.split(""".""" ):
lowerCAmelCase_ : Optional[Any] = getattr(lowercase__ , lowercase__ )
if weight_type is not None:
lowerCAmelCase_ : Tuple = getattr(lowercase__ , lowercase__ ).shape
else:
lowerCAmelCase_ : List[Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
lowerCAmelCase_ : Union[str, Any] = value
elif weight_type == "weight_g":
lowerCAmelCase_ : Any = value
elif weight_type == "weight_v":
lowerCAmelCase_ : str = value
elif weight_type == "bias":
lowerCAmelCase_ : Any = value
elif weight_type == "running_mean":
lowerCAmelCase_ : List[str] = value
elif weight_type == "running_var":
lowerCAmelCase_ : Optional[int] = value
elif weight_type == "num_batches_tracked":
lowerCAmelCase_ : List[Any] = value
elif weight_type == "weight_ih_l0":
lowerCAmelCase_ : Optional[int] = value
elif weight_type == "weight_hh_l0":
lowerCAmelCase_ : Tuple = value
elif weight_type == "bias_ih_l0":
lowerCAmelCase_ : int = value
elif weight_type == "bias_hh_l0":
lowerCAmelCase_ : Tuple = value
elif weight_type == "weight_ih_l1":
lowerCAmelCase_ : List[str] = value
elif weight_type == "weight_hh_l1":
lowerCAmelCase_ : int = value
elif weight_type == "bias_ih_l1":
lowerCAmelCase_ : int = value
elif weight_type == "bias_hh_l1":
lowerCAmelCase_ : Optional[int] = value
else:
lowerCAmelCase_ : Any = value
logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def __UpperCamelCase ( lowercase__ : Optional[Any] , lowercase__ : Dict ) -> Tuple:
'''simple docstring'''
for key in ignore_keys:
if key.endswith(""".*""" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = key.split(""".*.""" )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def __UpperCamelCase ( lowercase__ : List[str] , lowercase__ : Optional[Any] , lowercase__ : Optional[int] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : int = []
if model_name == "encodec_24khz" or "encodec_32khz":
lowerCAmelCase_ : Optional[int] = MAPPING_24K
elif model_name == "encodec_48khz":
lowerCAmelCase_ : List[Any] = MAPPING_48K
else:
raise ValueError(f'Unsupported model: {model_name}' )
for name, value in orig_dict.items():
if should_ignore(lowercase__ , lowercase__ ):
logger.info(f'{name} was ignored' )
continue
lowerCAmelCase_ : str = False
for key, mapped_key in MAPPING.items():
if "*" in key:
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = key.split(""".*.""" )
if prefix in name and suffix in name:
lowerCAmelCase_ : Tuple = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith("""embed""" ) and name.endswith("""embed_avg""" ):
continue
lowerCAmelCase_ : str = True
if "*" in mapped_key:
lowerCAmelCase_ : Optional[Any] = name.split(lowercase__ )[0].split(""".""" )[-2]
lowerCAmelCase_ : Optional[Any] = mapped_key.replace("""*""" , lowercase__ )
if "weight_g" in name:
lowerCAmelCase_ : List[str] = """weight_g"""
elif "weight_v" in name:
lowerCAmelCase_ : Optional[int] = """weight_v"""
elif "weight_ih_l0" in name:
lowerCAmelCase_ : Dict = """weight_ih_l0"""
elif "weight_hh_l0" in name:
lowerCAmelCase_ : List[Any] = """weight_hh_l0"""
elif "bias_ih_l0" in name:
lowerCAmelCase_ : int = """bias_ih_l0"""
elif "bias_hh_l0" in name:
lowerCAmelCase_ : Optional[int] = """bias_hh_l0"""
elif "weight_ih_l1" in name:
lowerCAmelCase_ : Union[str, Any] = """weight_ih_l1"""
elif "weight_hh_l1" in name:
lowerCAmelCase_ : Any = """weight_hh_l1"""
elif "bias_ih_l1" in name:
lowerCAmelCase_ : Dict = """bias_ih_l1"""
elif "bias_hh_l1" in name:
lowerCAmelCase_ : List[str] = """bias_hh_l1"""
elif "bias" in name:
lowerCAmelCase_ : Tuple = """bias"""
elif "weight" in name:
lowerCAmelCase_ : List[str] = """weight"""
elif "running_mean" in name:
lowerCAmelCase_ : Optional[Any] = """running_mean"""
elif "running_var" in name:
lowerCAmelCase_ : int = """running_var"""
elif "num_batches_tracked" in name:
lowerCAmelCase_ : Tuple = """num_batches_tracked"""
else:
lowerCAmelCase_ : Any = None
set_recursively(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
continue
if not is_used:
unused_weights.append(lowercase__ )
logger.warning(f'Unused weights: {unused_weights}' )
@torch.no_grad()
def __UpperCamelCase ( lowercase__ : Dict , lowercase__ : List[str] , lowercase__ : Union[str, Any] , lowercase__ : Tuple=None , lowercase__ : str=None , ) -> Optional[int]:
'''simple docstring'''
if config_path is not None:
lowerCAmelCase_ : List[str] = EncodecConfig.from_pretrained(lowercase__ )
else:
lowerCAmelCase_ : Any = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
lowerCAmelCase_ : List[str] = [8, 5, 4, 4]
lowerCAmelCase_ : int = [2.2]
lowerCAmelCase_ : List[str] = 64
lowerCAmelCase_ : Optional[int] = 32000
lowerCAmelCase_ : Tuple = 2048
lowerCAmelCase_ : Optional[int] = False
lowerCAmelCase_ : Any = False
lowerCAmelCase_ : int = False
elif model_name == "encodec_48khz":
lowerCAmelCase_ : Optional[int] = [8, 5, 4, 2]
lowerCAmelCase_ : Optional[Any] = [3.0, 6.0, 12.0, 24.0]
lowerCAmelCase_ : Optional[int] = 48000
lowerCAmelCase_ : str = 2
lowerCAmelCase_ : List[str] = False
lowerCAmelCase_ : str = """time_group_norm"""
lowerCAmelCase_ : int = True
lowerCAmelCase_ : Optional[Any] = 1.0
lowerCAmelCase_ : Optional[Any] = 0.01
else:
raise ValueError(f'Unknown model name: {model_name}' )
lowerCAmelCase_ : Dict = EncodecModel(lowercase__ )
lowerCAmelCase_ : Tuple = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(lowercase__ )
lowerCAmelCase_ : int = torch.load(lowercase__ )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
lowerCAmelCase_ : Optional[Any] = original_checkpoint["""best_state"""]
recursively_load_weights(lowercase__ , lowercase__ , lowercase__ )
model.save_pretrained(lowercase__ )
if repo_id:
print("""Pushing to the hub...""" )
feature_extractor.push_to_hub(lowercase__ )
model.push_to_hub(lowercase__ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--model',
default='encodec_24khz',
type=str,
help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
)
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
__UpperCAmelCase = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 28 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger('transformers.models.speecht5')
def __UpperCamelCase ( lowercase__ : Optional[Any] , lowercase__ : Optional[Any] , lowercase__ : str ) -> List[str]:
'''simple docstring'''
hf_model.apply_weight_norm()
lowerCAmelCase_ : Dict = checkpoint["""input_conv.weight_g"""]
lowerCAmelCase_ : Any = checkpoint["""input_conv.weight_v"""]
lowerCAmelCase_ : Any = checkpoint["""input_conv.bias"""]
for i in range(len(config.upsample_rates ) ):
lowerCAmelCase_ : Tuple = checkpoint[f'upsamples.{i}.1.weight_g']
lowerCAmelCase_ : Any = checkpoint[f'upsamples.{i}.1.weight_v']
lowerCAmelCase_ : int = checkpoint[f'upsamples.{i}.1.bias']
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
lowerCAmelCase_ : Dict = checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
lowerCAmelCase_ : Dict = checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
lowerCAmelCase_ : Tuple = checkpoint[f'blocks.{i}.convs1.{j}.1.bias']
lowerCAmelCase_ : str = checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
lowerCAmelCase_ : Optional[Any] = checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
lowerCAmelCase_ : str = checkpoint[f'blocks.{i}.convs2.{j}.1.bias']
lowerCAmelCase_ : str = checkpoint["""output_conv.1.weight_g"""]
lowerCAmelCase_ : Dict = checkpoint["""output_conv.1.weight_v"""]
lowerCAmelCase_ : Optional[int] = checkpoint["""output_conv.1.bias"""]
hf_model.remove_weight_norm()
@torch.no_grad()
def __UpperCamelCase ( lowercase__ : str , lowercase__ : Tuple , lowercase__ : Dict , lowercase__ : List[Any]=None , lowercase__ : Union[str, Any]=None , ) -> List[Any]:
'''simple docstring'''
if config_path is not None:
lowerCAmelCase_ : Optional[Any] = SpeechTaHifiGanConfig.from_pretrained(lowercase__ )
else:
lowerCAmelCase_ : Any = SpeechTaHifiGanConfig()
lowerCAmelCase_ : str = SpeechTaHifiGan(lowercase__ )
lowerCAmelCase_ : Tuple = torch.load(lowercase__ )
load_weights(orig_checkpoint["""model"""]["""generator"""] , lowercase__ , lowercase__ )
lowerCAmelCase_ : Optional[int] = np.load(lowercase__ )
lowerCAmelCase_ : Any = stats[0].reshape(-1 )
lowerCAmelCase_ : List[str] = stats[1].reshape(-1 )
lowerCAmelCase_ : Optional[int] = torch.from_numpy(lowercase__ ).float()
lowerCAmelCase_ : Any = torch.from_numpy(lowercase__ ).float()
model.save_pretrained(lowercase__ )
if repo_id:
print("""Pushing to the hub...""" )
model.push_to_hub(lowercase__ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
__UpperCAmelCase = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 28 | 1 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase : int = 0
__UpperCamelCase : int = [
[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],
]
__UpperCamelCase : List[str] = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
__UpperCamelCase : Optional[Any] = tuple[int, int]
class lowercase__ :
def __init__( self : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : Node | None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = pos_x
SCREAMING_SNAKE_CASE : Optional[Any] = pos_y
SCREAMING_SNAKE_CASE : Dict = (pos_y, pos_x)
SCREAMING_SNAKE_CASE : Tuple = goal_x
SCREAMING_SNAKE_CASE : Optional[int] = goal_y
SCREAMING_SNAKE_CASE : Dict = g_cost
SCREAMING_SNAKE_CASE : Dict = parent
SCREAMING_SNAKE_CASE : List[str] = self.calculate_heuristic()
SCREAMING_SNAKE_CASE : Union[str, Any] = self.g_cost + self.h_cost
def __A ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.pos_x - self.goal_x
SCREAMING_SNAKE_CASE : Dict = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(UpperCamelCase__ ) + abs(UpperCamelCase__ )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self : Optional[int] , UpperCamelCase__ : Node ):
'''simple docstring'''
return self.f_cost < other.f_cost
class lowercase__ :
def __init__( self : Optional[Any] , UpperCamelCase__ : TPosition , UpperCamelCase__ : TPosition ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , UpperCamelCase__ )
SCREAMING_SNAKE_CASE : str = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9999 , UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = [self.start]
SCREAMING_SNAKE_CASE : list[Node] = []
SCREAMING_SNAKE_CASE : Tuple = False
def __A ( self : Tuple ):
'''simple docstring'''
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
SCREAMING_SNAKE_CASE : Union[str, Any] = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(UpperCamelCase__ )
self.closed_nodes.append(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Tuple = self.get_successors(UpperCamelCase__ )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(UpperCamelCase__ )
else:
# retrieve the best current path
SCREAMING_SNAKE_CASE : int = self.open_nodes.pop(self.open_nodes.index(UpperCamelCase__ ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(UpperCamelCase__ )
else:
self.open_nodes.append(UpperCamelCase__ )
return [self.start.pos]
def __A ( self : List[Any] , UpperCamelCase__ : Node ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = []
for action in delta:
SCREAMING_SNAKE_CASE : Union[str, Any] = parent.pos_x + action[1]
SCREAMING_SNAKE_CASE : Optional[int] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(UpperCamelCase__ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
UpperCamelCase__ , UpperCamelCase__ , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , UpperCamelCase__ , ) )
return successors
def __A ( self : str , UpperCamelCase__ : Node | None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = node
SCREAMING_SNAKE_CASE : Any = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
SCREAMING_SNAKE_CASE : Optional[int] = current_node.parent
path.reverse()
return path
class lowercase__ :
def __init__( self : List[Any] , UpperCamelCase__ : TPosition , UpperCamelCase__ : TPosition ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = AStar(UpperCamelCase__ , UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[int] = AStar(UpperCamelCase__ , UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Dict = False
def __A ( self : Any ):
'''simple docstring'''
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
SCREAMING_SNAKE_CASE : Tuple = self.fwd_astar.open_nodes.pop(0 )
SCREAMING_SNAKE_CASE : int = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
UpperCamelCase__ , UpperCamelCase__ )
self.fwd_astar.closed_nodes.append(UpperCamelCase__ )
self.bwd_astar.closed_nodes.append(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Any = current_bwd_node
SCREAMING_SNAKE_CASE : Union[str, Any] = current_fwd_node
SCREAMING_SNAKE_CASE : Optional[Any] = {
self.fwd_astar: self.fwd_astar.get_successors(UpperCamelCase__ ),
self.bwd_astar: self.bwd_astar.get_successors(UpperCamelCase__ ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(UpperCamelCase__ )
else:
# retrieve the best current path
SCREAMING_SNAKE_CASE : List[str] = astar.open_nodes.pop(
astar.open_nodes.index(UpperCamelCase__ ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(UpperCamelCase__ )
else:
astar.open_nodes.append(UpperCamelCase__ )
return [self.fwd_astar.start.pos]
def __A ( self : Union[str, Any] , UpperCamelCase__ : Node , UpperCamelCase__ : Node ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.fwd_astar.retrace_path(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.bwd_astar.retrace_path(UpperCamelCase__ )
bwd_path.pop()
bwd_path.reverse()
SCREAMING_SNAKE_CASE : Any = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
__UpperCamelCase : int = (0, 0)
__UpperCamelCase : Dict = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
__UpperCamelCase : Tuple = time.time()
__UpperCamelCase : Dict = AStar(init, goal)
__UpperCamelCase : Tuple = a_star.search()
__UpperCamelCase : str = time.time() - start_time
print(f"""AStar execution time = {end_time:f} seconds""")
__UpperCamelCase : str = time.time()
__UpperCamelCase : Dict = BidirectionalAStar(init, goal)
__UpperCamelCase : Dict = time.time() - bd_start_time
print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")
| 182 | import itertools
import string
from collections.abc import Generator, Iterable
def A ( _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = iter(_lowercase )
while True:
SCREAMING_SNAKE_CASE : Optional[Any] = tuple(itertools.islice(_lowercase , _lowercase ) )
if not chunk:
return
yield chunk
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : int = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
SCREAMING_SNAKE_CASE : List[str] = ''''''
if len(_lowercase ) < 2:
return dirty
for i in range(len(_lowercase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_lowercase ) & 1:
clean += "X"
return clean
def A ( _lowercase ):
# I and J are used interchangeably to allow
# us to use a 5x5 table (25 letters)
SCREAMING_SNAKE_CASE : Optional[Any] = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
SCREAMING_SNAKE_CASE : List[Any] = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_lowercase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_lowercase )
return table
def A ( _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Any = generate_table(_lowercase )
SCREAMING_SNAKE_CASE : Any = prepare_input(_lowercase )
SCREAMING_SNAKE_CASE : Union[str, Any] = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = divmod(table.index(_lowercase ) , 5 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def A ( _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Any = generate_table(_lowercase )
SCREAMING_SNAKE_CASE : List[Any] = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_lowercase , 2 ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = divmod(table.index(_lowercase ) , 5 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = divmod(table.index(_lowercase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 182 | 1 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
lowerCAmelCase__ = re.compile(r'\s+')
def __lowerCamelCase ( lowerCAmelCase__ ):
return {"hash": hashlib.mda(re.sub(lowerCAmelCase__ , '' , example['content'] ).encode('utf-8' ) ).hexdigest()}
def __lowerCamelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = [len(lowerCAmelCase__ ) for line in example['content'].splitlines()]
return {"line_mean": np.mean(lowerCAmelCase__ ), "line_max": max(lowerCAmelCase__ )}
def __lowerCamelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = np.mean([c.isalnum() for c in example['content']] )
return {"alpha_frac": alpha_frac}
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
if example["hash"] in uniques:
uniques.remove(example['hash'] )
return True
else:
return False
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__=5 ):
lowerCAmelCase__ = ['auto-generated', 'autogenerated', 'automatically generated']
lowerCAmelCase__ = example['content'].splitlines()
for _, line in zip(range(lowerCAmelCase__ ) , lowerCAmelCase__ ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__=5 , lowerCAmelCase__=0.0_5 ):
lowerCAmelCase__ = ['unit tests', 'test file', 'configuration file']
lowerCAmelCase__ = example['content'].splitlines()
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
# first test
for _, line in zip(range(lowerCAmelCase__ ) , lowerCAmelCase__ ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
lowerCAmelCase__ = example['content'].count('\n' )
lowerCAmelCase__ = int(coeff * nlines )
for line in lines:
count_config += line.lower().count('config' )
count_test += line.lower().count('test' )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def __lowerCamelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = ['def ', 'class ', 'for ', 'while ']
lowerCAmelCase__ = example['content'].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__=4 ):
lowerCAmelCase__ = example['content'].splitlines()
lowerCAmelCase__ = 0
for line in lines:
counter += line.lower().count('=' )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def __lowerCamelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = tokenizer(example['content'] , truncation=lowerCAmelCase__ )['input_ids']
lowerCAmelCase__ = len(example['content'] ) / len(lowerCAmelCase__ )
return {"ratio": ratio}
def __lowerCamelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = {}
results.update(get_hash(lowerCAmelCase__ ) )
results.update(line_stats(lowerCAmelCase__ ) )
results.update(alpha_stats(lowerCAmelCase__ ) )
results.update(char_token_ratio(lowerCAmelCase__ ) )
results.update(is_autogenerated(lowerCAmelCase__ ) )
results.update(is_config_or_test(lowerCAmelCase__ ) )
results.update(has_no_keywords(lowerCAmelCase__ ) )
results.update(has_few_assignments(lowerCAmelCase__ ) )
return results
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
if not check_uniques(lowerCAmelCase__ , lowerCAmelCase__ ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def __lowerCamelCase ( lowerCAmelCase__ ):
with open(lowerCAmelCase__ , 'rb' ) as f_in:
with gzip.open(str(lowerCAmelCase__ ) + '.gz' , 'wb' , compresslevel=6 ) as f_out:
shutil.copyfileobj(lowerCAmelCase__ , lowerCAmelCase__ )
os.unlink(lowerCAmelCase__ )
# Settings
lowerCAmelCase__ = HfArgumentParser(PreprocessingArguments)
lowerCAmelCase__ = parser.parse_args()
if args.num_workers is None:
lowerCAmelCase__ = multiprocessing.cpu_count()
lowerCAmelCase__ = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
lowerCAmelCase__ = time.time()
lowerCAmelCase__ = load_dataset(args.dataset_name, split='train')
print(F'Time to load dataset: {time.time()-t_start:.2f}')
# Run preprocessing
lowerCAmelCase__ = time.time()
lowerCAmelCase__ = ds.map(preprocess, num_proc=args.num_workers)
print(F'Time to preprocess dataset: {time.time()-t_start:.2f}')
# Deduplicate hashes
lowerCAmelCase__ = set(ds.unique('hash'))
lowerCAmelCase__ = len(uniques) / len(ds)
print(F'Fraction of duplicates: {1-frac:.2%}')
# Deduplicate data and apply heuristics
lowerCAmelCase__ = time.time()
lowerCAmelCase__ = ds.filter(filter, fn_kwargs={'uniques': uniques, 'args': args})
print(F'Time to filter dataset: {time.time()-t_start:.2f}')
print(F'Size of filtered dataset: {len(ds_filter)}')
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
lowerCAmelCase__ = time.time()
lowerCAmelCase__ , lowerCAmelCase__ = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F'Time to deduplicate dataset: {time.time()-t_start:.2f}')
print(F'Size of deduplicate dataset: {len(ds_filter)}')
# Save data in batches of samples_per_file
lowerCAmelCase__ = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / 'duplicate_clusters.json', 'w') as f:
json.dump(duplicate_clusters, f)
lowerCAmelCase__ = output_dir / 'data'
data_dir.mkdir(exist_ok=True)
lowerCAmelCase__ = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
lowerCAmelCase__ = str(data_dir / F'file-{file_number+1:012}.json')
lowerCAmelCase__ = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F'Time to save dataset: {time.time()-t_start:.2f}')
| 360 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowerCAmelCase__ = logging.get_logger(__name__)
class a_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCAmelCase_ = ['pixel_values']
def __init__( self : Tuple , lowercase__ : bool = True , lowercase__ : Dict[str, int] = None , lowercase__ : PILImageResampling = PILImageResampling.BICUBIC , lowercase__ : bool = True , lowercase__ : Union[int, float] = 1 / 255 , lowercase__ : bool = True , lowercase__ : Optional[Union[float, List[float]]] = None , lowercase__ : Optional[Union[float, List[float]]] = None , lowercase__ : bool = True , **lowercase__ : List[Any] , ):
'''simple docstring'''
super().__init__(**lowercase__)
lowerCAmelCase__ = size if size is not None else {'height': 384, 'width': 384}
lowerCAmelCase__ = get_size_dict(lowercase__ , default_to_square=lowercase__)
lowerCAmelCase__ = do_resize
lowerCAmelCase__ = size
lowerCAmelCase__ = resample
lowerCAmelCase__ = do_rescale
lowerCAmelCase__ = rescale_factor
lowerCAmelCase__ = do_normalize
lowerCAmelCase__ = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
lowerCAmelCase__ = image_std if image_std is not None else OPENAI_CLIP_STD
lowerCAmelCase__ = do_convert_rgb
def __snake_case ( self : List[str] , lowercase__ : np.ndarray , lowercase__ : Dict[str, int] , lowercase__ : PILImageResampling = PILImageResampling.BICUBIC , lowercase__ : Optional[Union[str, ChannelDimension]] = None , **lowercase__ : Dict , ):
'''simple docstring'''
lowerCAmelCase__ = get_size_dict(lowercase__ , default_to_square=lowercase__)
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(lowercase__ , size=lowercase__ , resample=lowercase__ , data_format=lowercase__ , **lowercase__)
def __snake_case ( self : List[str] , lowercase__ : np.ndarray , lowercase__ : Union[int, float] , lowercase__ : Optional[Union[str, ChannelDimension]] = None , **lowercase__ : Union[str, Any] , ):
'''simple docstring'''
return rescale(lowercase__ , scale=lowercase__ , data_format=lowercase__ , **lowercase__)
def __snake_case ( self : Optional[Any] , lowercase__ : np.ndarray , lowercase__ : Union[float, List[float]] , lowercase__ : Union[float, List[float]] , lowercase__ : Optional[Union[str, ChannelDimension]] = None , **lowercase__ : Any , ):
'''simple docstring'''
return normalize(lowercase__ , mean=lowercase__ , std=lowercase__ , data_format=lowercase__ , **lowercase__)
def __snake_case ( self : Any , lowercase__ : ImageInput , lowercase__ : Optional[bool] = None , lowercase__ : Optional[Dict[str, int]] = None , lowercase__ : PILImageResampling = None , lowercase__ : Optional[bool] = None , lowercase__ : Optional[float] = None , lowercase__ : Optional[bool] = None , lowercase__ : Optional[Union[float, List[float]]] = None , lowercase__ : Optional[Union[float, List[float]]] = None , lowercase__ : Optional[Union[str, TensorType]] = None , lowercase__ : bool = None , lowercase__ : ChannelDimension = ChannelDimension.FIRST , **lowercase__ : Dict , ):
'''simple docstring'''
lowerCAmelCase__ = do_resize if do_resize is not None else self.do_resize
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__ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowerCAmelCase__ = size if size is not None else self.size
lowerCAmelCase__ = get_size_dict(lowercase__ , default_to_square=lowercase__)
lowerCAmelCase__ = make_list_of_images(lowercase__)
if not valid_images(lowercase__):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.')
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.')
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.')
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.')
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowerCAmelCase__ = [convert_to_rgb(lowercase__) for image in images]
# All transformations expect numpy arrays.
lowerCAmelCase__ = [to_numpy_array(lowercase__) for image in images]
if do_resize:
lowerCAmelCase__ = [self.resize(image=lowercase__ , size=lowercase__ , resample=lowercase__) for image in images]
if do_rescale:
lowerCAmelCase__ = [self.rescale(image=lowercase__ , scale=lowercase__) for image in images]
if do_normalize:
lowerCAmelCase__ = [self.normalize(image=lowercase__ , mean=lowercase__ , std=lowercase__) for image in images]
lowerCAmelCase__ = [to_channel_dimension_format(lowercase__ , lowercase__) for image in images]
lowerCAmelCase__ = BatchFeature(data={'pixel_values': images} , tensor_type=lowercase__)
return encoded_outputs
| 119 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCAmelCase_ =["pixel_values"]
def __init__( self , _A = True , _A = None , _A = PILImageResampling.BICUBIC , _A = True , _A = True , _A = 1 / 255 , _A = None , _A = True , _A = None , _A = None , **_A , ) -> None:
super().__init__(**_A )
SCREAMING_SNAKE_CASE_ = size if size is not None else {'''height''': 224, '''width''': 224}
SCREAMING_SNAKE_CASE_ = get_size_dict(_A )
SCREAMING_SNAKE_CASE_ = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
SCREAMING_SNAKE_CASE_ = get_size_dict(_A , default_to_square=_A , param_name='''crop_size''' )
SCREAMING_SNAKE_CASE_ = do_resize
SCREAMING_SNAKE_CASE_ = do_rescale
SCREAMING_SNAKE_CASE_ = do_normalize
SCREAMING_SNAKE_CASE_ = do_center_crop
SCREAMING_SNAKE_CASE_ = crop_size
SCREAMING_SNAKE_CASE_ = size
SCREAMING_SNAKE_CASE_ = resample
SCREAMING_SNAKE_CASE_ = rescale_factor
SCREAMING_SNAKE_CASE_ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
SCREAMING_SNAKE_CASE_ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def _UpperCamelCase ( self , _A , _A , _A = PILImageResampling.BILINEAR , _A = None , **_A , ) -> np.ndarray:
SCREAMING_SNAKE_CASE_ = get_size_dict(_A )
if "shortest_edge" in size:
SCREAMING_SNAKE_CASE_ = get_resize_output_image_size(_A , size=size['''shortest_edge'''] , default_to_square=_A )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
SCREAMING_SNAKE_CASE_ = (size['''height'''], size['''width'''])
else:
raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def _UpperCamelCase ( self , _A , _A , _A = None , **_A , ) -> np.ndarray:
SCREAMING_SNAKE_CASE_ = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(_A , size=(size['''height'''], size['''width''']) , data_format=_A , **_A )
def _UpperCamelCase ( self , _A , _A , _A = None , **_A ) -> np.ndarray:
return rescale(_A , scale=_A , data_format=_A , **_A )
def _UpperCamelCase ( self , _A , _A , _A , _A = None , **_A , ) -> np.ndarray:
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def _UpperCamelCase ( self , _A , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = ChannelDimension.FIRST , **_A , ) -> BatchFeature:
SCREAMING_SNAKE_CASE_ = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE_ = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE_ = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE_ = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE_ = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE_ = get_size_dict(_A , param_name='''crop_size''' , default_to_square=_A )
SCREAMING_SNAKE_CASE_ = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE_ = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE_ = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE_ = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE_ = size if size is not None else self.size
SCREAMING_SNAKE_CASE_ = get_size_dict(_A )
if not is_batched(_A ):
SCREAMING_SNAKE_CASE_ = [images]
if not valid_images(_A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE_ = [to_numpy_array(_A ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE_ = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE_ = [self.center_crop(image=_A , size=_A ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE_ = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE_ = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
SCREAMING_SNAKE_CASE_ = [to_channel_dimension_format(_A , _A ) for image in images]
SCREAMING_SNAKE_CASE_ = {'''pixel_values''': images}
return BatchFeature(data=_A , tensor_type=_A )
| 299 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _A , _A=3 , _A=32 , _A=3 , _A=10 , _A=[10, 20, 30, 40] , _A=[1, 1, 2, 1] , _A=True , _A=True , _A="relu" , _A=3 , _A=None , ) -> Tuple:
SCREAMING_SNAKE_CASE_ = parent
SCREAMING_SNAKE_CASE_ = batch_size
SCREAMING_SNAKE_CASE_ = image_size
SCREAMING_SNAKE_CASE_ = num_channels
SCREAMING_SNAKE_CASE_ = embeddings_size
SCREAMING_SNAKE_CASE_ = hidden_sizes
SCREAMING_SNAKE_CASE_ = depths
SCREAMING_SNAKE_CASE_ = is_training
SCREAMING_SNAKE_CASE_ = use_labels
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = num_labels
SCREAMING_SNAKE_CASE_ = scope
SCREAMING_SNAKE_CASE_ = len(_A )
def _UpperCamelCase ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ = self.get_config()
return config, pixel_values
def _UpperCamelCase ( self ) -> Optional[Any]:
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def _UpperCamelCase ( self , _A , _A ) -> int:
SCREAMING_SNAKE_CASE_ = FlaxRegNetModel(config=_A )
SCREAMING_SNAKE_CASE_ = model(_A )
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _UpperCamelCase ( self , _A , _A ) -> Any:
SCREAMING_SNAKE_CASE_ = self.num_labels
SCREAMING_SNAKE_CASE_ = FlaxRegNetForImageClassification(config=_A )
SCREAMING_SNAKE_CASE_ = model(_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _UpperCamelCase ( self ) -> Any:
SCREAMING_SNAKE_CASE_ = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = config_and_inputs
SCREAMING_SNAKE_CASE_ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class UpperCamelCase__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ =(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
UpperCAmelCase_ =False
UpperCAmelCase_ =False
UpperCAmelCase_ =False
def _UpperCamelCase ( self ) -> None:
SCREAMING_SNAKE_CASE_ = FlaxRegNetModelTester(self )
SCREAMING_SNAKE_CASE_ = ConfigTester(self , config_class=_A , has_text_modality=_A )
def _UpperCamelCase ( self ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _UpperCamelCase ( self ) -> str:
return
def _UpperCamelCase ( self ) -> List[str]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def _UpperCamelCase ( self ) -> str:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def _UpperCamelCase ( self ) -> int:
pass
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def _UpperCamelCase ( self ) -> Dict:
pass
def _UpperCamelCase ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ = model_class(_A )
SCREAMING_SNAKE_CASE_ = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE_ = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE_ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _A )
def _UpperCamelCase ( self ) -> Any:
def check_hidden_states_output(_A , _A , _A ):
SCREAMING_SNAKE_CASE_ = model_class(_A )
SCREAMING_SNAKE_CASE_ = model(**self._prepare_for_class(_A , _A ) )
SCREAMING_SNAKE_CASE_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
SCREAMING_SNAKE_CASE_ = self.model_tester.num_stages
self.assertEqual(len(_A ) , expected_num_stages + 1 )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ = True
check_hidden_states_output(_A , _A , _A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE_ = True
check_hidden_states_output(_A , _A , _A )
def _UpperCamelCase ( self ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE_ = self._prepare_for_class(_A , _A )
SCREAMING_SNAKE_CASE_ = model_class(_A )
@jax.jit
def model_jitted(_A , **_A ):
return model(pixel_values=_A , **_A )
with self.subTest('''JIT Enabled''' ):
SCREAMING_SNAKE_CASE_ = model_jitted(**_A ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE_ = model_jitted(**_A ).to_tuple()
self.assertEqual(len(_A ) , len(_A ) )
for jitted_output, output in zip(_A , _A ):
self.assertEqual(jitted_output.shape , output.shape )
def A__ ( ):
SCREAMING_SNAKE_CASE_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_flax
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _UpperCamelCase ( self ) -> Optional[int]:
return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None
@slow
def _UpperCamelCase ( self ) -> int:
SCREAMING_SNAKE_CASE_ = FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=_A , return_tensors='''np''' )
SCREAMING_SNAKE_CASE_ = model(**_A )
# verify the logits
SCREAMING_SNAKE_CASE_ = (1, 1000)
self.assertEqual(outputs.logits.shape , _A )
SCREAMING_SNAKE_CASE_ = jnp.array([-0.4180, -1.5051, -3.4836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , _A , atol=1E-4 ) )
| 299 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase = {
'''configuration_owlvit''': [
'''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''OwlViTConfig''',
'''OwlViTOnnxConfig''',
'''OwlViTTextConfig''',
'''OwlViTVisionConfig''',
],
'''processing_owlvit''': ['''OwlViTProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ['''OwlViTFeatureExtractor''']
UpperCAmelCase = ['''OwlViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''OwlViTModel''',
'''OwlViTPreTrainedModel''',
'''OwlViTTextModel''',
'''OwlViTVisionModel''',
'''OwlViTForObjectDetection''',
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 172 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}
class __magic_name__ ( __UpperCAmelCase ):
__A : Tuple = "layoutlmv3"
def __init__( self : int , snake_case__ : Any=5_0_2_6_5 , snake_case__ : int=7_6_8 , snake_case__ : Dict=1_2 , snake_case__ : Optional[Any]=1_2 , snake_case__ : Union[str, Any]=3_0_7_2 , snake_case__ : Tuple="gelu" , snake_case__ : List[str]=0.1 , snake_case__ : List[str]=0.1 , snake_case__ : int=5_1_2 , snake_case__ : int=2 , snake_case__ : Optional[int]=0.02 , snake_case__ : Union[str, Any]=1e-5 , snake_case__ : Optional[int]=1 , snake_case__ : Any=0 , snake_case__ : Optional[int]=2 , snake_case__ : int=1_0_2_4 , snake_case__ : str=1_2_8 , snake_case__ : Tuple=1_2_8 , snake_case__ : Optional[Any]=True , snake_case__ : Union[str, Any]=3_2 , snake_case__ : Any=1_2_8 , snake_case__ : List[Any]=6_4 , snake_case__ : List[Any]=2_5_6 , snake_case__ : Any=True , snake_case__ : Optional[Any]=True , snake_case__ : Tuple=True , snake_case__ : List[Any]=2_2_4 , snake_case__ : Optional[int]=3 , snake_case__ : Union[str, Any]=1_6 , snake_case__ : str=None , **snake_case__ : List[str] , ):
'''simple docstring'''
super().__init__(
vocab_size=snake_case__ , hidden_size=snake_case__ , num_hidden_layers=snake_case__ , num_attention_heads=snake_case__ , intermediate_size=snake_case__ , hidden_act=snake_case__ , hidden_dropout_prob=snake_case__ , attention_probs_dropout_prob=snake_case__ , max_position_embeddings=snake_case__ , type_vocab_size=snake_case__ , initializer_range=snake_case__ , layer_norm_eps=snake_case__ , pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ , )
lowercase :Optional[int] = max_ad_position_embeddings
lowercase :Tuple = coordinate_size
lowercase :Any = shape_size
lowercase :Union[str, Any] = has_relative_attention_bias
lowercase :Optional[Any] = rel_pos_bins
lowercase :Tuple = max_rel_pos
lowercase :Any = has_spatial_attention_bias
lowercase :Any = rel_ad_pos_bins
lowercase :str = max_rel_ad_pos
lowercase :int = text_embed
lowercase :Optional[int] = visual_embed
lowercase :str = input_size
lowercase :List[str] = num_channels
lowercase :str = patch_size
lowercase :Any = classifier_dropout
class __magic_name__ ( __UpperCAmelCase ):
__A : Tuple = version.parse("1.12" )
@property
def __snake_case ( self : Any ):
'''simple docstring'''
if self.task in ["question-answering", "sequence-classification"]:
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
('''bbox''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
else:
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''bbox''', {0: '''batch''', 1: '''sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
] )
@property
def __snake_case ( self : int ):
'''simple docstring'''
return 1e-5
@property
def __snake_case ( self : Union[str, Any] ):
'''simple docstring'''
return 1_2
def __snake_case ( self : str , snake_case__ : "ProcessorMixin" , snake_case__ : int = -1 , snake_case__ : int = -1 , snake_case__ : bool = False , snake_case__ : Optional["TensorType"] = None , snake_case__ : int = 3 , snake_case__ : int = 4_0 , snake_case__ : int = 4_0 , ):
'''simple docstring'''
setattr(processor.image_processor , '''apply_ocr''' , snake_case__ )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase :Dict = compute_effective_axis_dimension(
snake_case__ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase :Union[str, Any] = processor.tokenizer.num_special_tokens_to_add(snake_case__ )
lowercase :List[str] = compute_effective_axis_dimension(
snake_case__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case__ )
# Generate dummy inputs according to compute batch and sequence
lowercase :Tuple = [[''' '''.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
lowercase :List[str] = [[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
lowercase :List[Any] = self._generate_dummy_images(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
lowercase :Dict = dict(
processor(
snake_case__ , text=snake_case__ , boxes=snake_case__ , return_tensors=snake_case__ , ) )
return inputs
| 172 | 1 |
"""simple docstring"""
def A__ ( UpperCamelCase = 100 ):
A = n * (n + 1) * (2 * n + 1) / 6
A = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 292 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _UpperCAmelCase :
UpperCamelCase = None
def lowerCamelCase ( self :List[Any] ):
A = self.feature_extraction_class(**self.feat_extract_dict )
A = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __UpperCamelCase )
def lowerCamelCase ( self :Dict ):
A = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
A = os.path.join(__UpperCamelCase , "feat_extract.json" )
feat_extract_first.to_json_file(__UpperCamelCase )
A = self.feature_extraction_class.from_json_file(__UpperCamelCase )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase ( self :Dict ):
A = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
A = feat_extract_first.save_pretrained(__UpperCamelCase )[0]
check_json_file_has_correct_format(__UpperCamelCase )
A = self.feature_extraction_class.from_pretrained(__UpperCamelCase )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase ( self :Tuple ):
A = self.feature_extraction_class()
self.assertIsNotNone(__UpperCamelCase )
| 292 | 1 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a_ ( _lowercase , _lowercase , _lowercase , _lowercase ):
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_UpperCamelCase : Any = np.full((len(__lowerCAmelCase ), sequence_length, 2) , __lowerCAmelCase )
else:
_UpperCamelCase : Optional[int] = np.full((len(__lowerCAmelCase ), sequence_length) , __lowerCAmelCase )
for i, tensor in enumerate(__lowerCAmelCase ):
if padding_side == "right":
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_UpperCamelCase : Tuple = tensor[:sequence_length]
else:
_UpperCamelCase : Any = tensor[:sequence_length]
else:
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_UpperCamelCase : List[Any] = tensor[:sequence_length]
else:
_UpperCamelCase : List[Any] = tensor[:sequence_length]
return out_tensor.tolist()
def a_ ( _lowercase ):
_UpperCamelCase : Any = ord(__lowerCAmelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
_UpperCamelCase : List[Any] = unicodedata.category(__lowerCAmelCase )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class _a ( UpperCamelCase_ ):
UpperCamelCase = 42
UpperCamelCase = True
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = -100
UpperCamelCase = "pt"
def snake_case ( self : Tuple, lowerCAmelCase__ : Any ) -> int:
'''simple docstring'''
import torch
_UpperCamelCase : Tuple = """label""" if """label""" in features[0].keys() else """labels"""
_UpperCamelCase : int = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
_UpperCamelCase : List[str] = self.tokenizer.pad(
_a, padding=self.padding, max_length=self.max_length, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors='''pt''' if labels is None else None, )
if labels is None:
return batch
_UpperCamelCase : List[Any] = torch.tensor(batch['''entity_ids'''] ).shape[1]
_UpperCamelCase : Dict = self.tokenizer.padding_side
if padding_side == "right":
_UpperCamelCase : Any = [
list(_a ) + [self.label_pad_token_id] * (sequence_length - len(_a )) for label in labels
]
else:
_UpperCamelCase : List[str] = [
[self.label_pad_token_id] * (sequence_length - len(_a )) + list(_a ) for label in labels
]
_UpperCamelCase : List[str] = [feature["""ner_tags"""] for feature in features]
_UpperCamelCase : List[str] = padding_tensor(_a, -1, _a, _a )
_UpperCamelCase : Tuple = [feature["""original_entity_spans"""] for feature in features]
_UpperCamelCase : Tuple = padding_tensor(_a, (-1, -1), _a, _a )
_UpperCamelCase : Optional[Any] = {k: torch.tensor(_a, dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 362 |
"""simple docstring"""
def a_ ( _lowercase , _lowercase ):
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty''' )
_UpperCamelCase : Optional[int] = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_lowercase ) )
return round(_lowercase , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 128 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase :List[Any] = "bert-generation"
def __init__( self , _UpperCAmelCase=50358 , _UpperCAmelCase=1024 , _UpperCAmelCase=24 , _UpperCAmelCase=16 , _UpperCAmelCase=4096 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=512 , _UpperCAmelCase=0.02 , _UpperCAmelCase=1e-1_2 , _UpperCAmelCase=0 , _UpperCAmelCase=2 , _UpperCAmelCase=1 , _UpperCAmelCase="absolute" , _UpperCAmelCase=True , **_UpperCAmelCase , ):
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
lowercase__: str = vocab_size
lowercase__: Optional[Any] = hidden_size
lowercase__: Union[str, Any] = num_hidden_layers
lowercase__: Dict = num_attention_heads
lowercase__: Any = hidden_act
lowercase__: Optional[Any] = intermediate_size
lowercase__: Any = hidden_dropout_prob
lowercase__: Optional[int] = attention_probs_dropout_prob
lowercase__: Optional[int] = max_position_embeddings
lowercase__: List[str] = initializer_range
lowercase__: List[str] = layer_norm_eps
lowercase__: Any = position_embedding_type
lowercase__: Optional[Any] = use_cache
| 177 | """simple docstring"""
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
# Initialise PyTorch model
lowercase__: Optional[Any] = FunnelConfig.from_json_file(__UpperCAmelCase )
print(F"""Building PyTorch model from configuration: {config}""" )
lowercase__: List[Any] = FunnelBaseModel(__UpperCAmelCase ) if base_model else FunnelModel(__UpperCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , __UpperCAmelCase )
if __name__ == "__main__":
__A = 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(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
__A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 177 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ : Optional[Any] = {
'configuration_efficientnet': [
'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EfficientNetConfig',
'EfficientNetOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : int = ['EfficientNetImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : str = [
'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'EfficientNetForImageClassification',
'EfficientNetModel',
'EfficientNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ : str = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 356 |
"""simple docstring"""
import math
def __UpperCAmelCase ( __lowerCamelCase ) -> str:
lowercase__ : Tuple = 0
lowercase__ : Tuple = 0
while num > 0:
lowercase__ : int = num % 8
lowercase__ : Tuple = octal + (remainder * math.floor(math.pow(10 , __lowerCamelCase ) ))
counter += 1
lowercase__ : Optional[Any] = math.floor(num / 8 ) # basically /= 8 without remainder if any
# This formatting removes trailing '.0' from `octal`.
return f"""0o{int(__lowerCamelCase )}"""
def __UpperCAmelCase ( ) -> None:
print('''\n2 in octal is:''' )
print(decimal_to_octal(2 ) ) # = 2
print('''\n8 in octal is:''' )
print(decimal_to_octal(8 ) ) # = 10
print('''\n65 in octal is:''' )
print(decimal_to_octal(65 ) ) # = 101
print('''\n216 in octal is:''' )
print(decimal_to_octal(2_16 ) ) # = 330
print('''\n512 in octal is:''' )
print(decimal_to_octal(5_12 ) ) # = 1000
print('''\n''' )
if __name__ == "__main__":
main()
| 302 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Dict = logging.get_logger(__name__)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Any ):
__a : List[str] = original_name.split('.' )[0]
__a : Tuple = key.split('.' )
__a : Any = int(key_list[key_list.index(_SCREAMING_SNAKE_CASE ) - 2] )
__a : Union[str, Any] = int(key_list[key_list.index(_SCREAMING_SNAKE_CASE ) - 1] )
__a : List[Any] = orig_block_num - offset
__a : List[Any] = key.replace(F"""{orig_block_num}.{layer_num}.{original_name}""" , F"""block.{new_block_num}.{layer_num}.{new_name}""" )
return key
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
__a : Optional[int] = OrderedDict()
__a , __a : Union[str, Any] = 0, 0
for key, value in state_dict.items():
if key.startswith('network' ):
__a : Optional[int] = key.replace('network' , 'poolformer.encoder' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('bias' ) and "patch_embed" not in key:
patch_emb_offset += 1
__a : Dict = key[: key.find('proj' )]
__a : Union[str, Any] = key.replace(_SCREAMING_SNAKE_CASE , F"""patch_embeddings.{total_embed_found}.""" )
__a : List[str] = key.replace('proj' , 'projection' )
if key.endswith('bias' ):
total_embed_found += 1
if "patch_embeddings" in key:
__a : str = 'poolformer.encoder.' + key
if "mlp.fc1" in key:
__a : Any = replace_key_with_offset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'mlp.fc1' , 'output.conv1' )
if "mlp.fc2" in key:
__a : str = replace_key_with_offset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'mlp.fc2' , 'output.conv2' )
if "norm1" in key:
__a : Tuple = replace_key_with_offset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'norm1' , 'before_norm' )
if "norm2" in key:
__a : str = replace_key_with_offset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'norm2' , 'after_norm' )
if "layer_scale_1" in key:
__a : Any = replace_key_with_offset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'layer_scale_1' , 'layer_scale_1' )
if "layer_scale_2" in key:
__a : Tuple = replace_key_with_offset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'layer_scale_2' , 'layer_scale_2' )
if "head" in key:
__a : List[str] = key.replace('head' , 'classifier' )
__a : int = value
return new_state_dict
def lowerCamelCase ():
__a : int = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__a : Optional[int] = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return image
@torch.no_grad()
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Optional[Any] ):
__a : Tuple = PoolFormerConfig()
# set attributes based on model_name
__a : str = 'huggingface/label-files'
__a : str = model_name[-3:]
__a : Optional[int] = 1_000
__a : Optional[int] = 'imagenet-1k-id2label.json'
__a : Any = (1, 1_000)
# set config attributes
__a : Optional[int] = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type='dataset' ) , 'r' ) )
__a : Optional[Any] = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__a : Optional[Any] = idalabel
__a : Optional[Any] = {v: k for k, v in idalabel.items()}
if size == "s12":
__a : int = [2, 2, 6, 2]
__a : str = [64, 128, 320, 512]
__a : Tuple = 4.0
__a : List[Any] = 0.9
elif size == "s24":
__a : Union[str, Any] = [4, 4, 12, 4]
__a : str = [64, 128, 320, 512]
__a : Optional[Any] = 4.0
__a : Tuple = 0.9
elif size == "s36":
__a : str = [6, 6, 18, 6]
__a : str = [64, 128, 320, 512]
__a : str = 4.0
__a : Any = 1e-6
__a : int = 0.9
elif size == "m36":
__a : Any = [6, 6, 18, 6]
__a : str = [96, 192, 384, 768]
__a : Dict = 4.0
__a : Optional[Any] = 1e-6
__a : List[str] = 0.9_5
elif size == "m48":
__a : Union[str, Any] = [8, 8, 24, 8]
__a : List[str] = [96, 192, 384, 768]
__a : Tuple = 4.0
__a : List[Any] = 1e-6
__a : int = 0.9_5
else:
raise ValueError(F"""Size {size} not supported""" )
# load image processor
__a : List[Any] = PoolFormerImageProcessor(crop_pct=_SCREAMING_SNAKE_CASE )
# Prepare image
__a : Dict = prepare_img()
__a : Dict = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values
logger.info(F"""Converting model {model_name}...""" )
# load original state dict
__a : Optional[int] = torch.load(_SCREAMING_SNAKE_CASE , map_location=torch.device('cpu' ) )
# rename keys
__a : Optional[int] = rename_keys(_SCREAMING_SNAKE_CASE )
# create HuggingFace model and load state dict
__a : Any = PoolFormerForImageClassification(_SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
model.eval()
# Define image processor
__a : Tuple = PoolFormerImageProcessor(crop_pct=_SCREAMING_SNAKE_CASE )
__a : Tuple = image_processor(images=prepare_img() , return_tensors='pt' ).pixel_values
# forward pass
__a : int = model(_SCREAMING_SNAKE_CASE )
__a : Optional[int] = outputs.logits
# define expected logit slices for different models
if size == "s12":
__a : str = torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9] )
elif size == "s24":
__a : Tuple = torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5] )
elif size == "s36":
__a : List[Any] = torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8] )
elif size == "m36":
__a : Any = torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8] )
elif size == "m48":
__a : Dict = torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3] )
else:
raise ValueError(F"""Size {size} not supported""" )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1e-2 )
# finally, save model and image processor
logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowercase : Tuple = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='poolformer_s12',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
__lowercase : Dict = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 27 |
import math
import sys
def lowerCAmelCase_ ( _lowercase : str) -> str:
"""simple docstring"""
a__ : str = """"""
try:
with open(_lowercase , """rb""") as binary_file:
a__ : Any = binary_file.read()
for dat in data:
a__ : Dict = F'''{dat:08b}'''
result += curr_byte
return result
except OSError:
print("""File not accessible""")
sys.exit()
def lowerCAmelCase_ ( _lowercase : str) -> str:
"""simple docstring"""
a__ : Optional[Any] = {"""0""": """0""", """1""": """1"""}
a__ , a__ : Optional[int] = """""", """"""
a__ : int = len(_lowercase)
for i in range(len(_lowercase)):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
a__ : List[str] = lexicon[curr_string]
result += last_match_id
a__ : Any = last_match_id + """0"""
if math.loga(_lowercase).is_integer():
a__ : Union[str, Any] = {}
for curr_key in list(_lowercase):
a__ : Optional[Any] = lexicon.pop(_lowercase)
a__ : Union[str, Any] = new_lex
a__ : str = last_match_id + """1"""
index += 1
a__ : List[Any] = """"""
return result
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str) -> None:
"""simple docstring"""
a__ : List[Any] = 8
try:
with open(_lowercase , """wb""") as opened_file:
a__ : Dict = [
to_write[i : i + byte_length]
for i in range(0 , len(_lowercase) , _lowercase)
]
if len(result_byte_array[-1]) % byte_length == 0:
result_byte_array.append("""10000000""")
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1]) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(_lowercase , 2).to_bytes(1 , byteorder="""big"""))
except OSError:
print("""File not accessible""")
sys.exit()
def lowerCAmelCase_ ( _lowercase : str) -> str:
"""simple docstring"""
a__ : Any = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
a__ : Optional[Any] = data_bits[counter:]
a__ : Tuple = data_bits[counter + 1 :]
return data_bits
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str) -> None:
"""simple docstring"""
a__ : Dict = read_file_binary(_lowercase)
a__ : str = remove_prefix(_lowercase)
a__ : List[str] = decompress_data(_lowercase)
write_file_binary(_lowercase , _lowercase)
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 170 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
@dataclass
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
snake_case__ : Any = 4_2
snake_case__ : List[Any] = 4_2
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_cycle_diffusion import CycleDiffusionPipeline
from .pipeline_stable_diffusion import StableDiffusionPipeline
from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from .pipeline_stable_unclip import StableUnCLIPPipeline
from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
from .safety_checker import StableDiffusionSafetyChecker
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
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 StableDiffusionImageVariationPipeline
else:
from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.26.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionPixaPixZeroPipeline,
)
else:
from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline
try:
if not (
is_torch_available()
and is_transformers_available()
and is_k_diffusion_available()
and is_k_diffusion_version('''>=''', '''0.0.12''')
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
try:
if not (is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_onnx_objects import * # noqa F403
else:
from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
if is_transformers_available() and is_flax_available():
import flax
@flax.struct.dataclass
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
snake_case__ : Tuple = 4_2
snake_case__ : List[Any] = 4_2
from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
| 353 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : List[str] ) -> str:
"""simple docstring"""
if "model" in orig_key:
UpperCamelCase :Union[str, Any] = orig_key.replace("""model.""" , """""" )
if "norm1" in orig_key:
UpperCamelCase :Optional[int] = orig_key.replace("""norm1""" , """attention.output.LayerNorm""" )
if "norm2" in orig_key:
UpperCamelCase :Optional[int] = orig_key.replace("""norm2""" , """output.LayerNorm""" )
if "norm" in orig_key:
UpperCamelCase :Optional[int] = orig_key.replace("""norm""" , """LayerNorm""" )
if "transformer" in orig_key:
UpperCamelCase :Any = orig_key.split(""".""" )[0].split("""_""" )[-1]
UpperCamelCase :List[str] = orig_key.replace(f"""transformer_{layer_num}""" , f"""encoder.layer.{layer_num}""" )
if "mha.attn" in orig_key:
UpperCamelCase :str = orig_key.replace("""mha.attn""" , """attention.self""" )
if "mha" in orig_key:
UpperCamelCase :List[str] = orig_key.replace("""mha""" , """attention""" )
if "W_q" in orig_key:
UpperCamelCase :Optional[Any] = orig_key.replace("""W_q""" , """self.query""" )
if "W_k" in orig_key:
UpperCamelCase :Optional[Any] = orig_key.replace("""W_k""" , """self.key""" )
if "W_v" in orig_key:
UpperCamelCase :List[Any] = orig_key.replace("""W_v""" , """self.value""" )
if "ff1" in orig_key:
UpperCamelCase :Dict = orig_key.replace("""ff1""" , """intermediate.dense""" )
if "ff2" in orig_key:
UpperCamelCase :List[Any] = orig_key.replace("""ff2""" , """output.dense""" )
if "ff" in orig_key:
UpperCamelCase :Optional[int] = orig_key.replace("""ff""" , """output.dense""" )
if "mlm_class" in orig_key:
UpperCamelCase :Dict = orig_key.replace("""mlm.mlm_class""" , """cls.predictions.decoder""" )
if "mlm" in orig_key:
UpperCamelCase :Tuple = orig_key.replace("""mlm""" , """cls.predictions.transform""" )
if "cls" not in orig_key:
UpperCamelCase :Any = """yoso.""" + orig_key
return orig_key
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Optional[int] , __magic_name__ : Optional[int] ) -> Any:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
UpperCamelCase :Any = orig_state_dict.pop(__magic_name__ )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
UpperCamelCase :Tuple = val
UpperCamelCase :Dict = orig_state_dict["""cls.predictions.decoder.bias"""]
UpperCamelCase :Union[str, Any] = torch.arange(__magic_name__ ).expand((1, -1) ) + 2
return orig_state_dict
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int , __magic_name__ : Optional[int] , __magic_name__ : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase :Optional[Any] = torch.load(__magic_name__ , map_location="""cpu""" )["""model_state_dict"""]
UpperCamelCase :int = YosoConfig.from_json_file(__magic_name__ )
UpperCamelCase :Optional[Any] = YosoForMaskedLM(__magic_name__ )
UpperCamelCase :int = convert_checkpoint_helper(config.max_position_embeddings , __magic_name__ )
print(model.load_state_dict(__magic_name__ ) )
model.eval()
model.save_pretrained(__magic_name__ )
print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
if __name__ == "__main__":
UpperCAmelCase_ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The json file for YOSO model config.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
UpperCAmelCase_ : Dict = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 62 | 0 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
lowercase_ = "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"
class __A ( unittest.TestCase , A ):
'''simple docstring'''
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = load_tool('''text-question-answering''' )
self.tool.setup()
_a = load_tool('''text-question-answering''' , remote=A )
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.tool(A , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = self.remote_tool(A , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def a__ (self ) -> int:
"""simple docstring"""
_a = self.tool(text=A , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.remote_tool(text=A , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(A , '''launched the BigScience Research Workshop''' )
| 211 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = "▁"
lowercase_ = {
"vocab_file": "vocab.json",
"spm_file": "sentencepiece.bpe.model",
"tokenizer_config_file": "tokenizer_config.json",
}
lowercase_ = {
"vocab_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
},
"spm_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_config_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
},
}
lowercase_ = {
"facebook/m2m100_418M": 1_024,
}
# fmt: off
lowercase_ = {
"m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
"wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = VOCAB_FILES_NAMES
__lowerCamelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Tuple = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Dict = ['input_ids', 'attention_mask']
__lowerCamelCase : List[int] = []
__lowerCamelCase : List[int] = []
def __init__(self , A , A , A=None , A=None , A="<s>" , A="</s>" , A="</s>" , A="<pad>" , A="<unk>" , A="m2m100" , A = None , A=8 , **A , ) -> None:
"""simple docstring"""
_a = {} if sp_model_kwargs is None else sp_model_kwargs
_a = language_codes
_a = FAIRSEQ_LANGUAGE_CODES[language_codes]
_a = {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}
_a = kwargs.get('''additional_special_tokens''' , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(A )
for lang_code in fairseq_language_code
if self.get_lang_token(A ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=A , tgt_lang=A , bos_token=A , eos_token=A , sep_token=A , unk_token=A , pad_token=A , language_codes=A , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=A , **A , )
_a = vocab_file
_a = load_json(A )
_a = {v: k for k, v in self.encoder.items()}
_a = spm_file
_a = load_spm(A , self.sp_model_kwargs )
_a = len(self.encoder )
_a = {
self.get_lang_token(A ): self.encoder_size + i for i, lang_code in enumerate(A )
}
_a = {lang_code: self.encoder_size + i for i, lang_code in enumerate(A )}
_a = {v: k for k, v in self.lang_token_to_id.items()}
_a = src_lang if src_lang is not None else '''en'''
_a = tgt_lang
_a = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
_a = num_madeup_words
@property
def a__ (self ) -> int:
"""simple docstring"""
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def a__ (self ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def a__ (self , A ) -> None:
"""simple docstring"""
_a = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def a__ (self , A ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(A , out_type=A )
def a__ (self , A ) -> Union[str, Any]:
"""simple docstring"""
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(A , self.encoder[self.unk_token] )
def a__ (self , A ) -> str:
"""simple docstring"""
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(A , self.unk_token )
def a__ (self , A ) -> Dict:
"""simple docstring"""
_a = []
_a = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A ) + token
_a = []
else:
current_sub_tokens.append(A )
out_string += self.sp_model.decode(A )
return out_string.strip()
def a__ (self , A , A = None , A = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A , token_ids_a=A , already_has_special_tokens=A )
_a = [1] * len(self.prefix_tokens )
_a = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(A )) + suffix_ones
return prefix_ones + ([0] * len(A )) + ([0] * len(A )) + suffix_ones
def a__ (self , A , A = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def a__ (self ) -> Dict:
"""simple docstring"""
_a = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__(self ) -> Dict:
"""simple docstring"""
_a = self.__dict__.copy()
_a = None
return state
def __setstate__(self , A ) -> None:
"""simple docstring"""
_a = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_a = {}
_a = load_spm(self.spm_file , self.sp_model_kwargs )
def a__ (self , A , A = None ) -> Tuple[str]:
"""simple docstring"""
_a = Path(A )
if not save_dir.is_dir():
raise OSError(f'''{save_directory} should be a directory''' )
_a = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
)
_a = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
)
save_json(self.encoder , A )
if os.path.abspath(self.spm_file ) != os.path.abspath(A ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , A )
elif not os.path.isfile(self.spm_file ):
with open(A , '''wb''' ) as fi:
_a = self.sp_model.serialized_model_proto()
fi.write(A )
return (str(A ), str(A ))
def a__ (self , A , A = "en" , A = None , A = "ro" , **A , ) -> BatchEncoding:
"""simple docstring"""
_a = src_lang
_a = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(A , A , **A )
def a__ (self , A , A , A , **A ) -> Union[str, Any]:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
_a = src_lang
_a = self(A , add_special_tokens=A , **A )
_a = self.get_lang_id(A )
_a = tgt_lang_id
return inputs
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self.set_src_lang_special_tokens(self.src_lang )
def a__ (self ) -> Tuple:
"""simple docstring"""
self.set_tgt_lang_special_tokens(self.tgt_lang )
def a__ (self , A ) -> None:
"""simple docstring"""
_a = self.get_lang_token(A )
_a = self.lang_token_to_id[lang_token]
_a = [self.cur_lang_id]
_a = [self.eos_token_id]
def a__ (self , A ) -> None:
"""simple docstring"""
_a = self.get_lang_token(A )
_a = self.lang_token_to_id[lang_token]
_a = [self.cur_lang_id]
_a = [self.eos_token_id]
def a__ (self , A ) -> str:
"""simple docstring"""
return self.lang_code_to_token[lang]
def a__ (self , A ) -> int:
"""simple docstring"""
_a = self.get_lang_token(A )
return self.lang_token_to_id[lang_token]
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = sentencepiece.SentencePieceProcessor(**__A)
spm.Load(str(__A))
return spm
def lowerCAmelCase (__A):
"""simple docstring"""
with open(__A , '''r''') as f:
return json.load(__A)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
with open(__A , '''w''') as f:
json.dump(__A , __A , indent=2)
| 211 | 1 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list , __lowerCAmelCase : int | None = None , __lowerCAmelCase : int | None = None ) -> None:
if start is None:
__lowerCamelCase = 0
if end is None:
__lowerCamelCase = len(__lowerCAmelCase ) - 1
if start >= end:
return
__lowerCamelCase = (start + end) // 2
slowsort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
slowsort(__lowerCAmelCase , mid + 1 , __lowerCAmelCase )
if sequence[end] < sequence[mid]:
__lowerCamelCase , __lowerCamelCase = sequence[mid], sequence[end]
slowsort(__lowerCAmelCase , __lowerCAmelCase , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 339 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Any = TypeVar("KEY")
SCREAMING_SNAKE_CASE__ : Dict = TypeVar("VAL")
@dataclass(frozen=__lowercase , slots=__lowercase )
class lowerCAmelCase__ ( Generic[KEY, VAL] ):
a__ : KEY
a__ : VAL
class lowerCAmelCase__ ( _Item ):
def __init__( self : str ) -> None:
super().__init__(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __bool__( self : Tuple ) -> bool:
return False
SCREAMING_SNAKE_CASE__ : List[Any] = _DeletedItem()
class lowerCAmelCase__ ( MutableMapping[KEY, VAL] ):
def __init__( self : int , SCREAMING_SNAKE_CASE__ : int = 8 , SCREAMING_SNAKE_CASE__ : float = 0.75 ) -> None:
__lowerCamelCase = initial_block_size
__lowerCamelCase = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
__lowerCamelCase = capacity_factor
__lowerCamelCase = 0
def __A ( self : Any , SCREAMING_SNAKE_CASE__ : KEY ) -> int:
return hash(SCREAMING_SNAKE_CASE__ ) % len(self._buckets )
def __A ( self : Any , SCREAMING_SNAKE_CASE__ : int ) -> int:
return (ind + 1) % len(self._buckets )
def __A ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : KEY , SCREAMING_SNAKE_CASE__ : VAL ) -> bool:
__lowerCamelCase = self._buckets[ind]
if not stored:
__lowerCamelCase = _Item(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self._len += 1
return True
elif stored.key == key:
__lowerCamelCase = _Item(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return True
else:
return False
def __A ( self : Any ) -> bool:
__lowerCamelCase = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(SCREAMING_SNAKE_CASE__ )
def __A ( self : List[Any] ) -> bool:
if len(self._buckets ) <= self._initial_block_size:
return False
__lowerCamelCase = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def __A ( self : int , SCREAMING_SNAKE_CASE__ : int ) -> None:
__lowerCamelCase = self._buckets
__lowerCamelCase = [None] * new_size
__lowerCamelCase = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def __A ( self : str ) -> None:
self._resize(len(self._buckets ) * 2 )
def __A ( self : Dict ) -> None:
self._resize(len(self._buckets ) // 2 )
def __A ( self : Tuple , SCREAMING_SNAKE_CASE__ : KEY ) -> Iterator[int]:
__lowerCamelCase = self._get_bucket_index(SCREAMING_SNAKE_CASE__ )
for _ in range(len(self._buckets ) ):
yield ind
__lowerCamelCase = self._get_next_ind(SCREAMING_SNAKE_CASE__ )
def __A ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : KEY , SCREAMING_SNAKE_CASE__ : VAL ) -> None:
for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE__ ):
if self._try_set(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
break
def __setitem__( self : Tuple , SCREAMING_SNAKE_CASE__ : KEY , SCREAMING_SNAKE_CASE__ : VAL ) -> None:
if self._is_full():
self._size_up()
self._add_item(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __delitem__( self : List[Any] , SCREAMING_SNAKE_CASE__ : KEY ) -> None:
for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase = self._buckets[ind]
if item is None:
raise KeyError(SCREAMING_SNAKE_CASE__ )
if item is _deleted:
continue
if item.key == key:
__lowerCamelCase = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : KEY ) -> VAL:
for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(SCREAMING_SNAKE_CASE__ )
def __len__( self : int ) -> int:
return self._len
def __iter__( self : Tuple ) -> Iterator[KEY]:
yield from (item.key for item in self._buckets if item)
def __repr__( self : Optional[Any] ) -> str:
__lowerCamelCase = ''' ,'''.join(
f'''{item.key}: {item.val}''' for item in self._buckets if item )
return f'''HashMap({val_string})'''
| 339 | 1 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
UpperCAmelCase__ : List[Any] = "sshleifer/mar_enro_6_3_student"
class a__ ( A__ ):
"""simple docstring"""
def _lowercase ( self : Tuple ) ->Tuple:
"""simple docstring"""
super().setUp()
SCREAMING_SNAKE_CASE : List[Any] = cached_path(
"""https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz""" , extract_compressed_file=_a , )
SCREAMING_SNAKE_CASE : Optional[int] = f"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"
@slow
@require_torch_gpu
def _lowercase ( self : List[Any] ) ->str:
"""simple docstring"""
MarianMTModel.from_pretrained(_a )
@slow
@require_torch_gpu
def _lowercase ( self : int ) ->Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = {
"""$MAX_LEN""": 6_4,
"""$BS""": 6_4,
"""$GAS""": 1,
"""$ENRO_DIR""": self.data_dir,
"""facebook/mbart-large-cc25""": MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
"""--learning_rate=3e-5""": """--learning_rate 3e-4""",
"""--num_train_epochs 6""": """--num_train_epochs 1""",
}
# Clean up bash script
SCREAMING_SNAKE_CASE : Any = (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py""" )[1].strip()
SCREAMING_SNAKE_CASE : List[Any] = bash_script.replace("""\\\n""" , """""" ).strip().replace("""\"$@\"""" , """""" )
for k, v in env_vars_to_replace.items():
SCREAMING_SNAKE_CASE : List[Any] = bash_script.replace(_a , str(_a ) )
SCREAMING_SNAKE_CASE : int = self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
SCREAMING_SNAKE_CASE : Optional[Any] = f"\n --output_dir {output_dir}\n --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n --sortish_sampler\n --do_predict\n --gpus 1\n --freeze_encoder\n --n_train 40000\n --n_val 500\n --n_test 500\n --fp16_opt_level O1\n --num_sanity_val_steps 0\n --eval_beams 2\n ".split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
SCREAMING_SNAKE_CASE : Optional[Any] = ["""finetune.py"""] + bash_script.split() + args
with patch.object(_a , """argv""" , _a ):
SCREAMING_SNAKE_CASE : Tuple = argparse.ArgumentParser()
SCREAMING_SNAKE_CASE : Union[str, Any] = pl.Trainer.add_argparse_args(_a )
SCREAMING_SNAKE_CASE : Tuple = SummarizationModule.add_model_specific_args(_a , os.getcwd() )
SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_args()
SCREAMING_SNAKE_CASE : List[Any] = main(_a )
# Check metrics
SCREAMING_SNAKE_CASE : List[str] = load_json(model.metrics_save_path )
SCREAMING_SNAKE_CASE : Tuple = metrics["""val"""][0]
SCREAMING_SNAKE_CASE : int = metrics["""val"""][-1]
self.assertEqual(len(metrics["""val"""] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"] , _a )
self.assertGreater(last_step_stats["""val_avg_gen_time"""] , 0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats["""val_avg_gen_time"""] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats["""val_avg_bleu"""] , 1_7 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
SCREAMING_SNAKE_CASE : Dict = os.listdir(_a )
SCREAMING_SNAKE_CASE : Dict = [x for x in contents if x.endswith(""".ckpt""" )][0]
SCREAMING_SNAKE_CASE : Dict = os.path.join(args.output_dir , _a )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.load(_a , map_location="""cpu""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
SCREAMING_SNAKE_CASE : Optional[int] = {os.path.basename(_a ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["""test"""] ) == 1
class a__ ( A__ ):
"""simple docstring"""
@timeout_decorator.timeout(6_0_0 )
@slow
@require_torch_gpu
def _lowercase ( self : List[str] ) ->List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = f"{self.test_file_dir_str}/test_data/wmt_en_ro"
SCREAMING_SNAKE_CASE : Optional[Any] = {
"""--fp16_opt_level=O1""": """""",
"""$MAX_LEN""": 1_2_8,
"""$BS""": 1_6,
"""$GAS""": 1,
"""$ENRO_DIR""": data_dir,
"""$m""": """sshleifer/student_marian_en_ro_6_1""",
"""val_check_interval=0.25""": """val_check_interval=1.0""",
}
# Clean up bash script
SCREAMING_SNAKE_CASE : str = (
(self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py""" )[1].strip()
)
SCREAMING_SNAKE_CASE : Optional[int] = bash_script.replace("""\\\n""" , """""" ).strip().replace("""\"$@\"""" , """""" )
SCREAMING_SNAKE_CASE : List[str] = bash_script.replace("""--fp16 """ , """ """ )
for k, v in env_vars_to_replace.items():
SCREAMING_SNAKE_CASE : Optional[int] = bash_script.replace(_a , str(_a ) )
SCREAMING_SNAKE_CASE : Dict = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE : Union[str, Any] = bash_script.replace("""--fp16""" , """""" )
SCREAMING_SNAKE_CASE : Any = 6
SCREAMING_SNAKE_CASE : Optional[int] = (
["""distillation.py"""]
+ bash_script.split()
+ [
f"--output_dir={output_dir}",
"""--gpus=1""",
"""--learning_rate=1e-3""",
f"--num_train_epochs={epochs}",
"""--warmup_steps=10""",
"""--val_check_interval=1.0""",
"""--do_predict""",
]
)
with patch.object(_a , """argv""" , _a ):
SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser()
SCREAMING_SNAKE_CASE : Any = pl.Trainer.add_argparse_args(_a )
SCREAMING_SNAKE_CASE : Dict = SummarizationDistiller.add_model_specific_args(_a , os.getcwd() )
SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
SCREAMING_SNAKE_CASE : int = distill_main(_a )
# Check metrics
SCREAMING_SNAKE_CASE : Optional[int] = load_json(model.metrics_save_path )
SCREAMING_SNAKE_CASE : Dict = metrics["""val"""][0]
SCREAMING_SNAKE_CASE : Optional[Any] = metrics["""val"""][-1]
assert len(metrics["""val"""] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"] , _a )
# check lightning ckpt can be loaded and has a reasonable statedict
SCREAMING_SNAKE_CASE : List[str] = os.listdir(_a )
SCREAMING_SNAKE_CASE : int = [x for x in contents if x.endswith(""".ckpt""" )][0]
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(args.output_dir , _a )
SCREAMING_SNAKE_CASE : List[Any] = torch.load(_a , map_location="""cpu""" )
SCREAMING_SNAKE_CASE : Tuple = """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
SCREAMING_SNAKE_CASE : int = {os.path.basename(_a ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["""test"""] ) == 1
| 245 |
'''simple docstring'''
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def _lowerCAmelCase ( _UpperCamelCase : Dict , _UpperCamelCase : Any=False ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =OmegaConf.load(_UpperCamelCase )
if display:
print(yaml.dump(OmegaConf.to_container(_UpperCamelCase ) ) )
return config
def _lowerCAmelCase ( _UpperCamelCase : Tuple , _UpperCamelCase : Optional[Any]=None , _UpperCamelCase : Union[str, Any]=None ) -> Optional[Any]:
"""simple docstring"""
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 : Tuple , _UpperCamelCase : int ) -> Dict:
"""simple docstring"""
_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 : Dict , _UpperCamelCase : List[str]=False ) -> int:
"""simple docstring"""
_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 : str ) -> List[str]:
"""simple docstring"""
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 : Tuple , _UpperCamelCase : List[Any] , _UpperCamelCase : List[str]=True , _UpperCamelCase : int=True ) -> Union[str, Any]:
"""simple docstring"""
_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 : Union[str, Any] , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
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
| 47 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ : int = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
'configuration_data2vec_text': [
'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Data2VecTextConfig',
'Data2VecTextOnnxConfig',
],
'configuration_data2vec_vision': [
'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Data2VecVisionConfig',
'Data2VecVisionOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[Any] = [
'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecAudioForAudioFrameClassification',
'Data2VecAudioForCTC',
'Data2VecAudioForSequenceClassification',
'Data2VecAudioForXVector',
'Data2VecAudioModel',
'Data2VecAudioPreTrainedModel',
]
UpperCAmelCase_ : str = [
'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecTextForCausalLM',
'Data2VecTextForMaskedLM',
'Data2VecTextForMultipleChoice',
'Data2VecTextForQuestionAnswering',
'Data2VecTextForSequenceClassification',
'Data2VecTextForTokenClassification',
'Data2VecTextModel',
'Data2VecTextPreTrainedModel',
]
UpperCAmelCase_ : List[Any] = [
'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecVisionForImageClassification',
'Data2VecVisionForMaskedImageModeling',
'Data2VecVisionForSemanticSegmentation',
'Data2VecVisionModel',
'Data2VecVisionPreTrainedModel',
]
if is_tf_available():
UpperCAmelCase_ : str = [
'TFData2VecVisionForImageClassification',
'TFData2VecVisionForSemanticSegmentation',
'TFData2VecVisionModel',
'TFData2VecVisionPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 353 |
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 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_A : Any = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Union[str, Any] , *SCREAMING_SNAKE_CASE__ : Tuple , **SCREAMING_SNAKE_CASE__ : List[Any] ) -> None:
warnings.warn(
"""The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use GLPNImageProcessor instead.""" , SCREAMING_SNAKE_CASE__ , )
super().__init__(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
| 229 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : list[int] , snake_case_ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case_ ) == len(snake_case_ ) == 3:
raise ValueError("""Please enter a valid equation.""" )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("""Both a & b of two equations can't be zero.""" )
# Extract the coefficients
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = equationa
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = equationa
# Calculate the determinants of the matrices
__lowerCAmelCase = aa * ba - aa * ba
__lowerCAmelCase = ca * ba - ca * ba
__lowerCAmelCase = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("""Infinite solutions. (Consistent system)""" )
else:
raise ValueError("""No solution. (Inconsistent system)""" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
__lowerCAmelCase = determinant_x / determinant
__lowerCAmelCase = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 229 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : Tuple = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Union[str, Any] = [
"""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
UpperCamelCase : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 345 | '''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( snake_case : int , snake_case : int ) -> bool:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 345 | 1 |
def snake_case_ ( snake_case , snake_case ) -> str:
lowercase__: int = ''
for i in table:
res += inp[i - 1]
return res
def snake_case_ ( snake_case ) -> List[str]:
return data[1:] + data[0]
def snake_case_ ( snake_case , snake_case ) -> List[Any]:
lowercase__: List[str] = ''
for i in range(len(snake_case ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def snake_case_ ( snake_case , snake_case ) -> Tuple:
lowercase__: List[str] = int('0b' + data[0] + data[-1] , 2 )
lowercase__: List[str] = int('0b' + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case ) -> List[str]:
lowercase__: Any = message[:4]
lowercase__: Optional[Any] = message[4:]
lowercase__: Dict = apply_table(snake_case , snake_case )
lowercase__: Optional[int] = xor(snake_case , snake_case )
lowercase__: Optional[int] = apply_sbox(snake_case , temp[:4] ) # noqa: E741
lowercase__: Optional[int] = apply_sbox(snake_case , temp[4:] )
lowercase__: Tuple = '0' * (2 - len(snake_case )) + l # noqa: E741
lowercase__: int = '0' * (2 - len(snake_case )) + r
lowercase__: Optional[Any] = apply_table(l + r , snake_case )
lowercase__: Optional[Any] = xor(snake_case , snake_case )
return temp + right
if __name__ == "__main__":
__lowerCAmelCase = input('''Enter 10 bit key: ''')
__lowerCAmelCase = input('''Enter 8 bit message: ''')
__lowerCAmelCase = [6, 3, 7, 4, 8, 5, 10, 9]
__lowerCAmelCase = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
__lowerCAmelCase = [2, 4, 3, 1]
__lowerCAmelCase = [2, 6, 3, 1, 4, 8, 5, 7]
__lowerCAmelCase = [4, 1, 3, 5, 7, 2, 8, 6]
__lowerCAmelCase = [4, 1, 2, 3, 2, 3, 4, 1]
__lowerCAmelCase = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
__lowerCAmelCase = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
__lowerCAmelCase = apply_table(key, paa_table)
__lowerCAmelCase = temp[:5]
__lowerCAmelCase = temp[5:]
__lowerCAmelCase = left_shift(left)
__lowerCAmelCase = left_shift(right)
__lowerCAmelCase = apply_table(left + right, pa_table)
__lowerCAmelCase = left_shift(left)
__lowerCAmelCase = left_shift(right)
__lowerCAmelCase = left_shift(left)
__lowerCAmelCase = left_shift(right)
__lowerCAmelCase = apply_table(left + right, pa_table)
# encryption
__lowerCAmelCase = apply_table(message, IP)
__lowerCAmelCase = function(expansion, sa, sa, keya, temp)
__lowerCAmelCase = temp[4:] + temp[:4]
__lowerCAmelCase = function(expansion, sa, sa, keya, temp)
__lowerCAmelCase = apply_table(temp, IP_inv)
print('''Cipher text is:''', CT)
# decryption
__lowerCAmelCase = apply_table(CT, IP)
__lowerCAmelCase = function(expansion, sa, sa, keya, temp)
__lowerCAmelCase = temp[4:] + temp[:4]
__lowerCAmelCase = function(expansion, sa, sa, keya, temp)
__lowerCAmelCase = apply_table(temp, IP_inv)
print('''Plain text after decypting is:''', PT)
| 196 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase = get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')
@require_sentencepiece
@require_tokenizers
class __a ( __UpperCamelCase , unittest.TestCase ):
__lowercase : int = PegasusTokenizer
__lowercase : Any = PegasusTokenizerFast
__lowercase : Optional[int] = True
__lowercase : Tuple = True
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowercase__: List[str] = PegasusTokenizer(lowerCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return PegasusTokenizer.from_pretrained('google/pegasus-large' )
def SCREAMING_SNAKE_CASE__ ( self , **lowerCAmelCase__ ) -> PegasusTokenizer:
'''simple docstring'''
return PegasusTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
return ("This is a test", "This is a test")
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
lowercase__: Optional[Any] = '</s>'
lowercase__: Union[str, Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__ ) , lowerCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__ ) , lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__: Tuple = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '</s>' )
self.assertEqual(vocab_keys[-1] , 'v' )
self.assertEqual(len(lowerCAmelCase__ ) , 1_103 )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_103 )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__: Dict = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowercase__: Optional[int] = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowercase__: Optional[Any] = (
'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
' </s> <pad> <pad> <pad>'
)
lowercase__: Dict = rust_tokenizer([raw_input_str] , return_tensors=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ ).input_ids[0]
lowercase__: Tuple = py_tokenizer([raw_input_str] , return_tensors=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ ).input_ids[0]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
lowercase__: int = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
lowercase__: Any = '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
lowercase__: Union[str, Any] = [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
lowercase__: int = tokenizer([raw_input_str] , return_tensors=lowerCAmelCase__ ).input_ids[0]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
lowercase__: Optional[int] = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 96_103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_024
lowercase__: int = 'To ensure a smooth flow of bank resolutions.'
lowercase__: Any = [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
lowercase__: str = tokenizer([raw_input_str] , return_tensors=lowerCAmelCase__ ).input_ids[0]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
lowercase__: Any = ['This is going to be way too long.' * 150, 'short example']
lowercase__: Tuple = ['not super long but more than 5 tokens', 'tiny']
lowercase__: Dict = self._large_tokenizer(lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors='pt' )
lowercase__: Any = self._large_tokenizer(
text_target=lowerCAmelCase__ , max_length=5 , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 1_024)
assert batch.attention_mask.shape == (2, 1_024)
assert targets["input_ids"].shape == (2, 5)
assert len(lowerCAmelCase__ ) == 2 # input_ids, attention_mask.
@slow
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
# fmt: off
lowercase__: List[str] = {'input_ids': [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase__ , model_name='google/bigbird-pegasus-large-arxiv' , revision='ba85d0851d708441f91440d509690f1ab6353415' , )
@require_sentencepiece
@require_tokenizers
class __a ( __UpperCamelCase , unittest.TestCase ):
__lowercase : int = PegasusTokenizer
__lowercase : Any = PegasusTokenizerFast
__lowercase : Any = True
__lowercase : Dict = True
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowercase__: Union[str, Any] = PegasusTokenizer(lowerCAmelCase__ , offset=0 , mask_token_sent=lowerCAmelCase__ , mask_token='[MASK]' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv' )
def SCREAMING_SNAKE_CASE__ ( self , **lowerCAmelCase__ ) -> PegasusTokenizer:
'''simple docstring'''
return PegasusTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
return ("This is a test", "This is a test")
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__: str = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowercase__: str = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowercase__: Tuple = (
'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
' <pad> <pad> <pad>'
)
lowercase__: List[Any] = rust_tokenizer([raw_input_str] , return_tensors=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ ).input_ids[0]
lowercase__: Any = py_tokenizer([raw_input_str] , return_tensors=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ ).input_ids[0]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@require_torch
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
lowercase__: List[Any] = ['This is going to be way too long.' * 1_000, 'short example']
lowercase__: str = ['not super long but more than 5 tokens', 'tiny']
lowercase__: Tuple = self._large_tokenizer(lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors='pt' )
lowercase__: Dict = self._large_tokenizer(
text_target=lowerCAmelCase__ , max_length=5 , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 4_096)
assert batch.attention_mask.shape == (2, 4_096)
assert targets["input_ids"].shape == (2, 5)
assert len(lowerCAmelCase__ ) == 2 # input_ids, attention_mask.
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
lowercase__: str = (
'This is an example string that is used to test the original TF implementation against the HF'
' implementation'
)
lowercase__: Optional[int] = self._large_tokenizer(lowerCAmelCase__ ).input_ids
self.assertListEqual(
lowerCAmelCase__ , [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1] , )
| 196 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A : Tuple = '''microsoft/speecht5_tts'''
_A : List[str] = (
'''This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '''
'''text to read (in English) and returns a waveform object containing the sound.'''
)
_A : Optional[int] = '''text_reader'''
_A : List[Any] = SpeechTaProcessor
_A : List[Any] = SpeechTaForTextToSpeech
_A : Union[str, Any] = SpeechTaHifiGan
_A : Tuple = ['''text''']
_A : Union[str, Any] = ['''audio''']
def lowerCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
if self.post_processor is None:
__lowercase : List[str] = """microsoft/speecht5_hifigan"""
super().setup()
def lowerCAmelCase ( self : Any , __a : Optional[int] , __a : int=None ) -> Any:
"""simple docstring"""
__lowercase : Optional[Any] = self.pre_processor(text=__a , return_tensors="""pt""" , truncation=__a )
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError("""Datasets needs to be installed if not passing speaker embeddings.""" )
__lowercase : Union[str, Any] = load_dataset("""Matthijs/cmu-arctic-xvectors""" , split="""validation""" )
__lowercase : Any = torch.tensor(embeddings_dataset[7305]["""xvector"""] ).unsqueeze(0 )
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def lowerCAmelCase ( self : Tuple , __a : Any ) -> Any:
"""simple docstring"""
with torch.no_grad():
return self.model.generate_speech(**__a )
def lowerCAmelCase ( self : int , __a : Union[str, Any] ) -> int:
"""simple docstring"""
with torch.no_grad():
return self.post_processor(__a ).cpu().detach() | 370 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
lowerCamelCase : str = trt.Logger(trt.Logger.WARNING)
lowerCamelCase : Any = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
lowerCamelCase : Optional[Any] = logging.getLogger(__name__)
lowerCamelCase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--onnx_model_path''',
default=None,
type=str,
required=True,
help='''Path to ONNX model: ''',
)
parser.add_argument(
'''--output_dir''',
default=None,
type=str,
required=True,
help='''The output directory where the model checkpoints and predictions will be written.''',
)
# Other parameters
parser.add_argument(
'''--tokenizer_name''',
default='''''',
type=str,
required=True,
help='''Pretrained tokenizer name or path if not the same as model_name''',
)
parser.add_argument(
'''--version_2_with_negative''',
action='''store_true''',
help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
'''--null_score_diff_threshold''',
type=float,
default=0.0,
help='''If null_score - best_non_null is greater than the threshold predict null.''',
)
parser.add_argument(
'''--max_seq_length''',
default=3_84,
type=int,
help=(
'''The maximum total input sequence length after WordPiece tokenization. Sequences '''
'''longer than this will be truncated, and sequences shorter than this will be padded.'''
),
)
parser.add_argument(
'''--doc_stride''',
default=1_28,
type=int,
help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)
parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')
parser.add_argument(
'''--n_best_size''',
default=20,
type=int,
help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
'''--max_answer_length''',
default=30,
type=int,
help=(
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
),
)
parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')
parser.add_argument(
'''--dataset_name''',
type=str,
default=None,
required=True,
help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--dataset_config_name''',
type=str,
default=None,
help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--fp16''',
action='''store_true''',
help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
'''--int8''',
action='''store_true''',
help='''Whether to use INT8''',
)
lowerCamelCase : Dict = parser.parse_args()
if args.tokenizer_name:
lowerCamelCase : str = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
'''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
)
logger.info('''Training/evaluation parameters %s''', args)
lowerCamelCase : List[str] = args.per_device_eval_batch_size
lowerCamelCase : Any = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
lowerCamelCase : List[str] = True
lowerCamelCase : List[Any] = '''temp_engine/bert-fp32.engine'''
if args.fpaa:
lowerCamelCase : Optional[Any] = '''temp_engine/bert-fp16.engine'''
if args.inta:
lowerCamelCase : int = '''temp_engine/bert-int8.engine'''
# import ONNX file
if not os.path.exists('''temp_engine'''):
os.makedirs('''temp_engine''')
lowerCamelCase : int = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, '''rb''') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
lowerCamelCase : Union[str, Any] = [network.get_input(i) for i in range(network.num_inputs)]
lowerCamelCase : Dict = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
lowerCamelCase : List[str] = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
lowerCamelCase : Optional[int] = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
lowerCamelCase : Optional[Any] = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, '''wb''') as f:
f.write(engine.serialize())
def snake_case_ ( lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Tuple ):
__lowercase : List[str] = np.asarray(inputs["""input_ids"""] , dtype=np.intaa )
__lowercase : Union[str, Any] = np.asarray(inputs["""attention_mask"""] , dtype=np.intaa )
__lowercase : int = np.asarray(inputs["""token_type_ids"""] , dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowerCAmelCase_ )
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowerCAmelCase_ )
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowerCAmelCase_ )
# start time
__lowercase : Optional[Any] = time.time()
# Run inference
context.execute_async(
bindings=[int(lowerCAmelCase_ ) for d_inp in d_inputs] + [int(lowerCAmelCase_ ), int(lowerCAmelCase_ )] , stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
cuda.memcpy_dtoh_async(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# Synchronize the stream and take time
stream.synchronize()
# end time
__lowercase : int = time.time()
__lowercase : Union[str, Any] = end_time - start_time
__lowercase : Any = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
lowerCamelCase : Tuple = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
lowerCamelCase : List[Any] = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
lowerCamelCase : Optional[Any] = raw_datasets['''validation'''].column_names
lowerCamelCase : Union[str, Any] = '''question''' if '''question''' in column_names else column_names[0]
lowerCamelCase : str = '''context''' if '''context''' in column_names else column_names[1]
lowerCamelCase : Dict = '''answers''' if '''answers''' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
lowerCamelCase : Dict = tokenizer.padding_side == '''right'''
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'''The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'''
f'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''
)
lowerCamelCase : Tuple = min(args.max_seq_length, tokenizer.model_max_length)
def snake_case_ ( lowerCAmelCase_ : int ):
# Some of the questions have lots of whitespace on the left, which is not useful and will make the
# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
# left whitespace
__lowercase : str = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
__lowercase : List[str] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation="""only_second""" if pad_on_right else """only_first""" , max_length=lowerCAmelCase_ , stride=args.doc_stride , return_overflowing_tokens=lowerCAmelCase_ , return_offsets_mapping=lowerCAmelCase_ , padding="""max_length""" , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
__lowercase : List[str] = tokenized_examples.pop("""overflow_to_sample_mapping""" )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
__lowercase : Any = []
for i in range(len(tokenized_examples["""input_ids"""] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
__lowercase : Dict = tokenized_examples.sequence_ids(lowerCAmelCase_ )
__lowercase : List[Any] = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
__lowercase : List[str] = sample_mapping[i]
tokenized_examples["example_id"].append(examples["""id"""][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
__lowercase : Dict = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["""offset_mapping"""][i] )
]
return tokenized_examples
lowerCamelCase : Tuple = raw_datasets['''validation''']
# Validation Feature Creation
lowerCamelCase : Optional[int] = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='''Running tokenizer on validation dataset''',
)
lowerCamelCase : Union[str, Any] = default_data_collator
lowerCamelCase : Optional[Any] = eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
lowerCamelCase : List[str] = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def snake_case_ ( lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict="eval" ):
# Post-processing: we match the start logits and end logits to answers in the original context.
__lowercase : int = postprocess_qa_predictions(
examples=lowerCAmelCase_ , features=lowerCAmelCase_ , predictions=lowerCAmelCase_ , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowerCAmelCase_ , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
__lowercase : Optional[int] = [
{"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
]
else:
__lowercase : List[Any] = [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]
__lowercase : Optional[int] = [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowerCAmelCase_ , label_ids=lowerCAmelCase_ )
lowerCamelCase : Dict = load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')
# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def snake_case_ ( lowerCAmelCase_ : str ):
return trt.volume(engine.get_binding_shape(lowerCAmelCase_ ) ) * engine.get_binding_dtype(lowerCAmelCase_ ).itemsize
# Allocate device memory for inputs and outputs.
lowerCamelCase : int = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
lowerCamelCase : Dict = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
lowerCamelCase : str = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
lowerCamelCase : Dict = cuda.mem_alloc(h_outputa.nbytes)
lowerCamelCase : Optional[Any] = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
lowerCamelCase : Optional[int] = cuda.Stream()
# Evaluation
logger.info('''***** Running Evaluation *****''')
logger.info(f''' Num examples = {len(eval_dataset)}''')
logger.info(f''' Batch size = {args.per_device_eval_batch_size}''')
lowerCamelCase : int = 0.0
lowerCamelCase : List[str] = 0
lowerCamelCase : List[str] = timeit.default_timer()
lowerCamelCase : List[Any] = None
for step, batch in enumerate(eval_dataloader):
lowerCamelCase ,lowerCamelCase : str = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
lowerCamelCase ,lowerCamelCase : Union[str, Any] = outputs
lowerCamelCase : Optional[Any] = torch.tensor(start_logits)
lowerCamelCase : List[str] = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
lowerCamelCase : Optional[int] = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
lowerCamelCase : Dict = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)
lowerCamelCase : List[Any] = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
lowerCamelCase : Dict = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)
if all_preds is not None:
lowerCamelCase : Tuple = nested_truncate(all_preds, len(eval_dataset))
lowerCamelCase : Dict = timeit.default_timer() - start_time
logger.info(''' Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 10_00 / niter))
logger.info('''Total Inference Time = {:.3f} ms'''.format(total_time * 10_00))
logger.info('''Total Number of Inference = %d''', niter)
lowerCamelCase : str = post_processing_function(eval_examples, eval_dataset, all_preds)
lowerCamelCase : Optional[Any] = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'''Evaluation metrics: {eval_metric}''') | 306 | 0 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def _lowercase ( __snake_case ) -> Union[str, Any]:
if is_torch_version("<" ,"2.0.0" ) or not hasattr(__snake_case ,"_dynamo" ):
return False
return isinstance(__snake_case ,torch._dynamo.eval_frame.OptimizedModule )
def _lowercase ( __snake_case ,__snake_case = True ) -> Union[str, Any]:
__lowerCAmelCase : Dict = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
__lowerCAmelCase : Optional[int] = is_compiled_module(__snake_case )
if is_compiled:
__lowerCAmelCase : str = model
__lowerCAmelCase : Any = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(__snake_case ,__snake_case ):
__lowerCAmelCase : Any = model.module
if not keep_fpaa_wrapper:
__lowerCAmelCase : Dict = getattr(__snake_case ,"forward" )
__lowerCAmelCase : Dict = model.__dict__.pop("_original_forward" ,__snake_case )
if original_forward is not None:
while hasattr(__snake_case ,"__wrapped__" ):
__lowerCAmelCase : List[str] = forward.__wrapped__
if forward == original_forward:
break
__lowerCAmelCase : Optional[int] = forward
if getattr(__snake_case ,"_converted_to_transformer_engine" ,__snake_case ):
convert_model(__snake_case ,to_transformer_engine=__snake_case )
if is_compiled:
__lowerCAmelCase : Dict = model
__lowerCAmelCase : Optional[Any] = compiled_model
return model
def _lowercase ( ) -> List[str]:
PartialState().wait_for_everyone()
def _lowercase ( __snake_case ,__snake_case ) -> List[str]:
if PartialState().distributed_type == DistributedType.TPU:
xm.save(__snake_case ,__snake_case )
elif PartialState().local_process_index == 0:
torch.save(__snake_case ,__snake_case )
@contextmanager
def _lowercase ( **__snake_case ) -> Optional[int]:
for key, value in kwargs.items():
__lowerCAmelCase : List[Any] = str(__snake_case )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def _lowercase ( __snake_case ) -> Tuple:
if not hasattr(__snake_case ,"__qualname__" ) and not hasattr(__snake_case ,"__name__" ):
__lowerCAmelCase : Union[str, Any] = getattr(__snake_case ,"__class__" ,__snake_case )
if hasattr(__snake_case ,"__qualname__" ):
return obj.__qualname__
if hasattr(__snake_case ,"__name__" ):
return obj.__name__
return str(__snake_case )
def _lowercase ( __snake_case ,__snake_case ) -> Dict:
for key, value in source.items():
if isinstance(__snake_case ,__snake_case ):
__lowerCAmelCase : Tuple = destination.setdefault(__snake_case ,{} )
merge_dicts(__snake_case ,__snake_case )
else:
__lowerCAmelCase : List[Any] = value
return destination
def _lowercase ( __snake_case = None ) -> bool:
if port is None:
__lowerCAmelCase : str = 29_500
with socket.socket(socket.AF_INET ,socket.SOCK_STREAM ) as s:
return s.connect_ex(("localhost", port) ) == 0 | 269 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _lowercase ( __snake_case = "laptop" ) -> DataFrame:
__lowerCAmelCase : str = F"""https://www.amazon.in/laptop/s?k={product}"""
__lowerCAmelCase : Union[str, Any] = {
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
"Accept-Language": "en-US, en;q=0.5",
}
__lowerCAmelCase : List[str] = BeautifulSoup(requests.get(__snake_case ,headers=__snake_case ).text )
# Initialize a Pandas dataframe with the column titles
__lowerCAmelCase : Dict = DataFrame(
columns=[
"Product Title",
"Product Link",
"Current Price of the product",
"Product Rating",
"MRP of the product",
"Discount",
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
"div" ,attrs={"class": "s-result-item", "data-component-type": "s-search-result"} ,) ,soup.find_all("div" ,attrs={"class": "a-row a-size-base a-color-base"} ) ,):
try:
__lowerCAmelCase : Any = item.ha.text
__lowerCAmelCase : Union[str, Any] = "https://www.amazon.in/" + item.ha.a["href"]
__lowerCAmelCase : Any = item.find("span" ,attrs={"class": "a-offscreen"} ).text
try:
__lowerCAmelCase : Union[str, Any] = item.find("span" ,attrs={"class": "a-icon-alt"} ).text
except AttributeError:
__lowerCAmelCase : Optional[Any] = "Not available"
try:
__lowerCAmelCase : Union[str, Any] = (
"₹"
+ item.find(
"span" ,attrs={"class": "a-price a-text-price"} ).text.split("₹" )[1]
)
except AttributeError:
__lowerCAmelCase : Dict = ""
try:
__lowerCAmelCase : str = float(
(
(
float(product_mrp.strip("₹" ).replace("," ,"" ) )
- float(product_price.strip("₹" ).replace("," ,"" ) )
)
/ float(product_mrp.strip("₹" ).replace("," ,"" ) )
)
* 100 )
except ValueError:
__lowerCAmelCase : List[str] = float("nan" )
except AttributeError:
pass
__lowerCAmelCase : int = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
__lowerCAmelCase : Union[str, Any] = " "
__lowerCAmelCase : Union[str, Any] = " "
data_frame.index += 1
return data_frame
if __name__ == "__main__":
__snake_case : Any = 'headphones'
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""") | 269 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
_lowerCAmelCase :int = logging.get_logger(__name__)
if is_vision_available():
import PIL
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ =['''pixel_values''']
def __init__( self , A = True , A = None , A = PILImageResampling.BICUBIC , A = True , A = None , A = True , A = 1 / 2_5_5 , A = True , A = None , A = None , A = True , **A , ) -> None:
super().__init__(**A )
_UpperCAmelCase : Optional[int] = size if size is not None else {'''shortest_edge''': 2_2_4}
_UpperCAmelCase : Tuple = get_size_dict(A , default_to_square=A )
_UpperCAmelCase : Tuple = crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
_UpperCAmelCase : List[str] = get_size_dict(A , default_to_square=A , param_name='''crop_size''' )
_UpperCAmelCase : int = do_resize
_UpperCAmelCase : Optional[Any] = size
_UpperCAmelCase : int = resample
_UpperCAmelCase : Union[str, Any] = do_center_crop
_UpperCAmelCase : Dict = crop_size
_UpperCAmelCase : Optional[int] = do_rescale
_UpperCAmelCase : Tuple = rescale_factor
_UpperCAmelCase : List[str] = do_normalize
_UpperCAmelCase : Optional[int] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
_UpperCAmelCase : Optional[int] = image_std if image_std is not None else OPENAI_CLIP_STD
_UpperCAmelCase : Any = do_convert_rgb
def __lowerCAmelCase ( self , A , A , A = PILImageResampling.BICUBIC , A = None , **A , ) -> np.ndarray:
_UpperCAmelCase : List[Any] = get_size_dict(A , default_to_square=A )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
_UpperCAmelCase : Dict = get_resize_output_image_size(A , size=size['''shortest_edge'''] , default_to_square=A )
return resize(A , size=A , resample=A , data_format=A , **A )
def __lowerCAmelCase ( self , A , A , A = None , **A , ) -> np.ndarray:
_UpperCAmelCase : Any = get_size_dict(A )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(A , size=(size['''height'''], size['''width''']) , data_format=A , **A )
def __lowerCAmelCase ( self , A , A , A = None , **A , ) -> Optional[int]:
return rescale(A , scale=A , data_format=A , **A )
def __lowerCAmelCase ( self , A , A , A , A = None , **A , ) -> np.ndarray:
return normalize(A , mean=A , std=A , data_format=A , **A )
def __lowerCAmelCase ( self , A , A = None , A = None , A = None , A = None , A = None , A = None , A = None , A = None , A = None , A = None , A = None , A = None , A = ChannelDimension.FIRST , **A , ) -> PIL.Image.Image:
_UpperCAmelCase : List[Any] = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase : Any = size if size is not None else self.size
_UpperCAmelCase : Dict = get_size_dict(A , param_name='''size''' , default_to_square=A )
_UpperCAmelCase : List[Any] = resample if resample is not None else self.resample
_UpperCAmelCase : int = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCAmelCase : Tuple = crop_size if crop_size is not None else self.crop_size
_UpperCAmelCase : str = get_size_dict(A , param_name='''crop_size''' , default_to_square=A )
_UpperCAmelCase : List[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 : Any = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase : Tuple = image_mean if image_mean is not None else self.image_mean
_UpperCAmelCase : int = image_std if image_std is not None else self.image_std
_UpperCAmelCase : Tuple = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
_UpperCAmelCase : int = make_list_of_images(A )
if not valid_images(A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
_UpperCAmelCase : Optional[int] = [convert_to_rgb(A ) for image in images]
# All transformations expect numpy arrays.
_UpperCAmelCase : Tuple = [to_numpy_array(A ) for image in images]
if do_resize:
_UpperCAmelCase : List[str] = [self.resize(image=A , size=A , resample=A ) for image in images]
if do_center_crop:
_UpperCAmelCase : Optional[int] = [self.center_crop(image=A , size=A ) for image in images]
if do_rescale:
_UpperCAmelCase : Union[str, Any] = [self.rescale(image=A , scale=A ) for image in images]
if do_normalize:
_UpperCAmelCase : str = [self.normalize(image=A , mean=A , std=A ) for image in images]
_UpperCAmelCase : Dict = [to_channel_dimension_format(A , A ) for image in images]
_UpperCAmelCase : str = {'''pixel_values''': images}
return BatchFeature(data=A , tensor_type=A )
| 68 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase :int = logging.get_logger(__name__)
_lowerCAmelCase :Union[str, Any] = {
'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ ='''xlm'''
a__ ={
'''hidden_size''': '''emb_dim''',
'''num_attention_heads''': '''n_heads''',
'''num_hidden_layers''': '''n_layers''',
'''n_words''': '''vocab_size''', # For backward compatibility
}
def __init__( self , A=3_0_1_4_5 , A=2_0_4_8 , A=1_2 , A=1_6 , A=0.1 , A=0.1 , A=True , A=False , A=False , A=False , A=1 , A=True , A=5_1_2 , A=2_0_4_8**-0.5 , A=1E-12 , A=0.02 , A=0 , A=1 , A=2 , A=3 , A=5 , A=True , A="first" , A=True , A=None , A=True , A=0.1 , A=5 , A=5 , A=0 , A=0 , A=2 , A=0 , **A , ) -> Tuple:
_UpperCAmelCase : Dict = vocab_size
_UpperCAmelCase : Tuple = emb_dim
_UpperCAmelCase : Optional[Any] = n_layers
_UpperCAmelCase : Optional[Any] = n_heads
_UpperCAmelCase : Dict = dropout
_UpperCAmelCase : int = attention_dropout
_UpperCAmelCase : Optional[Any] = gelu_activation
_UpperCAmelCase : str = sinusoidal_embeddings
_UpperCAmelCase : Any = causal
_UpperCAmelCase : Optional[int] = asm
_UpperCAmelCase : List[str] = n_langs
_UpperCAmelCase : int = use_lang_emb
_UpperCAmelCase : Any = layer_norm_eps
_UpperCAmelCase : Any = bos_index
_UpperCAmelCase : Optional[Any] = eos_index
_UpperCAmelCase : List[str] = pad_index
_UpperCAmelCase : Optional[int] = unk_index
_UpperCAmelCase : Dict = mask_index
_UpperCAmelCase : Any = is_encoder
_UpperCAmelCase : Optional[Any] = max_position_embeddings
_UpperCAmelCase : List[Any] = embed_init_std
_UpperCAmelCase : Union[str, Any] = init_std
_UpperCAmelCase : List[str] = summary_type
_UpperCAmelCase : Dict = summary_use_proj
_UpperCAmelCase : str = summary_activation
_UpperCAmelCase : Union[str, Any] = summary_proj_to_labels
_UpperCAmelCase : Tuple = summary_first_dropout
_UpperCAmelCase : List[str] = start_n_top
_UpperCAmelCase : Tuple = end_n_top
_UpperCAmelCase : List[str] = mask_token_id
_UpperCAmelCase : Optional[int] = lang_id
if "n_words" in kwargs:
_UpperCAmelCase : Tuple = kwargs['''n_words''']
super().__init__(pad_token_id=A , bos_token_id=A , **A )
class _UpperCAmelCase ( a ):
'''simple docstring'''
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase : Tuple = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_UpperCAmelCase : Dict = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 68 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _a ( self ) -> str:
__UpperCamelCase =tempfile.mkdtemp()
# fmt: off
__UpperCamelCase =['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
__UpperCamelCase =dict(zip(A_ , range(len(A_ ) ) ) )
__UpperCamelCase =['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
__UpperCamelCase ={'unk_token': '<unk>'}
__UpperCamelCase =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__UpperCamelCase =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(A_ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(A_ ) )
__UpperCamelCase ={
'do_resize': True,
'size': 20,
'do_center_crop': True,
'crop_size': 18,
'do_normalize': True,
'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
__UpperCamelCase =os.path.join(self.tmpdirname , A_ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(A_ , A_ )
def _a ( self , **A_ ) -> List[Any]:
return CLIPTokenizer.from_pretrained(self.tmpdirname , **A_ )
def _a ( self , **A_ ) -> Any:
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **A_ )
def _a ( self , **A_ ) -> Optional[Any]:
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **A_ )
def _a ( self ) -> Any:
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Tuple:
__UpperCamelCase =[np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__UpperCamelCase =[Image.fromarray(np.moveaxis(A_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> List[str]:
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =self.get_rust_tokenizer()
__UpperCamelCase =self.get_image_processor()
__UpperCamelCase =CLIPProcessor(tokenizer=A_ , image_processor=A_ )
processor_slow.save_pretrained(self.tmpdirname )
__UpperCamelCase =CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=A_ )
__UpperCamelCase =CLIPProcessor(tokenizer=A_ , image_processor=A_ )
processor_fast.save_pretrained(self.tmpdirname )
__UpperCamelCase =CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , A_ )
self.assertIsInstance(processor_fast.tokenizer , A_ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , A_ )
self.assertIsInstance(processor_fast.image_processor , A_ )
def _a ( self ) -> Optional[Any]:
__UpperCamelCase =CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__UpperCamelCase =self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__UpperCamelCase =self.get_image_processor(do_normalize=A_ , padding_value=1.0 )
__UpperCamelCase =CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=A_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , A_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A_ )
def _a ( self ) -> Tuple:
__UpperCamelCase =self.get_image_processor()
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =CLIPProcessor(tokenizer=A_ , image_processor=A_ )
__UpperCamelCase =self.prepare_image_inputs()
__UpperCamelCase =image_processor(A_ , return_tensors='np' )
__UpperCamelCase =processor(images=A_ , return_tensors='np' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self ) -> Union[str, Any]:
__UpperCamelCase =self.get_image_processor()
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =CLIPProcessor(tokenizer=A_ , image_processor=A_ )
__UpperCamelCase ='lower newer'
__UpperCamelCase =processor(text=A_ )
__UpperCamelCase =tokenizer(A_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self ) -> List[Any]:
__UpperCamelCase =self.get_image_processor()
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =CLIPProcessor(tokenizer=A_ , image_processor=A_ )
__UpperCamelCase ='lower newer'
__UpperCamelCase =self.prepare_image_inputs()
__UpperCamelCase =processor(text=A_ , images=A_ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(A_ ):
processor()
def _a ( self ) -> Union[str, Any]:
__UpperCamelCase =self.get_image_processor()
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =CLIPProcessor(tokenizer=A_ , image_processor=A_ )
__UpperCamelCase =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__UpperCamelCase =processor.batch_decode(A_ )
__UpperCamelCase =tokenizer.batch_decode(A_ )
self.assertListEqual(A_ , A_ )
def _a ( self ) -> Union[str, Any]:
__UpperCamelCase =self.get_image_processor()
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =CLIPProcessor(tokenizer=A_ , image_processor=A_ )
__UpperCamelCase ='lower newer'
__UpperCamelCase =self.prepare_image_inputs()
__UpperCamelCase =processor(text=A_ , images=A_ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 62 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
# Throw an error if user passes both BYO and on-demand cluster args
# Otherwise, use default values
UpperCAmelCase : str = argparse.ArgumentParser()
parser.add_argument("--user", type=str, default="ubuntu")
parser.add_argument("--host", type=str, default="localhost")
parser.add_argument("--key_path", type=str, default=None)
parser.add_argument("--instance", type=str, default="V100:1")
parser.add_argument("--provider", type=str, default="cheapest")
parser.add_argument("--use_spot", type=bool, default=False)
parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
UpperCAmelCase, UpperCAmelCase : Optional[Any] = parser.parse_known_args()
if args.host != "localhost":
if args.instance != "V100:1" or args.provider != "cheapest":
raise ValueError("Cannot specify both BYO and on-demand cluster args")
UpperCAmelCase : Dict = rh.cluster(
name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
)
else:
UpperCAmelCase : str = rh.cluster(
name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
)
UpperCAmelCase : str = args.example.rsplit("/", 1)[0]
# Set up remote environment
cluster.install_packages(["pip:./"]) # Installs transformers from local source
# Note transformers is copied into the home directory on the remote machine, so we can install from there
cluster.run([f"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])
# Run example. You can bypass the CLI wrapper and paste your own code here.
cluster.run([f"""python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}"""])
# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
# from my_script... import train
# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
# launch_train_gpu = rh.function(fn=train,
# system=gpu,
# reqs=reqs,
# name='train_bert_glue')
#
# We can pass in arguments just like we would to a function:
# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
# stream_logs=True)
| 252 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
__snake_case = logging.get_logger(__name__)
@dataclass
class lowercase__ :
def __init__( self : List[str] , UpperCAmelCase_ : Any=False , UpperCAmelCase_ : Tuple=False , UpperCAmelCase_ : Dict=6.0 , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : List[str]=False , UpperCAmelCase_ : int=False , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : Union[str, Any]="fp4" , UpperCAmelCase_ : List[str]=False , **UpperCAmelCase_ : Optional[Any] , ):
SCREAMING_SNAKE_CASE__ = load_in_abit
SCREAMING_SNAKE_CASE__ = load_in_abit
SCREAMING_SNAKE_CASE__ = llm_inta_threshold
SCREAMING_SNAKE_CASE__ = llm_inta_skip_modules
SCREAMING_SNAKE_CASE__ = llm_inta_enable_fpaa_cpu_offload
SCREAMING_SNAKE_CASE__ = llm_inta_has_fpaa_weight
SCREAMING_SNAKE_CASE__ = bnb_abit_quant_type
SCREAMING_SNAKE_CASE__ = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
SCREAMING_SNAKE_CASE__ = torch.floataa
elif isinstance(__lowercase , __lowercase ):
SCREAMING_SNAKE_CASE__ = getattr(__lowercase , __lowercase )
elif isinstance(__lowercase , torch.dtype ):
SCREAMING_SNAKE_CASE__ = bnb_abit_compute_dtype
else:
raise ValueError('bnb_4bit_compute_dtype must be a string or a torch.dtype' )
self.post_init()
def A_ ( self : str ):
if not isinstance(self.llm_inta_threshold , __lowercase ):
raise ValueError('llm_int8_threshold must be a float' )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , __lowercase ):
raise ValueError('llm_int8_skip_modules must be a list of strings' )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , __lowercase ):
raise ValueError('llm_int8_enable_fp32_cpu_offload must be a boolean' )
if not isinstance(self.llm_inta_has_fpaa_weight , __lowercase ):
raise ValueError('llm_int8_has_fp16_weight must be a boolean' )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError('bnb_4bit_compute_dtype must be torch.dtype' )
if not isinstance(self.bnb_abit_quant_type , __lowercase ):
raise ValueError('bnb_4bit_quant_type must be a string' )
if not isinstance(self.bnb_abit_use_double_quant , __lowercase ):
raise ValueError('bnb_4bit_use_double_quant must be a boolean' )
if self.load_in_abit and not version.parse(importlib.metadata.version('bitsandbytes' ) ) >= version.parse(
'0.39.0' ):
raise ValueError(
'4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version' )
def A_ ( self : Optional[int] ):
return self.load_in_abit or self.load_in_abit
def A_ ( self : Dict ):
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def A_ ( cls : Dict , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Any , **UpperCAmelCase_ : List[str] ):
SCREAMING_SNAKE_CASE__ = cls(**__lowercase )
SCREAMING_SNAKE_CASE__ = []
for key, value in kwargs.items():
if hasattr(__lowercase , __lowercase ):
setattr(__lowercase , __lowercase , __lowercase )
to_remove.append(__lowercase )
for key in to_remove:
kwargs.pop(__lowercase , __lowercase )
if return_unused_kwargs:
return config, kwargs
else:
return config
def A_ ( self : Union[str, Any] , UpperCAmelCase_ : Dict ):
with open(__lowercase , 'w' , encoding='utf-8' ) as writer:
SCREAMING_SNAKE_CASE__ = self.to_dict()
SCREAMING_SNAKE_CASE__ = json.dumps(__lowercase , indent=2 , sort_keys=__lowercase ) + '''\n'''
writer.write(__lowercase )
def A_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE__ = str(output['bnb_4bit_compute_dtype'] ).split('.' )[1]
return output
def __repr__( self : Dict ):
return F'{self.__class__.__name__} {self.to_json_string()}'
def A_ ( self : str , UpperCAmelCase_ : Tuple = True ):
if use_diff is True:
SCREAMING_SNAKE_CASE__ = self.to_diff_dict()
else:
SCREAMING_SNAKE_CASE__ = self.to_dict()
return json.dumps(__lowercase , indent=2 , sort_keys=__lowercase ) + "\n"
def A_ ( self : Dict ):
SCREAMING_SNAKE_CASE__ = self.to_dict()
# get the default config dict
SCREAMING_SNAKE_CASE__ = BitsAndBytesConfig().to_dict()
SCREAMING_SNAKE_CASE__ = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
SCREAMING_SNAKE_CASE__ = value
return serializable_config_dict
| 367 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class lowercase__ ( _UpperCAmelCase , unittest.TestCase ):
A__ : Tuple =FlaxAutoencoderKL
@property
def A_ ( self : Any ):
SCREAMING_SNAKE_CASE__ = 4
SCREAMING_SNAKE_CASE__ = 3
SCREAMING_SNAKE_CASE__ = (32, 32)
SCREAMING_SNAKE_CASE__ = jax.random.PRNGKey(0 )
SCREAMING_SNAKE_CASE__ = jax.random.uniform(UpperCAmelCase_ , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def A_ ( self : Tuple ):
SCREAMING_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,
}
SCREAMING_SNAKE_CASE__ = self.dummy_input
return init_dict, inputs_dict
| 169 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase = False ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
__lowercase : List[Any] = f"""Expected string as input, found {type(__lowerCamelCase )}"""
raise ValueError(__lowerCamelCase )
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
__lowercase : Tuple = f"""Expected boolean as use_pascal parameter, found {type(__lowerCamelCase )}"""
raise ValueError(__lowerCamelCase )
__lowercase : Optional[int] = input_str.split('''_''' )
__lowercase : Dict = 0 if use_pascal else 1
__lowercase : Tuple = words[start_index:]
__lowercase : List[Any] = [word[0].upper() + word[1:] for word in words_to_capitalize]
__lowercase : List[Any] = '''''' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 249 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
_a = ['image_processor', 'tokenizer']
_a = 'BlipImageProcessor'
_a = 'AutoTokenizer'
def __init__( self : Tuple, lowerCamelCase : List[str], lowerCamelCase : Dict )-> str:
lowerCamelCase__ : Any =False
super().__init__(lowerCamelCase, lowerCamelCase )
lowerCamelCase__ : List[str] =self.image_processor
def __call__( self : Union[str, Any], lowerCamelCase : ImageInput = None, lowerCamelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, lowerCamelCase : bool = True, lowerCamelCase : Union[bool, str, PaddingStrategy] = False, lowerCamelCase : Union[bool, str, TruncationStrategy] = None, lowerCamelCase : Optional[int] = None, lowerCamelCase : int = 0, lowerCamelCase : Optional[int] = None, lowerCamelCase : Optional[bool] = None, lowerCamelCase : bool = False, lowerCamelCase : bool = False, lowerCamelCase : bool = False, lowerCamelCase : bool = False, lowerCamelCase : bool = False, lowerCamelCase : bool = True, lowerCamelCase : Optional[Union[str, TensorType]] = None, **lowerCamelCase : List[str], )-> BatchEncoding:
if images is None and text is None:
raise ValueError('''You have to specify either images or text.''' )
# Get only text
if images is None:
lowerCamelCase__ : str =self.tokenizer
lowerCamelCase__ : str =self.tokenizer(
text=lowerCamelCase, add_special_tokens=lowerCamelCase, padding=lowerCamelCase, truncation=lowerCamelCase, max_length=lowerCamelCase, stride=lowerCamelCase, pad_to_multiple_of=lowerCamelCase, return_attention_mask=lowerCamelCase, return_overflowing_tokens=lowerCamelCase, return_special_tokens_mask=lowerCamelCase, return_offsets_mapping=lowerCamelCase, return_token_type_ids=lowerCamelCase, return_length=lowerCamelCase, verbose=lowerCamelCase, return_tensors=lowerCamelCase, **lowerCamelCase, )
return text_encoding
# add pixel_values
lowerCamelCase__ : Optional[int] =self.image_processor(lowerCamelCase, return_tensors=lowerCamelCase )
if text is not None:
lowerCamelCase__ : Union[str, Any] =self.tokenizer(
text=lowerCamelCase, add_special_tokens=lowerCamelCase, padding=lowerCamelCase, truncation=lowerCamelCase, max_length=lowerCamelCase, stride=lowerCamelCase, pad_to_multiple_of=lowerCamelCase, return_attention_mask=lowerCamelCase, return_overflowing_tokens=lowerCamelCase, return_special_tokens_mask=lowerCamelCase, return_offsets_mapping=lowerCamelCase, return_token_type_ids=lowerCamelCase, return_length=lowerCamelCase, verbose=lowerCamelCase, return_tensors=lowerCamelCase, **lowerCamelCase, )
else:
lowerCamelCase__ : Optional[Any] =None
if text_encoding is not None:
encoding_image_processor.update(lowerCamelCase )
return encoding_image_processor
def snake_case ( self : str, *lowerCamelCase : Any, **lowerCamelCase : List[str] )-> Union[str, Any]:
return self.tokenizer.batch_decode(*lowerCamelCase, **lowerCamelCase )
def snake_case ( self : Dict, *lowerCamelCase : str, **lowerCamelCase : str )-> Union[str, Any]:
return self.tokenizer.decode(*lowerCamelCase, **lowerCamelCase )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def snake_case ( self : List[str] )-> List[str]:
lowerCamelCase__ : Union[str, Any] =self.tokenizer.model_input_names
lowerCamelCase__ : List[str] =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 238 | 0 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
lowerCamelCase_ = logging.get_logger(__name__)
# General docstring
lowerCamelCase_ = '''PoolFormerConfig'''
# Base docstring
lowerCamelCase_ = '''sail/poolformer_s12'''
lowerCamelCase_ = [1, 512, 7, 7]
# Image classification docstring
lowerCamelCase_ = '''sail/poolformer_s12'''
lowerCamelCase_ = '''tabby, tabby cat'''
lowerCamelCase_ = [
'''sail/poolformer_s12''',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def UpperCamelCase( lowercase_ , lowercase_ = 0.0 , lowercase_ = False ) -> List[str]:
'''simple docstring'''
if drop_prob == 0.0 or not training:
return input
snake_case_ = 1 - drop_prob
snake_case_ = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
snake_case_ = keep_prob + torch.rand(lowercase_ , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
snake_case_ = input.div(lowercase_ ) * random_tensor
return output
class __lowerCamelCase ( nn.Module ):
def __init__( self , lowerCamelCase = None ) -> None:
super().__init__()
snake_case_ = drop_prob
def lowerCAmelCase_ ( self , lowerCamelCase ) -> torch.Tensor:
return drop_path(lowerCamelCase , self.drop_prob , self.training )
def lowerCAmelCase_ ( self ) -> str:
return "p={}".format(self.drop_prob )
class __lowerCamelCase ( nn.Module ):
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=None ) -> Union[str, Any]:
super().__init__()
snake_case_ = patch_size if isinstance(lowerCamelCase , collections.abc.Iterable ) else (patch_size, patch_size)
snake_case_ = stride if isinstance(lowerCamelCase , collections.abc.Iterable ) else (stride, stride)
snake_case_ = padding if isinstance(lowerCamelCase , collections.abc.Iterable ) else (padding, padding)
snake_case_ = nn.Convad(lowerCamelCase , lowerCamelCase , kernel_size=lowerCamelCase , stride=lowerCamelCase , padding=lowerCamelCase )
snake_case_ = norm_layer(lowerCamelCase ) if norm_layer else nn.Identity()
def lowerCAmelCase_ ( self , lowerCamelCase ) -> Any:
snake_case_ = self.projection(lowerCamelCase )
snake_case_ = self.norm(lowerCamelCase )
return embeddings
class __lowerCamelCase ( nn.GroupNorm ):
def __init__( self , lowerCamelCase , **lowerCamelCase ) -> Optional[Any]:
super().__init__(1 , lowerCamelCase , **lowerCamelCase )
class __lowerCamelCase ( nn.Module ):
def __init__( self , lowerCamelCase ) -> Any:
super().__init__()
snake_case_ = nn.AvgPoolad(lowerCamelCase , stride=1 , padding=pool_size // 2 , count_include_pad=lowerCamelCase )
def lowerCAmelCase_ ( self , lowerCamelCase ) -> int:
return self.pool(lowerCamelCase ) - hidden_states
class __lowerCamelCase ( nn.Module ):
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
super().__init__()
snake_case_ = nn.Convad(lowerCamelCase , lowerCamelCase , 1 )
snake_case_ = nn.Convad(lowerCamelCase , lowerCamelCase , 1 )
snake_case_ = PoolFormerDropPath(lowerCamelCase )
if isinstance(config.hidden_act , lowerCamelCase ):
snake_case_ = ACTaFN[config.hidden_act]
else:
snake_case_ = config.hidden_act
def lowerCAmelCase_ ( self , lowerCamelCase ) -> List[str]:
snake_case_ = self.conva(lowerCamelCase )
snake_case_ = self.act_fn(lowerCamelCase )
snake_case_ = self.drop(lowerCamelCase )
snake_case_ = self.conva(lowerCamelCase )
snake_case_ = self.drop(lowerCamelCase )
return hidden_states
class __lowerCamelCase ( nn.Module ):
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
super().__init__()
snake_case_ = PoolFormerPooling(lowerCamelCase )
snake_case_ = PoolFormerOutput(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
snake_case_ = PoolFormerGroupNorm(lowerCamelCase )
snake_case_ = PoolFormerGroupNorm(lowerCamelCase )
# Useful for training neural nets
snake_case_ = PoolFormerDropPath(lowerCamelCase ) if drop_path > 0.0 else nn.Identity()
snake_case_ = config.use_layer_scale
if config.use_layer_scale:
snake_case_ = nn.Parameter(
config.layer_scale_init_value * torch.ones((lowerCamelCase) ) , requires_grad=lowerCamelCase )
snake_case_ = nn.Parameter(
config.layer_scale_init_value * torch.ones((lowerCamelCase) ) , requires_grad=lowerCamelCase )
def lowerCAmelCase_ ( self , lowerCamelCase ) -> Optional[int]:
if self.use_layer_scale:
snake_case_ = self.pooling(self.before_norm(lowerCamelCase ) )
snake_case_ = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
snake_case_ = hidden_states + self.drop_path(lowerCamelCase )
snake_case_ = ()
snake_case_ = self.output(self.after_norm(lowerCamelCase ) )
snake_case_ = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
snake_case_ = hidden_states + self.drop_path(lowerCamelCase )
snake_case_ = (output,) + outputs
return outputs
else:
snake_case_ = self.drop_path(self.pooling(self.before_norm(lowerCamelCase ) ) )
# First residual connection
snake_case_ = pooling_output + hidden_states
snake_case_ = ()
# Second residual connection inside the PoolFormerOutput block
snake_case_ = self.drop_path(self.output(self.after_norm(lowerCamelCase ) ) )
snake_case_ = hidden_states + layer_output
snake_case_ = (output,) + outputs
return outputs
class __lowerCamelCase ( nn.Module ):
def __init__( self , lowerCamelCase ) -> Optional[Any]:
super().__init__()
snake_case_ = config
# stochastic depth decay rule
snake_case_ = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
snake_case_ = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
snake_case_ = nn.ModuleList(lowerCamelCase )
# Transformer blocks
snake_case_ = []
snake_case_ = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
snake_case_ = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
lowerCamelCase , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(lowerCamelCase ) )
snake_case_ = nn.ModuleList(lowerCamelCase )
def lowerCAmelCase_ ( self , lowerCamelCase , lowerCamelCase=False , lowerCamelCase=True ) -> Union[str, Any]:
snake_case_ = () if output_hidden_states else None
snake_case_ = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
snake_case_ , snake_case_ = layers
# Get patch embeddings from hidden_states
snake_case_ = embedding_layer(lowerCamelCase )
# Send the embeddings through the blocks
for _, blk in enumerate(lowerCamelCase ):
snake_case_ = blk(lowerCamelCase )
snake_case_ = layer_outputs[0]
if output_hidden_states:
snake_case_ = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=lowerCamelCase , hidden_states=lowerCamelCase )
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[Any] = PoolFormerConfig
lowerCamelCase_ : Any = 'poolformer'
lowerCamelCase_ : Tuple = 'pixel_values'
lowerCamelCase_ : str = True
def lowerCAmelCase_ ( self , lowerCamelCase ) -> List[str]:
if isinstance(lowerCamelCase , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(lowerCamelCase , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def lowerCAmelCase_ ( self , lowerCamelCase , lowerCamelCase=False ) -> Any:
if isinstance(lowerCamelCase , lowerCamelCase ):
snake_case_ = value
lowerCamelCase_ = R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
lowerCamelCase_ = R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`PoolFormerImageProcessor.__call__`] for details.
'''
@add_start_docstrings(
'The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.' , __snake_case , )
class __lowerCamelCase ( __snake_case ):
def __init__( self , lowerCamelCase ) -> int:
super().__init__(lowerCamelCase )
snake_case_ = config
snake_case_ = PoolFormerEncoder(lowerCamelCase )
# Initialize weights and apply final processing
self.post_init()
def lowerCAmelCase_ ( self ) -> List[str]:
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(lowerCamelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowerCamelCase , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowerCAmelCase_ ( self , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , ) -> Union[Tuple, BaseModelOutputWithNoAttention]:
snake_case_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case_ = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("""You have to specify pixel_values""" )
snake_case_ = self.encoder(
lowerCamelCase , output_hidden_states=lowerCamelCase , return_dict=lowerCamelCase , )
snake_case_ = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=lowerCamelCase , hidden_states=encoder_outputs.hidden_states , )
class __lowerCamelCase ( nn.Module ):
def __init__( self , lowerCamelCase ) -> Optional[int]:
super().__init__()
snake_case_ = nn.Linear(config.hidden_size , config.hidden_size )
def lowerCAmelCase_ ( self , lowerCamelCase ) -> Tuple:
snake_case_ = self.dense(lowerCamelCase )
return output
@add_start_docstrings(
'\n PoolFormer Model transformer with an image classification head on top\n ' , __snake_case , )
class __lowerCamelCase ( __snake_case ):
def __init__( self , lowerCamelCase ) -> Optional[Any]:
super().__init__(lowerCamelCase )
snake_case_ = config.num_labels
snake_case_ = PoolFormerModel(lowerCamelCase )
# Final norm
snake_case_ = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
snake_case_ = (
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowerCamelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowerCamelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowerCAmelCase_ ( self , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , ) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
snake_case_ = return_dict if return_dict is not None else self.config.use_return_dict
snake_case_ = self.poolformer(
lowerCamelCase , output_hidden_states=lowerCamelCase , return_dict=lowerCamelCase , )
snake_case_ = outputs[0]
snake_case_ = self.classifier(self.norm(lowerCamelCase ).mean([-2, -1] ) )
snake_case_ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
snake_case_ = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
snake_case_ = """single_label_classification"""
else:
snake_case_ = """multi_label_classification"""
if self.config.problem_type == "regression":
snake_case_ = MSELoss()
if self.num_labels == 1:
snake_case_ = loss_fct(logits.squeeze() , labels.squeeze() )
else:
snake_case_ = loss_fct(lowerCamelCase , lowerCamelCase )
elif self.config.problem_type == "single_label_classification":
snake_case_ = CrossEntropyLoss()
snake_case_ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
snake_case_ = BCEWithLogitsLoss()
snake_case_ = loss_fct(lowerCamelCase , lowerCamelCase )
if not return_dict:
snake_case_ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=lowerCamelCase , logits=lowerCamelCase , hidden_states=outputs.hidden_states ) | 34 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance < 0:
raise ValueError("""Resistance cannot be negative""" )
if voltage == 0:
return {"voltage": float(current * resistance )}
elif current == 0:
return {"current": voltage / resistance}
elif resistance == 0:
return {"resistance": voltage / current}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod() | 34 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__A : Tuple = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = ['''DPTFeatureExtractor''']
__A : Any = ['''DPTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = [
'''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DPTForDepthEstimation''',
'''DPTForSemanticSegmentation''',
'''DPTModel''',
'''DPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
__A : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 33 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask
__snake_case = logging.getLogger(__name__)
class _lowerCAmelCase ( snake_case_ ):
__UpperCAmelCase : Optional[int] = '''token-classification'''
def __init__( self , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
if type(UpperCamelCase__ ) == dict:
snake_case : Optional[int] = Namespace(**UpperCamelCase__ )
snake_case : Optional[int] = import_module("tasks" )
try:
snake_case : Optional[int] = getattr(UpperCamelCase__ , hparams.task_type )
snake_case : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}' )
snake_case : str = self.token_classification_task.get_labels(hparams.labels )
snake_case : Union[str, Any] = CrossEntropyLoss().ignore_index
super().__init__(UpperCamelCase__ , len(self.labels ) , self.mode )
def lowerCamelCase ( self , **UpperCamelCase__ ) -> Any:
'''simple docstring'''
return self.model(**UpperCamelCase__ )
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
snake_case : Optional[Any] = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type != "distilbert":
snake_case : Optional[int] = (
batch[2] if self.config.model_type in ["bert", "xlnet"] else None
) # XLM and RoBERTa don"t use token_type_ids
snake_case : List[Any] = self(**UpperCamelCase__ )
snake_case : Union[str, Any] = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def lowerCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
snake_case : Optional[Any] = self.hparams
for mode in ["train", "dev", "test"]:
snake_case : Optional[Any] = self._feature_file(UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ) and not args.overwrite_cache:
logger.info("Loading features from cached file %s" , UpperCamelCase__ )
snake_case : List[str] = torch.load(UpperCamelCase__ )
else:
logger.info("Creating features from dataset file at %s" , args.data_dir )
snake_case : Optional[Any] = self.token_classification_task.read_examples_from_file(args.data_dir , UpperCamelCase__ )
snake_case : Dict = self.token_classification_task.convert_examples_to_features(
UpperCamelCase__ , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ["xlnet"] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=UpperCamelCase__ , pad_on_left=bool(self.config.model_type in ["xlnet"] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info("Saving features into cached file %s" , UpperCamelCase__ )
torch.save(UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False ) -> DataLoader:
'''simple docstring'''
snake_case : Optional[Any] = self._feature_file(UpperCamelCase__ )
logger.info("Loading features from cached file %s" , UpperCamelCase__ )
snake_case : Any = torch.load(UpperCamelCase__ )
snake_case : Tuple = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
snake_case : List[str] = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
if features[0].token_type_ids is not None:
snake_case : Tuple = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
else:
snake_case : Union[str, Any] = torch.tensor([0 for f in features] , dtype=torch.long )
# HACK(we will not use this anymore soon)
snake_case : Optional[int] = torch.tensor([f.label_ids for f in features] , dtype=torch.long )
return DataLoader(
TensorDataset(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) , batch_size=UpperCamelCase__ )
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
"""Compute validation""" ""
snake_case : Any = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type != "distilbert":
snake_case : Optional[int] = (
batch[2] if self.config.model_type in ["bert", "xlnet"] else None
) # XLM and RoBERTa don"t use token_type_ids
snake_case : Optional[int] = self(**UpperCamelCase__ )
snake_case ,snake_case : str = outputs[:2]
snake_case : Optional[int] = logits.detach().cpu().numpy()
snake_case : str = inputs["labels"].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def lowerCamelCase ( self , UpperCamelCase__ ) -> Any:
'''simple docstring'''
snake_case : Dict = torch.stack([x["val_loss"] for x in outputs] ).mean()
snake_case : List[str] = np.concatenate([x["pred"] for x in outputs] , axis=0 )
snake_case : Any = np.argmax(UpperCamelCase__ , axis=2 )
snake_case : Dict = np.concatenate([x["target"] for x in outputs] , axis=0 )
snake_case : Tuple = dict(enumerate(self.labels ) )
snake_case : str = [[] for _ in range(out_label_ids.shape[0] )]
snake_case : List[Any] = [[] for _ in range(out_label_ids.shape[0] )]
for i in range(out_label_ids.shape[0] ):
for j in range(out_label_ids.shape[1] ):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
snake_case : Union[str, Any] = {
"val_loss": val_loss_mean,
"accuracy_score": accuracy_score(UpperCamelCase__ , UpperCamelCase__ ),
"precision": precision_score(UpperCamelCase__ , UpperCamelCase__ ),
"recall": recall_score(UpperCamelCase__ , UpperCamelCase__ ),
"f1": fa_score(UpperCamelCase__ , UpperCamelCase__ ),
}
snake_case : int = dict(results.items() )
snake_case : Union[str, Any] = results
return ret, preds_list, out_label_list
def lowerCamelCase ( self , UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
snake_case ,snake_case ,snake_case : Optional[Any] = self._eval_end(UpperCamelCase__ )
snake_case : Tuple = ret["log"]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def lowerCamelCase ( self , UpperCamelCase__ ) -> Any:
'''simple docstring'''
snake_case ,snake_case ,snake_case : List[Any] = self._eval_end(UpperCamelCase__ )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
snake_case : Optional[Any] = ret["log"]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> str:
'''simple docstring'''
BaseTransformer.add_model_specific_args(UpperCamelCase__ , UpperCamelCase__ )
parser.add_argument(
"--task_type" , default="NER" , type=UpperCamelCase__ , help="Task type to fine tune in training (e.g. NER, POS, etc)" )
parser.add_argument(
"--max_seq_length" , default=128 , 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(
"--labels" , default="" , type=UpperCamelCase__ , help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used." , )
parser.add_argument(
"--gpus" , default=0 , type=UpperCamelCase__ , help="The number of GPUs allocated for this, it is by default 0 meaning none" , )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" )
return parser
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
__snake_case = NERTransformer.add_model_specific_args(parser, os.getcwd())
__snake_case = parser.parse_args()
__snake_case = NERTransformer(args)
__snake_case = generic_train(model, args)
if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this default format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L322
__snake_case = sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
__snake_case = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model)
| 203 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCamelCase : Optional[Any] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( __lowerCAmelCase , unittest.TestCase):
A: Tuple = XLMRobertaTokenizer
A: Union[str, Any] = XLMRobertaTokenizerFast
A: List[Any] = True
A: Optional[int] = True
def UpperCAmelCase__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
UpperCamelCase__ : List[Any] = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self : int ) -> int:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = '''<pad>'''
UpperCamelCase__ : Any = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__ ) , lowerCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__ ) , lowerCamelCase__ )
def UpperCAmelCase__ ( self : int ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ : int = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowerCamelCase__ ) , 1002 )
def UpperCAmelCase__ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1002 )
def UpperCAmelCase__ ( self : Any ) -> Dict:
'''simple docstring'''
UpperCamelCase__ : List[str] = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
UpperCamelCase__ : List[Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCamelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
UpperCamelCase__ : int = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
UpperCamelCase__ : Dict = tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
UpperCamelCase__ : Tuple = tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def UpperCAmelCase__ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
UpperCamelCase__ : Optional[Any] = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
UpperCamelCase__ : List[str] = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
UpperCamelCase__ : Union[str, Any] = self.tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = tempfile.mkdtemp()
UpperCamelCase__ : Optional[Any] = tokenizer_r.save_pretrained(lowerCamelCase__ )
UpperCamelCase__ : Union[str, Any] = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
UpperCamelCase__ : Optional[Any] = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
UpperCamelCase__ : Any = tokenizer_r.from_pretrained(lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=True
UpperCamelCase__ : List[str] = tempfile.mkdtemp()
UpperCamelCase__ : Optional[Any] = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
UpperCamelCase__ : Dict = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
UpperCamelCase__ : List[str] = tokenizer_r.from_pretrained(lowerCamelCase__ )
UpperCamelCase__ : int = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=False
UpperCamelCase__ : Union[str, Any] = tempfile.mkdtemp()
UpperCamelCase__ : int = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
UpperCamelCase__ : List[Any] = tokenizer_r.from_pretrained(lowerCamelCase__ )
UpperCamelCase__ : Tuple = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
@cached_property
def UpperCAmelCase__ ( self : List[str] ) -> str:
'''simple docstring'''
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def UpperCAmelCase__ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCamelCase__ , f.name )
UpperCamelCase__ : Any = XLMRobertaTokenizer(f.name , keep_accents=lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = pickle.dumps(lowerCamelCase__ )
pickle.loads(lowerCamelCase__ )
def UpperCAmelCase__ ( self : int ) -> Tuple:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
UpperCamelCase__ : Tuple = self.get_tokenizer()
UpperCamelCase__ : List[str] = self.get_rust_tokenizer()
UpperCamelCase__ : Optional[int] = '''I was born in 92000, and this is falsé.'''
UpperCamelCase__ : Optional[int] = tokenizer.tokenize(lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = rust_tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
UpperCamelCase__ : str = tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = rust_tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
UpperCamelCase__ : str = self.get_rust_tokenizer()
UpperCamelCase__ : List[Any] = tokenizer.encode(lowerCamelCase__ )
UpperCamelCase__ : List[Any] = rust_tokenizer.encode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@slow
def UpperCAmelCase__ ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ : Dict = '''Hello World!'''
UpperCamelCase__ : int = [0, 35378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def UpperCAmelCase__ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
UpperCamelCase__ : str = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
179459,
124850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
10114,
711,
152,
20,
6,
5,
22376,
642,
1221,
15190,
34153,
450,
5608,
959,
1119,
57702,
136,
186,
47,
1098,
29367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
50901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def UpperCAmelCase__ ( self : List[Any] ) -> Dict:
'''simple docstring'''
UpperCamelCase__ : int = {'''input_ids''': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase__ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 351 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __magic_name__ ( __lowerCAmelCase):
A: Optional[int] = ["image_processor", "tokenizer"]
A: int = "FlavaImageProcessor"
A: List[str] = ("BertTokenizer", "BertTokenizerFast")
def __init__( self : int , lowerCamelCase__ : Dict=None , lowerCamelCase__ : Union[str, Any]=None , **lowerCamelCase__ : int ) -> int:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = 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__ : Union[str, Any] = kwargs.pop('''feature_extractor''' )
UpperCamelCase__ : str = 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__ )
UpperCamelCase__ : List[str] = self.image_processor
def __call__( self : int , lowerCamelCase__ : Optional[ImageInput] = None , lowerCamelCase__ : Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None , lowerCamelCase__ : bool = True , lowerCamelCase__ : Union[bool, str, PaddingStrategy] = False , lowerCamelCase__ : Union[bool, str, TruncationStrategy] = False , lowerCamelCase__ : Optional[int] = None , lowerCamelCase__ : int = 0 , lowerCamelCase__ : Optional[int] = None , lowerCamelCase__ : Optional[bool] = None , lowerCamelCase__ : Optional[bool] = None , lowerCamelCase__ : Optional[bool] = None , lowerCamelCase__ : Optional[bool] = None , lowerCamelCase__ : bool = False , lowerCamelCase__ : bool = False , lowerCamelCase__ : bool = False , lowerCamelCase__ : bool = False , lowerCamelCase__ : bool = True , lowerCamelCase__ : Optional[Union[str, TensorType]] = None , **lowerCamelCase__ : List[str] , ) -> Any:
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
UpperCamelCase__ : Dict = self.tokenizer(
text=lowerCamelCase__ , add_special_tokens=lowerCamelCase__ , padding=lowerCamelCase__ , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ , stride=lowerCamelCase__ , pad_to_multiple_of=lowerCamelCase__ , return_token_type_ids=lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , return_overflowing_tokens=lowerCamelCase__ , return_special_tokens_mask=lowerCamelCase__ , return_offsets_mapping=lowerCamelCase__ , return_length=lowerCamelCase__ , verbose=lowerCamelCase__ , return_tensors=lowerCamelCase__ , **lowerCamelCase__ , )
if images is not None:
UpperCamelCase__ : Optional[int] = self.image_processor(
lowerCamelCase__ , return_image_mask=lowerCamelCase__ , return_codebook_pixels=lowerCamelCase__ , return_tensors=lowerCamelCase__ , **lowerCamelCase__ , )
if text is not None and images is not None:
encoding.update(lowerCamelCase__ )
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowerCamelCase__ ) , tensor_type=lowerCamelCase__ )
def UpperCAmelCase__ ( self : Optional[Any] , *lowerCamelCase__ : str , **lowerCamelCase__ : int ) -> Optional[Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCamelCase__ , **lowerCamelCase__ )
def UpperCAmelCase__ ( self : Dict , *lowerCamelCase__ : Dict , **lowerCamelCase__ : int ) -> List[str]:
'''simple docstring'''
return self.tokenizer.decode(*lowerCamelCase__ , **lowerCamelCase__ )
@property
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = self.tokenizer.model_input_names
UpperCamelCase__ : Union[str, Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCAmelCase__ ( self : Tuple ) -> Dict:
'''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 UpperCAmelCase__ ( self : List[Any] ) -> Any:
'''simple docstring'''
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , lowerCamelCase__ , )
return self.image_processor
| 51 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['''VisionEncoderDecoderModel''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['''TFVisionEncoderDecoderModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['''FlaxVisionEncoderDecoderModel''']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
a_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 340 |
from __future__ import annotations
import os
from collections.abc import Mapping
a_ = tuple[int, int]
class lowercase__ :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase )-> None:
'''simple docstring'''
lowerCAmelCase__ = vertices
lowerCAmelCase__ = {
(min(__UpperCAmelCase ), max(__UpperCAmelCase )): weight for edge, weight in edges.items()
}
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase )-> None:
'''simple docstring'''
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
lowerCAmelCase__ = weight
def UpperCAmelCase ( self )-> Graph:
'''simple docstring'''
lowerCAmelCase__ = Graph({min(self.vertices )} , {} )
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
while len(subgraph.vertices ) < len(self.vertices ):
lowerCAmelCase__ = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
lowerCAmelCase__ = edge
lowerCAmelCase__ = weight
subgraph.add_edge(__UpperCAmelCase , __UpperCAmelCase )
return subgraph
def _a ( UpperCamelCase_ : str = "p107_network.txt" ) -> int:
"""simple docstring"""
lowerCAmelCase__ = os.path.abspath(os.path.dirname(UpperCamelCase_ ) )
lowerCAmelCase__ = os.path.join(UpperCamelCase_ , UpperCamelCase_ )
lowerCAmelCase__ = {}
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
with open(UpperCamelCase_ ) as f:
lowerCAmelCase__ = f.read().strip().split("\n" )
lowerCAmelCase__ = [line.split("," ) for line in data]
for edgea in range(1 , len(UpperCamelCase_ ) ):
for edgea in range(UpperCamelCase_ ):
if adjaceny_matrix[edgea][edgea] != "-":
lowerCAmelCase__ = int(adjaceny_matrix[edgea][edgea] )
lowerCAmelCase__ = Graph(set(range(len(UpperCamelCase_ ) ) ) , UpperCamelCase_ )
lowerCAmelCase__ = graph.prims_algorithm()
lowerCAmelCase__ = sum(graph.edges.values() )
lowerCAmelCase__ = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(F"{solution() = }")
| 340 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Any = "▁"
_lowerCamelCase : Any = {"vocab_file": "sentencepiece.bpe.model"}
_lowerCamelCase : Union[str, Any] = {
"vocab_file": {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
}
}
_lowerCamelCase : List[Any] = {
"facebook/xglm-564M": 2048,
}
class SCREAMING_SNAKE_CASE ( _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"""]
def __init__( self : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Dict="<s>" , UpperCamelCase__ : Tuple="</s>" , UpperCamelCase__ : Optional[Any]="</s>" , UpperCamelCase__ : Dict="<s>" , UpperCamelCase__ : Dict="<unk>" , UpperCamelCase__ : Union[str, Any]="<pad>" , UpperCamelCase__ : Optional[Dict[str, Any]] = None , **UpperCamelCase__ : Dict , ):
"""simple docstring"""
UpperCamelCase = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
UpperCamelCase = 7
UpperCamelCase = [f"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
UpperCamelCase = kwargs.get('additional_special_tokens' , [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase__ , )
UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(UpperCamelCase__ ) )
UpperCamelCase = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
UpperCamelCase = 1
# Mimic fairseq token-to-id alignment for the first 4 token
UpperCamelCase = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
UpperCamelCase = len(self.sp_model )
UpperCamelCase = {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(UpperCamelCase__ )
UpperCamelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : str ):
"""simple docstring"""
UpperCamelCase = self.__dict__.copy()
UpperCamelCase = None
UpperCamelCase = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : List[Any] , UpperCamelCase__ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
UpperCamelCase = {}
UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def A ( self : Optional[int] , UpperCamelCase__ : List[int] , UpperCamelCase__ : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
UpperCamelCase = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def A ( self : Optional[int] , UpperCamelCase__ : List[int] , UpperCamelCase__ : Optional[List[int]] = None , UpperCamelCase__ : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase__ , token_ids_a=UpperCamelCase__ , already_has_special_tokens=UpperCamelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(UpperCamelCase__ ))
return [1] + ([0] * len(UpperCamelCase__ )) + [1, 1] + ([0] * len(UpperCamelCase__ ))
def A ( self : Optional[int] , UpperCamelCase__ : List[int] , UpperCamelCase__ : Optional[List[int]] = None ):
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def A ( self : int ):
"""simple docstring"""
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def A ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = {self.convert_ids_to_tokens(UpperCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def A ( self : Optional[int] , UpperCamelCase__ : str ):
"""simple docstring"""
return self.sp_model.encode(UpperCamelCase__ , out_type=UpperCamelCase__ )
def A ( self : Optional[int] , UpperCamelCase__ : Union[str, Any] ):
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCamelCase = self.sp_model.PieceToId(UpperCamelCase__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def A ( self : str , UpperCamelCase__ : List[Any] ):
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def A ( self : Any , UpperCamelCase__ : Dict ):
"""simple docstring"""
UpperCamelCase = ''.join(UpperCamelCase__ ).replace(UpperCamelCase__ , ' ' ).strip()
return out_string
def A ( self : Any , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(UpperCamelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCamelCase = os.path.join(
UpperCamelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCamelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCamelCase__ , 'wb' ) as fi:
UpperCamelCase = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase__ )
return (out_vocab_file,)
| 249 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def __lowerCamelCase ( A__ ) -> Any:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = image.size
UpperCamelCase , UpperCamelCase = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
UpperCamelCase = image.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] )
UpperCamelCase = np.array(A__ ).astype(np.floataa ) / 255.0
UpperCamelCase = image[None].transpose(0 , 3 , 1 , 2 )
UpperCamelCase = torch.from_numpy(A__ )
return 2.0 * image - 1.0
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
def __init__( self : str , UpperCamelCase__ : VQModel , UpperCamelCase__ : UNetaDModel , UpperCamelCase__ : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] , ):
"""simple docstring"""
super().__init__()
self.register_modules(vqvae=UpperCamelCase__ , unet=UpperCamelCase__ , scheduler=UpperCamelCase__ )
@torch.no_grad()
def __call__( self : Union[str, Any] , UpperCamelCase__ : Union[torch.Tensor, PIL.Image.Image] = None , UpperCamelCase__ : Optional[int] = 1 , UpperCamelCase__ : Optional[int] = 1_0_0 , UpperCamelCase__ : Optional[float] = 0.0 , UpperCamelCase__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCamelCase__ : Optional[str] = "pil" , UpperCamelCase__ : bool = True , ):
"""simple docstring"""
if isinstance(UpperCamelCase__ , PIL.Image.Image ):
UpperCamelCase = 1
elif isinstance(UpperCamelCase__ , torch.Tensor ):
UpperCamelCase = image.shape[0]
else:
raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__ )}""" )
if isinstance(UpperCamelCase__ , PIL.Image.Image ):
UpperCamelCase = preprocess(UpperCamelCase__ )
UpperCamelCase , UpperCamelCase = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
UpperCamelCase = (batch_size, self.unet.config.in_channels // 2, height, width)
UpperCamelCase = next(self.unet.parameters() ).dtype
UpperCamelCase = randn_tensor(UpperCamelCase__ , generator=UpperCamelCase__ , device=self.device , dtype=UpperCamelCase__ )
UpperCamelCase = image.to(device=self.device , dtype=UpperCamelCase__ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(UpperCamelCase__ , device=self.device )
UpperCamelCase = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
UpperCamelCase = 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]
UpperCamelCase = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
UpperCamelCase = {}
if accepts_eta:
UpperCamelCase = eta
for t in self.progress_bar(UpperCamelCase__ ):
# concat latents and low resolution image in the channel dimension.
UpperCamelCase = torch.cat([latents, image] , dim=1 )
UpperCamelCase = self.scheduler.scale_model_input(UpperCamelCase__ , UpperCamelCase__ )
# predict the noise residual
UpperCamelCase = self.unet(UpperCamelCase__ , UpperCamelCase__ ).sample
# compute the previous noisy sample x_t -> x_t-1
UpperCamelCase = self.scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
# decode the image latents with the VQVAE
UpperCamelCase = self.vqvae.decode(UpperCamelCase__ ).sample
UpperCamelCase = torch.clamp(UpperCamelCase__ , -1.0 , 1.0 )
UpperCamelCase = image / 2 + 0.5
UpperCamelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCamelCase = self.numpy_to_pil(UpperCamelCase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCamelCase__ )
| 249 | 1 |
'''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 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
a : Union[str, Any] = logging.getLogger(__name__)
def _SCREAMING_SNAKE_CASE ( ) ->Tuple:
'''simple docstring'''
a : Dict = argparse.ArgumentParser(
description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." )
parser.add_argument("--file_path" , type=_lowercase , default="data/dump.txt" , help="The path to the data." )
parser.add_argument("--tokenizer_type" , type=_lowercase , default="bert" , choices=["bert", "roberta", "gpt2"] )
parser.add_argument("--tokenizer_name" , type=_lowercase , default="bert-base-uncased" , help="The tokenizer to use." )
parser.add_argument("--dump_file" , type=_lowercase , default="data/dump" , help="The dump file prefix." )
a : Dict = parser.parse_args()
logger.info(F"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
a : Optional[Any] = BertTokenizer.from_pretrained(args.tokenizer_name )
a : str = tokenizer.special_tokens_map["cls_token"] # `[CLS]`
a : List[str] = tokenizer.special_tokens_map["sep_token"] # `[SEP]`
elif args.tokenizer_type == "roberta":
a : Tuple = RobertaTokenizer.from_pretrained(args.tokenizer_name )
a : Union[str, Any] = tokenizer.special_tokens_map["cls_token"] # `<s>`
a : str = tokenizer.special_tokens_map["sep_token"] # `</s>`
elif args.tokenizer_type == "gpt2":
a : List[Any] = GPTaTokenizer.from_pretrained(args.tokenizer_name )
a : Optional[int] = tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
a : List[Any] = tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
logger.info(F"""Loading text from {args.file_path}""" )
with open(args.file_path , "r" , encoding="utf8" ) as fp:
a : List[Any] = fp.readlines()
logger.info("Start encoding" )
logger.info(F"""{len(_lowercase )} examples to process.""" )
a : Optional[Any] = []
a : Optional[Any] = 0
a : int = 1_0000
a : Dict = time.time()
for text in data:
a : List[Any] = F"""{bos} {text.strip()} {sep}"""
a : Optional[int] = tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
rslt.append(_lowercase )
iter += 1
if iter % interval == 0:
a : Optional[Any] = time.time()
logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
a : Optional[Any] = time.time()
logger.info("Finished binarization" )
logger.info(F"""{len(_lowercase )} examples processed.""" )
a : Optional[int] = F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
a : Tuple = tokenizer.vocab_size
if vocab_size < (1 << 16):
a : Optional[int] = [np.uintaa(_lowercase ) for d in rslt]
else:
a : Optional[Any] = [np.intaa(_lowercase ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F"""Dump to {dp_file}""" )
with open(_lowercase , "wb" ) as handle:
pickle.dump(rslt_ , _lowercase , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 105 | 0 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class lowerCAmelCase__ ( lowercase, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = RoFormerTokenizer
lowerCamelCase__ = RoFormerTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = True
def A_ ( self ):
super().setUp()
def A_ ( self , **lowercase ):
return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **lowercase )
def A_ ( self , **lowercase ):
return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **lowercase )
def A_ ( self ):
_lowerCamelCase : str = '永和服装饰品有限公司,今天天气非常好'
_lowerCamelCase : Optional[int] = '永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
return input_text, output_text
def A_ ( self ):
_lowerCamelCase : Optional[int] = self.get_tokenizer()
_lowerCamelCase, _lowerCamelCase : List[str] = self.get_chinese_input_output_texts()
_lowerCamelCase : List[Any] = tokenizer.tokenize(lowercase )
self.assertListEqual(lowercase , output_text.split() )
_lowerCamelCase : Union[str, Any] = tokens + [tokenizer.unk_token]
_lowerCamelCase : Optional[Any] = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase ) , lowercase )
def A_ ( self ):
_lowerCamelCase : int = self.get_rust_tokenizer()
_lowerCamelCase, _lowerCamelCase : int = self.get_chinese_input_output_texts()
_lowerCamelCase : int = tokenizer.tokenize(lowercase )
self.assertListEqual(lowercase , output_text.split() )
_lowerCamelCase : List[Any] = tokens + [tokenizer.unk_token]
_lowerCamelCase : Tuple = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase ) , lowercase )
def A_ ( self ):
pass
def A_ ( self ):
pass
def A_ ( self ):
pass | 12 |
"""simple docstring"""
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 ):
'''simple docstring'''
lowerCamelCase__ = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def A_ ( self , lowercase , lowercase , lowercase ):
_lowerCamelCase : Optional[int] = hf_hub_download(
repo_id='nateraw/video-demo' , filename='archery.mp4' , repo_type='dataset' )
_lowerCamelCase : Tuple = VideoClassificationPipeline(model=lowercase , image_processor=lowercase , top_k=2 )
_lowerCamelCase : List[str] = [
example_video_filepath,
'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
]
return video_classifier, examples
def A_ ( self , lowercase , lowercase ):
for example in examples:
_lowerCamelCase : Tuple = video_classifier(lowercase )
self.assertEqual(
lowercase , [
{'score': ANY(lowercase ), 'label': ANY(lowercase )},
{'score': ANY(lowercase ), 'label': ANY(lowercase )},
] , )
@require_torch
def A_ ( self ):
_lowerCamelCase : Optional[Any] = 'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
_lowerCamelCase : Tuple = VideoMAEFeatureExtractor(
size={'shortest_edge': 10} , crop_size={'height': 10, 'width': 10} )
_lowerCamelCase : Dict = pipeline(
'video-classification' , model=lowercase , feature_extractor=lowercase , frame_sampling_rate=4 )
_lowerCamelCase : Any = hf_hub_download(repo_id='nateraw/video-demo' , filename='archery.mp4' , repo_type='dataset' )
_lowerCamelCase : Dict = video_classifier(lowercase , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [{'score': 0.51_99, 'label': 'LABEL_0'}, {'score': 0.48_01, 'label': 'LABEL_1'}] , )
_lowerCamelCase : str = video_classifier(
[
video_file_path,
video_file_path,
] , top_k=2 , )
self.assertEqual(
nested_simplify(lowercase , 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 A_ ( self ):
pass | 12 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowerCamelCase__ : Tuple = logging.get_logger(__name__)
class _UpperCAmelCase ( __a):
def __init__( self , *_A , **_A ) -> None:
'''simple docstring'''
warnings.warn(
"""The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use LayoutLMv2ImageProcessor instead.""" , _A , )
super().__init__(*_A , **_A )
| 246 |
"""simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : int, _lowerCAmelCase : int ) -> int:
_UpperCAmelCase : str = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
_UpperCAmelCase : Dict = n - k
# Calculate C(n,k)
for i in range(_lowerCAmelCase ):
result *= n - i
result //= i + 1
return result
def UpperCamelCase ( _lowerCAmelCase : int ) -> int:
return binomial_coefficient(2 * node_count, _lowerCAmelCase ) // (node_count + 1)
def UpperCamelCase ( _lowerCAmelCase : int ) -> int:
if n < 0:
raise ValueError("""factorial() not defined for negative values""" )
_UpperCAmelCase : str = 1
for i in range(1, n + 1 ):
result *= i
return result
def UpperCamelCase ( _lowerCAmelCase : int ) -> int:
return catalan_number(_lowerCAmelCase ) * factorial(_lowerCAmelCase )
if __name__ == "__main__":
lowerCamelCase__ : Optional[Any] = int(input('''Enter the number of nodes: ''').strip() or 0)
if node_count <= 0:
raise ValueError('''We need some nodes to work with.''')
print(
F'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
F'''binary trees and {catalan_number(node_count)} binary search trees.'''
)
| 246 | 1 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
_lowerCAmelCase = [
# (stable-diffusion, HF Diffusers)
("time_embed.0.weight", "time_embedding.linear_1.weight"),
("time_embed.0.bias", "time_embedding.linear_1.bias"),
("time_embed.2.weight", "time_embedding.linear_2.weight"),
("time_embed.2.bias", "time_embedding.linear_2.bias"),
("input_blocks.0.0.weight", "conv_in.weight"),
("input_blocks.0.0.bias", "conv_in.bias"),
("out.0.weight", "conv_norm_out.weight"),
("out.0.bias", "conv_norm_out.bias"),
("out.2.weight", "conv_out.weight"),
("out.2.bias", "conv_out.bias"),
]
_lowerCAmelCase = [
# (stable-diffusion, HF Diffusers)
("in_layers.0", "norm1"),
("in_layers.2", "conv1"),
("out_layers.0", "norm2"),
("out_layers.3", "conv2"),
("emb_layers.1", "time_emb_proj"),
("skip_connection", "conv_shortcut"),
]
_lowerCAmelCase = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
_lowerCAmelCase = F'''down_blocks.{i}.resnets.{j}.'''
_lowerCAmelCase = F'''input_blocks.{3*i + j + 1}.0.'''
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
_lowerCAmelCase = F'''down_blocks.{i}.attentions.{j}.'''
_lowerCAmelCase = F'''input_blocks.{3*i + j + 1}.1.'''
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
_lowerCAmelCase = F'''up_blocks.{i}.resnets.{j}.'''
_lowerCAmelCase = F'''output_blocks.{3*i + j}.0.'''
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
_lowerCAmelCase = F'''up_blocks.{i}.attentions.{j}.'''
_lowerCAmelCase = F'''output_blocks.{3*i + j}.1.'''
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
_lowerCAmelCase = F'''down_blocks.{i}.downsamplers.0.conv.'''
_lowerCAmelCase = F'''input_blocks.{3*(i+1)}.0.op.'''
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
_lowerCAmelCase = F'''up_blocks.{i}.upsamplers.0.'''
_lowerCAmelCase = F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
_lowerCAmelCase = "mid_block.attentions.0."
_lowerCAmelCase = "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
_lowerCAmelCase = F'''mid_block.resnets.{j}.'''
_lowerCAmelCase = F'''middle_block.{2*j}.'''
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def UpperCamelCase ( a ) -> Dict:
'''simple docstring'''
# buyer beware: this is a *brittle* function,
# and correct output requires that all of these pieces interact in
# the exact order in which I have arranged them.
__magic_name__ = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
__magic_name__ = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
__magic_name__ = v.replace(a , a )
__magic_name__ = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
__magic_name__ = v.replace(a , a )
__magic_name__ = v
__magic_name__ = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
_lowerCAmelCase = [
# (stable-diffusion, HF Diffusers)
("nin_shortcut", "conv_shortcut"),
("norm_out", "conv_norm_out"),
("mid.attn_1.", "mid_block.attentions.0."),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
_lowerCAmelCase = F'''encoder.down_blocks.{i}.resnets.{j}.'''
_lowerCAmelCase = F'''encoder.down.{i}.block.{j}.'''
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
_lowerCAmelCase = F'''down_blocks.{i}.downsamplers.0.'''
_lowerCAmelCase = F'''down.{i}.downsample.'''
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
_lowerCAmelCase = F'''up_blocks.{i}.upsamplers.0.'''
_lowerCAmelCase = F'''up.{3-i}.upsample.'''
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
_lowerCAmelCase = F'''decoder.up_blocks.{i}.resnets.{j}.'''
_lowerCAmelCase = F'''decoder.up.{3-i}.block.{j}.'''
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
_lowerCAmelCase = F'''mid_block.resnets.{i}.'''
_lowerCAmelCase = F'''mid.block_{i+1}.'''
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
_lowerCAmelCase = [
# (stable-diffusion, HF Diffusers)
("norm.", "group_norm."),
("q.", "query."),
("k.", "key."),
("v.", "value."),
("proj_out.", "proj_attn."),
]
def UpperCamelCase ( a ) -> Dict:
'''simple docstring'''
# convert HF linear weights to SD conv2d weights
return w.reshape(*w.shape , 1 , 1 )
def UpperCamelCase ( a ) -> Tuple:
'''simple docstring'''
__magic_name__ = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
__magic_name__ = v.replace(a , a )
__magic_name__ = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
__magic_name__ = v.replace(a , a )
__magic_name__ = v
__magic_name__ = {v: vae_state_dict[k] for k, v in mapping.items()}
__magic_name__ = ['''q''', '''k''', '''v''', '''proj_out''']
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if F'''mid.attn_1.{weight_name}.weight''' in k:
print(F'''Reshaping {k} for SD format''' )
__magic_name__ = reshape_weight_for_sd(a )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
_lowerCAmelCase = [
# (stable-diffusion, HF Diffusers)
("resblocks.", "text_model.encoder.layers."),
("ln_1", "layer_norm1"),
("ln_2", "layer_norm2"),
(".c_fc.", ".fc1."),
(".c_proj.", ".fc2."),
(".attn", ".self_attn"),
("ln_final.", "transformer.text_model.final_layer_norm."),
("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
_lowerCAmelCase = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_lowerCAmelCase = re.compile("|".join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_lowerCAmelCase = {"q": 0, "k": 1, "v": 2}
def UpperCamelCase ( a ) -> Tuple:
'''simple docstring'''
__magic_name__ = {}
__magic_name__ = {}
__magic_name__ = {}
for k, v in text_enc_dict.items():
if (
k.endswith('''.self_attn.q_proj.weight''' )
or k.endswith('''.self_attn.k_proj.weight''' )
or k.endswith('''.self_attn.v_proj.weight''' )
):
__magic_name__ = k[: -len('''.q_proj.weight''' )]
__magic_name__ = k[-len('''q_proj.weight''' )]
if k_pre not in capture_qkv_weight:
__magic_name__ = [None, None, None]
__magic_name__ = v
continue
if (
k.endswith('''.self_attn.q_proj.bias''' )
or k.endswith('''.self_attn.k_proj.bias''' )
or k.endswith('''.self_attn.v_proj.bias''' )
):
__magic_name__ = k[: -len('''.q_proj.bias''' )]
__magic_name__ = k[-len('''q_proj.bias''' )]
if k_pre not in capture_qkv_bias:
__magic_name__ = [None, None, None]
__magic_name__ = v
continue
__magic_name__ = textenc_pattern.sub(lambda a : protected[re.escape(m.group(0 ) )] , a )
__magic_name__ = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing''' )
__magic_name__ = textenc_pattern.sub(lambda a : protected[re.escape(m.group(0 ) )] , a )
__magic_name__ = torch.cat(a )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing''' )
__magic_name__ = textenc_pattern.sub(lambda a : protected[re.escape(m.group(0 ) )] , a )
__magic_name__ = torch.cat(a )
return new_state_dict
def UpperCamelCase ( a ) -> int:
'''simple docstring'''
return text_enc_dict
if __name__ == "__main__":
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
parser.add_argument(
"--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
)
_lowerCAmelCase = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
_lowerCAmelCase = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
_lowerCAmelCase = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
_lowerCAmelCase = osp.join(args.model_path, "text_encoder", "model.safetensors")
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
_lowerCAmelCase = load_file(unet_path, device="cpu")
else:
_lowerCAmelCase = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
_lowerCAmelCase = torch.load(unet_path, map_location="cpu")
if osp.exists(vae_path):
_lowerCAmelCase = load_file(vae_path, device="cpu")
else:
_lowerCAmelCase = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
_lowerCAmelCase = torch.load(vae_path, map_location="cpu")
if osp.exists(text_enc_path):
_lowerCAmelCase = load_file(text_enc_path, device="cpu")
else:
_lowerCAmelCase = osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
_lowerCAmelCase = torch.load(text_enc_path, map_location="cpu")
# Convert the UNet model
_lowerCAmelCase = convert_unet_state_dict(unet_state_dict)
_lowerCAmelCase = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
_lowerCAmelCase = convert_vae_state_dict(vae_state_dict)
_lowerCAmelCase = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
_lowerCAmelCase = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
_lowerCAmelCase = {"transformer." + k: v for k, v in text_enc_dict.items()}
_lowerCAmelCase = convert_text_enc_state_dict_vaa(text_enc_dict)
_lowerCAmelCase = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
else:
_lowerCAmelCase = convert_text_enc_state_dict(text_enc_dict)
_lowerCAmelCase = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
_lowerCAmelCase = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
_lowerCAmelCase = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
_lowerCAmelCase = {"state_dict": state_dict}
torch.save(state_dict, args.checkpoint_path)
| 98 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCamelCase ( a="ro" , a="en" , a="wmt16" , a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('''run pip install datasets''' )
__magic_name__ = F'''{src_lang}-{tgt_lang}'''
print(F'''Converting {dataset}-{pair}''' )
__magic_name__ = datasets.load_dataset(a , a )
if save_dir is None:
__magic_name__ = F'''{dataset}-{pair}'''
__magic_name__ = Path(a )
save_dir.mkdir(exist_ok=a )
for split in ds.keys():
print(F'''Splitting {split} with {ds[split].num_rows} records''' )
# to save to val.source, val.target like summary datasets
__magic_name__ = '''val''' if split == '''validation''' else split
__magic_name__ = save_dir.joinpath(F'''{fn}.source''' )
__magic_name__ = save_dir.joinpath(F'''{fn}.target''' )
__magic_name__ = src_path.open('''w+''' )
__magic_name__ = tgt_path.open('''w+''' )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
__magic_name__ = x['''translation''']
src_fp.write(ex[src_lang] + '''\n''' )
tgt_fp.write(ex[tgt_lang] + '''\n''' )
print(F'''Saved {dataset} dataset to {save_dir}''' )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset)
| 98 | 1 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCAmelCase__ : Union[str, Any] ) -> str:
"""simple docstring"""
if not all(char in '01' for char in bin_string ):
raise ValueError('Non-binary value was passed to the function' )
if not bin_string:
raise ValueError('Empty string was passed to the function' )
lowerCAmelCase_ : int = ''
while len(_lowerCAmelCase ) % 3 != 0:
lowerCAmelCase_ : str = '0' + bin_string
lowerCAmelCase_ : Union[str, Any] = [
bin_string[index : index + 3]
for index in range(len(_lowerCAmelCase ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
lowerCAmelCase_ : Tuple = 0
for index, val in enumerate(_lowerCAmelCase ):
oct_val += int(2 ** (2 - index) * int(_lowerCAmelCase ) )
oct_string += str(_lowerCAmelCase )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 224 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = 100 ,) -> float:
__lowerCamelCase : Dict = x_start
__lowerCamelCase : int = fnc(_lowerCAmelCase )
__lowerCamelCase : Dict = 0.0
for _ in range(_lowerCAmelCase ):
# Approximates curve as a sequence of linear lines and sums their length
__lowerCamelCase : List[str] = (x_end - x_start) / steps + xa
__lowerCamelCase : List[Any] = fnc(_lowerCAmelCase )
length += math.hypot(xa - xa ,fxa - fxa )
# Increment step
__lowerCamelCase : Any = xa
__lowerCamelCase : Tuple = fxa
return length
if __name__ == "__main__":
def a_ ( _lowerCAmelCase ) -> Dict:
return math.sin(10 * x )
print('f(x) = sin(10 * x)')
print('The length of the curve from x = -10 to x = 10 is:')
_UpperCamelCase = 10
while i <= 100000:
print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
i *= 10
| 208 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : Dict , snake_case_ : Any , snake_case_ : List[Any] , snake_case_ : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
UpperCAmelCase_ = mf_knapsack(i - 1 , snake_case_ , snake_case_ , snake_case_ )
else:
UpperCAmelCase_ = max(
mf_knapsack(i - 1 , snake_case_ , snake_case_ , snake_case_ ) , mf_knapsack(i - 1 , snake_case_ , snake_case_ , j - wt[i - 1] ) + val[i - 1] , )
UpperCAmelCase_ = val
return f[i][j]
def lowerCAmelCase_ ( snake_case_ : List[str] , snake_case_ : Dict , snake_case_ : Dict , snake_case_ : Tuple ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
UpperCAmelCase_ = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
UpperCAmelCase_ = dp[i - 1][w_]
return dp[n][w_], dp
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : list , snake_case_ : list ) -> Union[str, Any]:
'''simple docstring'''
if not (isinstance(snake_case_ , (list, tuple) ) and isinstance(snake_case_ , (list, tuple) )):
raise ValueError(
"Both the weights and values vectors must be either lists or tuples" )
UpperCAmelCase_ = len(snake_case_ )
if num_items != len(snake_case_ ):
UpperCAmelCase_ = (
"The number of weights must be the same as the number of values.\n"
f"""But got {num_items} weights and {len(snake_case_ )} values"""
)
raise ValueError(snake_case_ )
for i in range(snake_case_ ):
if not isinstance(wt[i] , snake_case_ ):
UpperCAmelCase_ = (
"All weights must be integers but got weight of "
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(snake_case_ )
UpperCAmelCase_ , UpperCAmelCase_ = knapsack(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
UpperCAmelCase_ = set()
_construct_solution(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
return optimal_val, example_optional_set
def lowerCAmelCase_ ( snake_case_ : list , snake_case_ : list , snake_case_ : int , snake_case_ : int , snake_case_ : set ) -> Tuple:
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(snake_case_ , snake_case_ , i - 1 , snake_case_ , snake_case_ )
else:
optimal_set.add(snake_case_ )
_construct_solution(snake_case_ , snake_case_ , i - 1 , j - wt[i - 1] , snake_case_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_: List[Any] =[3, 2, 4, 4]
SCREAMING_SNAKE_CASE_: int =[4, 3, 2, 3]
SCREAMING_SNAKE_CASE_: str =4
SCREAMING_SNAKE_CASE_: Tuple =6
SCREAMING_SNAKE_CASE_: Optional[int] =[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_: Optional[Any] =knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_: Dict =knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('optimal_value = ', optimal_solution)
print('An optimal subset corresponding to the optimal value', optimal_subset)
| 106 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
SCREAMING_SNAKE_CASE_: List[str] =get_logger()
SCREAMING_SNAKE_CASE_: Optional[dict] =None
class __A ( TensorFormatter[Mapping, """jax.Array""", Mapping] ):
def __init__(self : List[Any] , __a : Optional[int]=None , __a : Any=None , **__a : Dict ):
super().__init__(features=__a )
import jax
from jaxlib.xla_client import Device
if isinstance(__a , __a ):
raise ValueError(
f"""Expected {device} to be a `str` not {type(__a )}, as `jaxlib.xla_extension.Device` """
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
UpperCAmelCase_ = device if isinstance(__a , __a ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
UpperCAmelCase_ = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f"""Device with string identifier {self.device} not listed among the available """
f"""devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default """
f"""device: {str(jax.devices()[0] )}.""" )
UpperCAmelCase_ = str(jax.devices()[0] )
UpperCAmelCase_ = jnp_array_kwargs
@staticmethod
def _lowercase ():
import jax
return {str(__a ): device for device in jax.devices()}
def _lowercase (self : str , __a : Tuple ):
import jax
import jax.numpy as jnp
if isinstance(__a , __a ) and column:
if all(
isinstance(__a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(__a , axis=0 )
return column
def _lowercase (self : Any , __a : Optional[int] ):
import jax
import jax.numpy as jnp
if isinstance(__a , (str, bytes, type(__a )) ):
return value
elif isinstance(__a , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
UpperCAmelCase_ = {}
if isinstance(__a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
UpperCAmelCase_ = {"dtype": jnp.intaa}
else:
UpperCAmelCase_ = {"dtype": jnp.intaa}
elif isinstance(__a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
UpperCAmelCase_ = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(__a , PIL.Image.Image ):
UpperCAmelCase_ = np.asarray(__a )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
UpperCAmelCase_ = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(__a , **{**default_dtype, **self.jnp_array_kwargs} )
def _lowercase (self : int , __a : Any ):
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(__a , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(__a , "__array__" ) and not isinstance(__a , jax.Array ):
UpperCAmelCase_ = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(__a , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(__a ) for substruct in data_struct] )
elif isinstance(__a , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(__a ) for substruct in data_struct] )
return self._tensorize(__a )
def _lowercase (self : Union[str, Any] , __a : dict ):
return map_nested(self._recursive_tensorize , __a , map_list=__a )
def _lowercase (self : str , __a : pa.Table ):
UpperCAmelCase_ = self.numpy_arrow_extractor().extract_row(__a )
UpperCAmelCase_ = self.python_features_decoder.decode_row(__a )
return self.recursive_tensorize(__a )
def _lowercase (self : Tuple , __a : pa.Table ):
UpperCAmelCase_ = self.numpy_arrow_extractor().extract_column(__a )
UpperCAmelCase_ = self.python_features_decoder.decode_column(__a , pa_table.column_names[0] )
UpperCAmelCase_ = self.recursive_tensorize(__a )
UpperCAmelCase_ = self._consolidate(__a )
return column
def _lowercase (self : str , __a : pa.Table ):
UpperCAmelCase_ = self.numpy_arrow_extractor().extract_batch(__a )
UpperCAmelCase_ = self.python_features_decoder.decode_batch(__a )
UpperCAmelCase_ = self.recursive_tensorize(__a )
for column_name in batch:
UpperCAmelCase_ = self._consolidate(batch[column_name] )
return batch
| 106 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class lowercase( __a ):
'''simple docstring'''
lowercase__ = "biogpt"
def __init__( self: Dict, a_: List[Any]=42_384, a_: int=1_024, a_: Optional[int]=24, a_: List[str]=16, a_: Optional[Any]=4_096, a_: int="gelu", a_: int=0.1, a_: List[Any]=0.1, a_: Any=1_024, a_: Optional[Any]=0.02, a_: Dict=1E-12, a_: Tuple=True, a_: Any=True, a_: Tuple=0.0, a_: str=0.0, a_: int=1, a_: Any=0, a_: List[str]=2, **a_: Optional[int], ):
'''simple docstring'''
_snake_case : Dict = vocab_size
_snake_case : int = max_position_embeddings
_snake_case : Optional[int] = hidden_size
_snake_case : Any = num_hidden_layers
_snake_case : List[str] = num_attention_heads
_snake_case : Optional[int] = intermediate_size
_snake_case : Dict = hidden_act
_snake_case : Tuple = hidden_dropout_prob
_snake_case : Tuple = attention_probs_dropout_prob
_snake_case : Optional[Any] = initializer_range
_snake_case : List[str] = layer_norm_eps
_snake_case : List[str] = scale_embedding
_snake_case : Dict = use_cache
_snake_case : Optional[int] = layerdrop
_snake_case : Any = activation_dropout
super().__init__(pad_token_id=a_, bos_token_id=a_, eos_token_id=a_, **a_ )
| 64 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
a_ :str = logging.get_logger(__name__)
def lowercase_ (A : str ):
snake_case__ : Tuple = SwinConfig.from_pretrained(
'microsoft/swin-tiny-patch4-window7-224' , out_features=['stage1', 'stage2', 'stage3', 'stage4'] )
snake_case__ : List[Any] = MaskFormerConfig(backbone_config=A )
snake_case__ : Union[str, Any] = 'huggingface/label-files'
if "ade20k-full" in model_name:
# this should be ok
snake_case__ : Dict = 8_4_7
snake_case__ : List[str] = 'maskformer-ade20k-full-id2label.json'
elif "ade" in model_name:
# this should be ok
snake_case__ : Union[str, Any] = 1_5_0
snake_case__ : Any = 'ade20k-id2label.json'
elif "coco-stuff" in model_name:
# this should be ok
snake_case__ : List[str] = 1_7_1
snake_case__ : Union[str, Any] = 'maskformer-coco-stuff-id2label.json'
elif "coco" in model_name:
# TODO
snake_case__ : Dict = 1_3_3
snake_case__ : str = 'coco-panoptic-id2label.json'
elif "cityscapes" in model_name:
# this should be ok
snake_case__ : List[str] = 1_9
snake_case__ : Union[str, Any] = 'cityscapes-id2label.json'
elif "vistas" in model_name:
# this should be ok
snake_case__ : Tuple = 6_5
snake_case__ : List[str] = 'mapillary-vistas-id2label.json'
snake_case__ : Dict = json.load(open(hf_hub_download(A , A , repo_type='dataset' ) , 'r' ) )
snake_case__ : List[str] = {int(A ): v for k, v in idalabel.items()}
return config
def lowercase_ (A : Any ):
snake_case__ : Optional[int] = []
# stem
# fmt: off
rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight') )
rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias') )
rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight') )
rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''') )
if i < 3:
rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''') )
rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''') )
rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''') )
rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''') )
rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''') )
# FPN
rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight') )
rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight') )
rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias') )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''') )
rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''') )
rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''') )
rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''') )
rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''') )
rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''') )
rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight') )
rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias') )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''') )
# cross-attention out projection
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''') )
# MLP 1
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''') )
# MLP 2
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''') )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''') )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''') )
# layernorm 3 (final layernorm)
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''') )
rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight') )
rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias') )
# heads on top
rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight') )
rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight') )
rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias') )
rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight') )
rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias') )
for i in range(3 ):
rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''') )
# fmt: on
return rename_keys
def lowercase_ (A : Tuple , A : Tuple , A : Optional[Any] ):
snake_case__ : Optional[int] = dct.pop(A )
snake_case__ : Union[str, Any] = val
def lowercase_ (A : Optional[Any] , A : Tuple ):
snake_case__ : Optional[int] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
snake_case__ : Optional[int] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
snake_case__ : int = state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight''' )
snake_case__ : Tuple = state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case__ : str = in_proj_weight[:dim, :]
snake_case__ : int = in_proj_bias[: dim]
snake_case__ : List[Any] = in_proj_weight[
dim : dim * 2, :
]
snake_case__ : List[str] = in_proj_bias[
dim : dim * 2
]
snake_case__ : List[Any] = in_proj_weight[
-dim :, :
]
snake_case__ : Dict = in_proj_bias[-dim :]
# fmt: on
def lowercase_ (A : List[str] , A : List[Any] ):
# fmt: off
snake_case__ : str = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
snake_case__ : List[Any] = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight''' )
snake_case__ : int = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case__ : Any = in_proj_weight[: hidden_size, :]
snake_case__ : Tuple = in_proj_bias[:config.hidden_size]
snake_case__ : List[str] = in_proj_weight[hidden_size : hidden_size * 2, :]
snake_case__ : Dict = in_proj_bias[hidden_size : hidden_size * 2]
snake_case__ : Any = in_proj_weight[-hidden_size :, :]
snake_case__ : int = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
snake_case__ : List[Any] = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight''' )
snake_case__ : List[str] = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case__ : Optional[int] = in_proj_weight[: hidden_size, :]
snake_case__ : Optional[Any] = in_proj_bias[:config.hidden_size]
snake_case__ : int = in_proj_weight[hidden_size : hidden_size * 2, :]
snake_case__ : List[str] = in_proj_bias[hidden_size : hidden_size * 2]
snake_case__ : List[str] = in_proj_weight[-hidden_size :, :]
snake_case__ : str = in_proj_bias[-hidden_size :]
# fmt: on
def lowercase_ ():
snake_case__ : Any = 'http://images.cocodataset.org/val2017/000000039769.jpg'
snake_case__ : int = Image.open(requests.get(A , stream=A ).raw )
return im
@torch.no_grad()
def lowercase_ (A : str , A : str , A : str , A : bool = False ):
snake_case__ : Optional[int] = get_maskformer_config(A )
# load original state_dict
with open(A , 'rb' ) as f:
snake_case__ : List[Any] = pickle.load(A )
snake_case__ : Optional[int] = data['model']
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
snake_case__ : List[str] = create_rename_keys(A )
for src, dest in rename_keys:
rename_key(A , A , A )
read_in_swin_q_k_v(A , config.backbone_config )
read_in_decoder_q_k_v(A , A )
# update to torch tensors
for key, value in state_dict.items():
snake_case__ : int = torch.from_numpy(A )
# load 🤗 model
snake_case__ : str = MaskFormerForInstanceSegmentation(A )
model.eval()
for name, param in model.named_parameters():
print(A , param.shape )
snake_case__ , snake_case__ : Union[str, Any] = model.load_state_dict(A , strict=A )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(A ) == 0, F'''Unexpected keys: {unexpected_keys}'''
# verify results
snake_case__ : Optional[Any] = prepare_img()
if "vistas" in model_name:
snake_case__ : int = 6_5
elif "cityscapes" in model_name:
snake_case__ : Dict = 6_5_5_3_5
else:
snake_case__ : Tuple = 2_5_5
snake_case__ : Optional[int] = True if 'ade' in model_name else False
snake_case__ : Dict = MaskFormerImageProcessor(ignore_index=A , reduce_labels=A )
snake_case__ : Any = image_processor(A , return_tensors='pt' )
snake_case__ : Any = model(**A )
print('Logits:' , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
snake_case__ : Tuple = torch.tensor(
[[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , A , atol=1e-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F'''Saving model and image processor to {pytorch_dump_folder_path}''' )
Path(A ).mkdir(exist_ok=A )
model.save_pretrained(A )
image_processor.save_pretrained(A )
if push_to_hub:
print('Pushing model and image processor to the hub...' )
model.push_to_hub(F'''nielsr/{model_name}''' )
image_processor.push_to_hub(F'''nielsr/{model_name}''' )
if __name__ == "__main__":
a_ :Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="maskformer-swin-tiny-ade",
type=str,
help=("Name of the MaskFormer model you'd like to convert",),
)
parser.add_argument(
"--checkpoint_path",
default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
type=str,
help="Path to the original state dict (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
a_ :Dict = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 277 | 0 |
"""simple docstring"""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
__A = (
'''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)
__A = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowercase_ ( ) -> Tuple:
'''simple docstring'''
__lowerCamelCase : List[str] = '''https://pypi.org/pypi/diffusers/json'''
__lowerCamelCase : Any = json.loads(request.urlopen(A_ ).read() )['''releases'''].keys()
return sorted(A_ , key=lambda _lowerCamelCase : version.Version(A_ ) )
def lowercase_ ( ) -> Union[str, Any]:
'''simple docstring'''
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(A_ )
os.makedirs(A_ , exist_ok=A_ )
__lowerCamelCase : List[Any] = Path(A_ ) / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def lowercase_ ( _lowerCamelCase: List[Any] ) -> Tuple:
'''simple docstring'''
init_hf_modules()
__lowerCamelCase : List[str] = Path(A_ ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(A_ , exist_ok=A_ )
__lowerCamelCase : Union[str, Any] = dynamic_module_path / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def lowercase_ ( _lowerCamelCase: Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
with open(A_ , "r" , encoding="utf-8" ) as f:
__lowerCamelCase : List[str] = f.read()
# Imports of the form `import .xxx`
__lowerCamelCase : Any = re.findall("^\s*import\s+\.(\S+)\s*$" , A_ , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import" , A_ , flags=re.MULTILINE )
# Unique-ify
return list(set(A_ ) )
def lowercase_ ( _lowerCamelCase: List[Any] ) -> str:
'''simple docstring'''
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : Optional[Any] = [module_file]
__lowerCamelCase : int = []
# Let's recurse through all relative imports
while not no_change:
__lowerCamelCase : Optional[Any] = []
for f in files_to_check:
new_imports.extend(get_relative_imports(A_ ) )
__lowerCamelCase : Optional[int] = Path(A_ ).parent
__lowerCamelCase : List[Any] = [str(module_path / m ) for m in new_imports]
__lowerCamelCase : Tuple = [f for f in new_import_files if f not in all_relative_imports]
__lowerCamelCase : Dict = [F"""{f}.py""" for f in new_import_files]
__lowerCamelCase : Union[str, Any] = len(A_ ) == 0
all_relative_imports.extend(A_ )
return all_relative_imports
def lowercase_ ( _lowerCamelCase: int ) -> Optional[int]:
'''simple docstring'''
with open(A_ , "r" , encoding="utf-8" ) as f:
__lowerCamelCase : Optional[Any] = f.read()
# Imports of the form `import xxx`
__lowerCamelCase : Tuple = re.findall("^\s*import\s+(\S+)\s*$" , A_ , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall("^\s*from\s+(\S+)\s+import" , A_ , flags=re.MULTILINE )
# Only keep the top-level module
__lowerCamelCase : str = [imp.split("." )[0] for imp in imports if not imp.startswith("." )]
# Unique-ify and test we got them all
__lowerCamelCase : Union[str, Any] = list(set(A_ ) )
__lowerCamelCase : List[Any] = []
for imp in imports:
try:
importlib.import_module(A_ )
except ImportError:
missing_packages.append(A_ )
if len(A_ ) > 0:
raise ImportError(
"This modeling file requires the following packages that were not found in your environment: "
F"""{", ".join(A_ )}. Run `pip install {" ".join(A_ )}`""" )
return get_relative_imports(A_ )
def lowercase_ ( _lowerCamelCase: Any , _lowerCamelCase: Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase : List[str] = module_path.replace(os.path.sep , "." )
__lowerCamelCase : Any = importlib.import_module(A_ )
if class_name is None:
return find_pipeline_class(A_ )
return getattr(A_ , A_ )
def lowercase_ ( _lowerCamelCase: List[Any] ) -> Dict:
'''simple docstring'''
from ..pipelines import DiffusionPipeline
__lowerCamelCase : List[Any] = dict(inspect.getmembers(A_ , inspect.isclass ) )
__lowerCamelCase : Union[str, Any] = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , A_ )
and cls.__module__.split("." )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
F"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"""
F""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"""
F""" {loaded_module}.""" )
__lowerCamelCase : Tuple = cls
return pipeline_class
def lowercase_ ( _lowerCamelCase: Tuple , _lowerCamelCase: Tuple , _lowerCamelCase: Tuple = None , _lowerCamelCase: int = False , _lowerCamelCase: List[str] = False , _lowerCamelCase: List[str] = None , _lowerCamelCase: List[str] = None , _lowerCamelCase: Any = None , _lowerCamelCase: List[Any] = False , ) -> Dict:
'''simple docstring'''
__lowerCamelCase : Tuple = str(A_ )
__lowerCamelCase : str = os.path.join(A_ , A_ )
if os.path.isfile(A_ ):
__lowerCamelCase : Optional[Any] = module_file_or_url
__lowerCamelCase : Dict = '''local'''
elif pretrained_model_name_or_path.count("/" ) == 0:
__lowerCamelCase : Union[str, Any] = get_diffusers_versions()
# cut ".dev0"
__lowerCamelCase : Any = '''v''' + '''.'''.join(__version__.split("." )[:3] )
# retrieve github version that matches
if revision is None:
__lowerCamelCase : str = latest_version if latest_version[1:] in available_versions else '''main'''
logger.info(F"""Defaulting to latest_version: {revision}.""" )
elif revision in available_versions:
__lowerCamelCase : Optional[Any] = F"""v{revision}"""
elif revision == "main":
__lowerCamelCase : Tuple = revision
else:
raise ValueError(
F"""`custom_revision`: {revision} does not exist. Please make sure to choose one of"""
F""" {", ".join(available_versions + ["main"] )}.""" )
# community pipeline on GitHub
__lowerCamelCase : Tuple = COMMUNITY_PIPELINES_URL.format(revision=A_ , pipeline=A_ )
try:
__lowerCamelCase : str = cached_download(
A_ , cache_dir=A_ , force_download=A_ , proxies=A_ , resume_download=A_ , local_files_only=A_ , use_auth_token=A_ , )
__lowerCamelCase : Dict = '''git'''
__lowerCamelCase : Union[str, Any] = pretrained_model_name_or_path + '''.py'''
except EnvironmentError:
logger.error(F"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
raise
else:
try:
# Load from URL or cache if already cached
__lowerCamelCase : Dict = hf_hub_download(
A_ , A_ , cache_dir=A_ , force_download=A_ , proxies=A_ , resume_download=A_ , local_files_only=A_ , use_auth_token=A_ , )
__lowerCamelCase : str = os.path.join("local" , "--".join(pretrained_model_name_or_path.split("/" ) ) )
except EnvironmentError:
logger.error(F"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
raise
# Check we have all the requirements in our environment
__lowerCamelCase : Optional[int] = check_imports(A_ )
# Now we move the module inside our cached dynamic modules.
__lowerCamelCase : int = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(A_ )
__lowerCamelCase : Tuple = Path(A_ ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(A_ , submodule_path / module_file )
for module_needed in modules_needed:
__lowerCamelCase : str = F"""{module_needed}.py"""
shutil.copy(os.path.join(A_ , A_ ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(A_ , A_ ):
__lowerCamelCase : List[str] = use_auth_token
elif use_auth_token is True:
__lowerCamelCase : Optional[int] = HfFolder.get_token()
else:
__lowerCamelCase : List[Any] = None
__lowerCamelCase : List[Any] = model_info(A_ , revision=A_ , token=A_ ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
__lowerCamelCase : List[str] = submodule_path / commit_hash
__lowerCamelCase : Optional[Any] = full_submodule + os.path.sep + commit_hash
create_dynamic_module(A_ )
if not (submodule_path / module_file).exists():
shutil.copy(A_ , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
A_ , F"""{module_needed}.py""" , cache_dir=A_ , force_download=A_ , resume_download=A_ , proxies=A_ , use_auth_token=A_ , revision=A_ , local_files_only=A_ , )
return os.path.join(A_ , A_ )
def lowercase_ ( _lowerCamelCase: Tuple , _lowerCamelCase: Optional[Any] , _lowerCamelCase: Optional[Any] = None , _lowerCamelCase: int = None , _lowerCamelCase: str = False , _lowerCamelCase: Union[str, Any] = False , _lowerCamelCase: List[str] = None , _lowerCamelCase: Optional[int] = None , _lowerCamelCase: Optional[int] = None , _lowerCamelCase: int = False , **_lowerCamelCase: str , ) -> int:
'''simple docstring'''
__lowerCamelCase : Tuple = get_cached_module_file(
A_ , A_ , cache_dir=A_ , force_download=A_ , resume_download=A_ , proxies=A_ , use_auth_token=A_ , revision=A_ , local_files_only=A_ , )
return get_class_in_module(A_ , final_module.replace(".py" , "" ) ) | 357 | """simple docstring"""
from typing import Any
import numpy as np
def lowercase_ ( _lowerCamelCase: np.ndarray ) -> bool:
'''simple docstring'''
return np.array_equal(_lowerCamelCase , matrix.conjugate().T )
def lowercase_ ( _lowerCamelCase: np.ndarray , _lowerCamelCase: np.ndarray ) -> Any:
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = v.conjugate().T
__lowerCamelCase : Any = v_star.dot(_lowerCamelCase )
assert isinstance(_lowerCamelCase , np.ndarray )
return (v_star_dot.dot(_lowerCamelCase )) / (v_star.dot(_lowerCamelCase ))
def lowercase_ ( ) -> None:
'''simple docstring'''
__lowerCamelCase : List[str] = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]] )
__lowerCamelCase : int = np.array([[1], [2], [3]] )
assert is_hermitian(_lowerCamelCase ), F"""{a} is not hermitian."""
print(rayleigh_quotient(_lowerCamelCase , _lowerCamelCase ) )
__lowerCamelCase : Dict = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]] )
assert is_hermitian(_lowerCamelCase ), F"""{a} is not hermitian."""
assert rayleigh_quotient(_lowerCamelCase , _lowerCamelCase ) == float(3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
tests() | 64 | 0 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowercase ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
__snake_case : Optional[Any] = AlbertConfig.from_json_file(_snake_case )
print(f"""Building PyTorch model from configuration: {config}""" )
__snake_case : Tuple = AlbertForPreTraining(_snake_case )
# Load weights from tf checkpoint
load_tf_weights_in_albert(_snake_case , _snake_case , _snake_case )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , _snake_case )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--albert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained ALBERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 102 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__lowerCamelCase = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__lowerCamelCase = {
"""vocab_file""": {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
},
"""tokenizer_file""": {
"""unc-nlp/lxmert-base-uncased""": (
"""https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
),
},
}
__lowerCamelCase = {
"""unc-nlp/lxmert-base-uncased""": 5_12,
}
__lowerCamelCase = {
"""unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}
class UpperCAmelCase ( A_ ):
A__ : Any = VOCAB_FILES_NAMES
A__ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
A__ : Tuple = PRETRAINED_INIT_CONFIGURATION
A__ : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : List[Any] = LxmertTokenizer
def __init__(self : Dict , snake_case__ : Tuple=None , snake_case__ : Optional[Any]=None , snake_case__ : Optional[Any]=True , snake_case__ : Tuple="[UNK]" , snake_case__ : Optional[Any]="[SEP]" , snake_case__ : Optional[Any]="[PAD]" , snake_case__ : List[Any]="[CLS]" , snake_case__ : Tuple="[MASK]" , snake_case__ : Dict=True , snake_case__ : Union[str, Any]=None , **snake_case__ : Dict , ) -> Optional[int]:
'''simple docstring'''
super().__init__(
snake_case__ , tokenizer_file=snake_case__ , do_lower_case=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , tokenize_chinese_chars=snake_case__ , strip_accents=snake_case__ , **snake_case__ , )
snake_case : int = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , snake_case__ ) != do_lower_case
or normalizer_state.get("strip_accents" , snake_case__ ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , snake_case__ ) != tokenize_chinese_chars
):
snake_case : Union[str, Any] = getattr(snake_case__ , normalizer_state.pop("type" ) )
snake_case : str = do_lower_case
snake_case : List[Any] = strip_accents
snake_case : Optional[int] = tokenize_chinese_chars
snake_case : int = normalizer_class(**snake_case__ )
snake_case : Optional[Any] = do_lower_case
def _SCREAMING_SNAKE_CASE (self : Union[str, Any] , snake_case__ : Optional[Any] , snake_case__ : Dict=None ) -> Any:
'''simple docstring'''
snake_case : Optional[int] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _SCREAMING_SNAKE_CASE (self : Optional[int] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
snake_case : Optional[Any] = [self.sep_token_id]
snake_case : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _SCREAMING_SNAKE_CASE (self : Any , snake_case__ : str , snake_case__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
snake_case : List[Any] = self._tokenizer.model.save(snake_case__ , name=snake_case__ )
return tuple(snake_case__ )
| 59 | 0 |
'''simple docstring'''
import os
from typing import Dict, List, Tuple, TypeVar, Union
a_ : Tuple = TypeVar("""T""")
a_ : Dict = Union[List[T], Tuple[T, ...]]
a_ : int = Union[T, List[T], Dict[str, T]]
a_ : Optional[Any] = Union[str, bytes, os.PathLike]
| 6 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Union[str, Any] =ShapEImgaImgPipeline
lowercase : Dict =['image']
lowercase : str =['image']
lowercase : int =[
'num_images_per_prompt',
'num_inference_steps',
'generator',
'latents',
'guidance_scale',
'frame_size',
'output_type',
'return_dict',
]
lowercase : int =False
@property
def lowercase__ ( self ):
"""simple docstring"""
return 32
@property
def lowercase__ ( self ):
"""simple docstring"""
return 32
@property
def lowercase__ ( self ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowercase__ ( self ):
"""simple docstring"""
return 8
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size, image_size=64, projection_dim=self.text_embedder_hidden_size, intermediate_size=37, num_attention_heads=4, num_channels=3, num_hidden_layers=5, patch_size=1, )
lowerCamelCase_ =CLIPVisionModel(lowerCAmelCase )
return model
@property
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =CLIPImageProcessor(
crop_size=224, do_center_crop=lowerCAmelCase, do_normalize=lowerCAmelCase, do_resize=lowerCAmelCase, image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3], image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1], resample=3, size=224, )
return image_processor
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ ={
'''num_attention_heads''': 2,
'''attention_head_dim''': 16,
'''embedding_dim''': self.time_input_dim,
'''num_embeddings''': 32,
'''embedding_proj_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''num_layers''': 1,
'''clip_embed_dim''': self.time_input_dim * 2,
'''additional_embeddings''': 0,
'''time_embed_act_fn''': '''gelu''',
'''norm_in_type''': '''layer''',
'''embedding_proj_norm_type''': '''layer''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
lowerCamelCase_ =PriorTransformer(**lowerCAmelCase )
return model
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ ={
'''param_shapes''': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'''d_latent''': self.time_input_dim,
'''d_hidden''': self.renderer_dim,
'''n_output''': 12,
'''background''': (
0.1,
0.1,
0.1,
),
}
lowerCamelCase_ =ShapERenderer(**lowerCAmelCase )
return model
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.dummy_prior
lowerCamelCase_ =self.dummy_image_encoder
lowerCamelCase_ =self.dummy_image_processor
lowerCamelCase_ =self.dummy_renderer
lowerCamelCase_ =HeunDiscreteScheduler(
beta_schedule='''exp''', num_train_timesteps=1_024, prediction_type='''sample''', use_karras_sigmas=lowerCAmelCase, clip_sample=lowerCAmelCase, clip_sample_range=1.0, )
lowerCamelCase_ ={
'''prior''': prior,
'''image_encoder''': image_encoder,
'''image_processor''': image_processor,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor((1, 3, 64, 64), rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
if str(lowerCAmelCase ).startswith('''mps''' ):
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
else:
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
lowerCamelCase_ ={
'''image''': input_image,
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu'''
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =pipe(**self.get_dummy_inputs(lowerCAmelCase ) )
lowerCamelCase_ =output.images[0]
lowerCamelCase_ =image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
lowerCamelCase_ =np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase__ ( self ):
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =torch_device == '''cpu'''
lowerCamelCase_ =True
self._test_inference_batch_single_identical(
batch_size=2, test_max_difference=lowerCAmelCase, relax_max_difference=lowerCAmelCase, )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =1
lowerCamelCase_ =2
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
for key in inputs.keys():
if key in self.batch_params:
lowerCamelCase_ =batch_size * [inputs[key]]
lowerCamelCase_ =pipe(**lowerCAmelCase, num_images_per_prompt=lowerCAmelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
lowerCamelCase_ =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_img2img_out.npy''' )
lowerCamelCase_ =ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(0 )
lowerCamelCase_ =pipe(
lowerCAmelCase, generator=lowerCAmelCase, guidance_scale=3.0, num_inference_steps=64, frame_size=64, output_type='''np''', ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(lowerCAmelCase, lowerCAmelCase )
| 6 | 1 |
def A_ ( _lowerCAmelCase = 1000 ) -> int:
UpperCamelCase , UpperCamelCase : Any = 1, 1
UpperCamelCase : Dict = []
for i in range(1 , n + 1 ):
UpperCamelCase : Union[str, Any] = prev_numerator + 2 * prev_denominator
UpperCamelCase : List[Any] = prev_numerator + prev_denominator
if len(str(_lowerCAmelCase ) ) > len(str(_lowerCAmelCase ) ):
result.append(_lowerCAmelCase )
UpperCamelCase : Dict = numerator
UpperCamelCase : Dict = denominator
return len(_lowerCAmelCase )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 52 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
lowerCamelCase = collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCamelCase = '''https://storage.googleapis.com/cvdf-datasets/mnist/'''
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Dict = numpy.dtype(numpy.uintaa ).newbyteorder('''>''' )
return numpy.frombuffer(bytestream.read(4 ) , dtype=_a )[0]
@deprecated(_a , '''Please use tf.data to implement this functionality.''' )
def lowerCamelCase_ ( _a ):
"""simple docstring"""
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=_a ) as bytestream:
lowerCAmelCase__ : Any = _readaa(_a )
if magic != 2_051:
raise ValueError(
'''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name) )
lowerCAmelCase__ : Any = _readaa(_a )
lowerCAmelCase__ : Tuple = _readaa(_a )
lowerCAmelCase__ : List[Any] = _readaa(_a )
lowerCAmelCase__ : Union[str, Any] = bytestream.read(rows * cols * num_images )
lowerCAmelCase__ : List[Any] = numpy.frombuffer(_a , dtype=numpy.uinta )
lowerCAmelCase__ : int = data.reshape(_a , _a , _a , 1 )
return data
@deprecated(_a , '''Please use tf.one_hot on tensors.''' )
def lowerCamelCase_ ( _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : List[Any] = labels_dense.shape[0]
lowerCAmelCase__ : Optional[Any] = numpy.arange(_a ) * num_classes
lowerCAmelCase__ : str = numpy.zeros((num_labels, num_classes) )
lowerCAmelCase__ : Optional[Any] = 1
return labels_one_hot
@deprecated(_a , '''Please use tf.data to implement this functionality.''' )
def lowerCamelCase_ ( _a , _a=False , _a=10 ):
"""simple docstring"""
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=_a ) as bytestream:
lowerCAmelCase__ : Optional[int] = _readaa(_a )
if magic != 2_049:
raise ValueError(
'''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name) )
lowerCAmelCase__ : Union[str, Any] = _readaa(_a )
lowerCAmelCase__ : Tuple = bytestream.read(_a )
lowerCAmelCase__ : Dict = numpy.frombuffer(_a , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(_a , _a )
return labels
class _a :
@deprecated(
_SCREAMING_SNAKE_CASE , '''Please use alternatives such as official/mnist/_DataSet.py'''
''' from tensorflow/models.''' , )
def __init__( self : Dict , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Tuple=False , _SCREAMING_SNAKE_CASE : Any=False , _SCREAMING_SNAKE_CASE : Optional[Any]=dtypes.floataa , _SCREAMING_SNAKE_CASE : List[str]=True , _SCREAMING_SNAKE_CASE : List[str]=None , )-> List[Any]:
lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = random_seed.get_seed(_SCREAMING_SNAKE_CASE )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
lowerCAmelCase__ : Optional[int] = dtypes.as_dtype(_SCREAMING_SNAKE_CASE ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype )
if fake_data:
lowerCAmelCase__ : int = 1_0000
lowerCAmelCase__ : List[Any] = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), F'images.shape: {images.shape} labels.shape: {labels.shape}'
lowerCAmelCase__ : List[Any] = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
lowerCAmelCase__ : Tuple = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
lowerCAmelCase__ : Any = images.astype(numpy.floataa )
lowerCAmelCase__ : Any = numpy.multiply(_SCREAMING_SNAKE_CASE , 1.0 / 255.0 )
lowerCAmelCase__ : Tuple = images
lowerCAmelCase__ : Tuple = labels
lowerCAmelCase__ : List[Any] = 0
lowerCAmelCase__ : Tuple = 0
@property
def UpperCAmelCase__( self : Tuple )-> Dict:
return self._images
@property
def UpperCAmelCase__( self : Tuple )-> Optional[int]:
return self._labels
@property
def UpperCAmelCase__( self : Tuple )-> Dict:
return self._num_examples
@property
def UpperCAmelCase__( self : Tuple )-> Any:
return self._epochs_completed
def UpperCAmelCase__( self : List[Any] , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Dict=False , _SCREAMING_SNAKE_CASE : Optional[int]=True )-> List[str]:
if fake_data:
lowerCAmelCase__ : Dict = [1] * 784
lowerCAmelCase__ : Union[str, Any] = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(_SCREAMING_SNAKE_CASE )],
[fake_label for _ in range(_SCREAMING_SNAKE_CASE )],
)
lowerCAmelCase__ : str = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
lowerCAmelCase__ : Union[str, Any] = numpy.arange(self._num_examples )
numpy.random.shuffle(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : List[Any] = self.images[perma]
lowerCAmelCase__ : Tuple = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
lowerCAmelCase__ : Any = self._num_examples - start
lowerCAmelCase__ : List[str] = self._images[start : self._num_examples]
lowerCAmelCase__ : Tuple = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
lowerCAmelCase__ : Union[str, Any] = numpy.arange(self._num_examples )
numpy.random.shuffle(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : str = self.images[perm]
lowerCAmelCase__ : List[Any] = self.labels[perm]
# Start next epoch
lowerCAmelCase__ : Dict = 0
lowerCAmelCase__ : Union[str, Any] = batch_size - rest_num_examples
lowerCAmelCase__ : Any = self._index_in_epoch
lowerCAmelCase__ : Optional[Any] = self._images[start:end]
lowerCAmelCase__ : Optional[Any] = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
lowerCAmelCase__ : Dict = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(_a , '''Please write your own downloading logic.''' )
def lowerCamelCase_ ( _a , _a , _a ):
"""simple docstring"""
if not gfile.Exists(_a ):
gfile.MakeDirs(_a )
lowerCAmelCase__ : str = os.path.join(_a , _a )
if not gfile.Exists(_a ):
urllib.request.urlretrieve(_a , _a ) # noqa: S310
with gfile.GFile(_a ) as f:
lowerCAmelCase__ : Optional[Any] = f.size()
print('''Successfully downloaded''' , _a , _a , '''bytes.''' )
return filepath
@deprecated(
_a , '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')''' )
def lowerCamelCase_ ( _a , _a=False , _a=False , _a=dtypes.floataa , _a=True , _a=5_000 , _a=None , _a=DEFAULT_SOURCE_URL , ):
"""simple docstring"""
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=_a , one_hot=_a , dtype=_a , seed=_a )
lowerCAmelCase__ : Tuple = fake()
lowerCAmelCase__ : Union[str, Any] = fake()
lowerCAmelCase__ : Tuple = fake()
return _Datasets(train=_a , validation=_a , test=_a )
if not source_url: # empty string check
lowerCAmelCase__ : Optional[Any] = DEFAULT_SOURCE_URL
lowerCAmelCase__ : Tuple = '''train-images-idx3-ubyte.gz'''
lowerCAmelCase__ : Dict = '''train-labels-idx1-ubyte.gz'''
lowerCAmelCase__ : List[str] = '''t10k-images-idx3-ubyte.gz'''
lowerCAmelCase__ : Optional[int] = '''t10k-labels-idx1-ubyte.gz'''
lowerCAmelCase__ : Optional[Any] = _maybe_download(
_a , _a , source_url + train_images_file )
with gfile.Open(_a , '''rb''' ) as f:
lowerCAmelCase__ : Optional[Any] = _extract_images(_a )
lowerCAmelCase__ : Any = _maybe_download(
_a , _a , source_url + train_labels_file )
with gfile.Open(_a , '''rb''' ) as f:
lowerCAmelCase__ : Any = _extract_labels(_a , one_hot=_a )
lowerCAmelCase__ : Any = _maybe_download(
_a , _a , source_url + test_images_file )
with gfile.Open(_a , '''rb''' ) as f:
lowerCAmelCase__ : str = _extract_images(_a )
lowerCAmelCase__ : Dict = _maybe_download(
_a , _a , source_url + test_labels_file )
with gfile.Open(_a , '''rb''' ) as f:
lowerCAmelCase__ : int = _extract_labels(_a , one_hot=_a )
if not 0 <= validation_size <= len(_a ):
lowerCAmelCase__ : Dict = (
'''Validation size should be between 0 and '''
f'{len(_a )}. Received: {validation_size}.'
)
raise ValueError(_a )
lowerCAmelCase__ : List[str] = train_images[:validation_size]
lowerCAmelCase__ : Any = train_labels[:validation_size]
lowerCAmelCase__ : Optional[Any] = train_images[validation_size:]
lowerCAmelCase__ : Optional[int] = train_labels[validation_size:]
lowerCAmelCase__ : Optional[Any] = {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}
lowerCAmelCase__ : List[str] = _DataSet(_a , _a , **_a )
lowerCAmelCase__ : Dict = _DataSet(_a , _a , **_a )
lowerCAmelCase__ : Dict = _DataSet(_a , _a , **_a )
return _Datasets(train=_a , validation=_a , test=_a )
| 131 | 0 |
def _A ( lowerCAmelCase_ : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
lowerCAmelCase__ = set()
# Replace all the whitespace in our sentence
lowerCAmelCase__ = input_str.replace(" " , "" )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(lowerCAmelCase_ ) == 26
def _A ( lowerCAmelCase_ : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
lowerCAmelCase__ = [False] * 26
for char in input_str:
if char.islower():
lowerCAmelCase__ = True
elif char.isupper():
lowerCAmelCase__ = True
return all(lowerCAmelCase_ )
def _A ( lowerCAmelCase_ : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def _A ( ):
"""simple docstring"""
from timeit import timeit
lowerCAmelCase__ = "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
print(timeit("is_pangram()" , setup=lowerCAmelCase_ ) )
print(timeit("is_pangram_faster()" , setup=lowerCAmelCase_ ) )
print(timeit("is_pangram_fastest()" , setup=lowerCAmelCase_ ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 354 |
from __future__ import annotations
UpperCamelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _A ( lowerCAmelCase_ : list[list[int]] , lowerCAmelCase_ : list[int] , lowerCAmelCase_ : list[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : list[list[int]] , ):
"""simple docstring"""
lowerCAmelCase__ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(lowerCAmelCase_ ) )
] # the reference grid
lowerCAmelCase__ = 1
lowerCAmelCase__ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(lowerCAmelCase_ ) )
] # the action grid
lowerCAmelCase__ = init[0]
lowerCAmelCase__ = init[1]
lowerCAmelCase__ = 0
lowerCAmelCase__ = g + heuristic[x][y] # cost from starting cell to destination cell
lowerCAmelCase__ = [[f, g, x, y]]
lowerCAmelCase__ = False # flag that is set when search is complete
lowerCAmelCase__ = False # flag set if we can't find expand
while not found and not resign:
if len(lowerCAmelCase_ ) == 0:
raise ValueError("Algorithm is unable to find solution" )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
lowerCAmelCase__ = cell.pop()
lowerCAmelCase__ = next_cell[2]
lowerCAmelCase__ = next_cell[3]
lowerCAmelCase__ = next_cell[1]
if x == goal[0] and y == goal[1]:
lowerCAmelCase__ = True
else:
for i in range(len(lowerCAmelCase_ ) ): # to try out different valid actions
lowerCAmelCase__ = x + DIRECTIONS[i][0]
lowerCAmelCase__ = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(lowerCAmelCase_ ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
lowerCAmelCase__ = g + cost
lowerCAmelCase__ = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
lowerCAmelCase__ = 1
lowerCAmelCase__ = i
lowerCAmelCase__ = []
lowerCAmelCase__ = goal[0]
lowerCAmelCase__ = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
lowerCAmelCase__ = x - DIRECTIONS[action[x][y]][0]
lowerCAmelCase__ = y - DIRECTIONS[action[x][y]][1]
lowerCAmelCase__ = xa
lowerCAmelCase__ = ya
invpath.append([x, y] )
lowerCAmelCase__ = []
for i in range(len(lowerCAmelCase_ ) ):
path.append(invpath[len(lowerCAmelCase_ ) - 1 - i] )
return path, action
if __name__ == "__main__":
UpperCamelCase = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
UpperCamelCase = [0, 0]
# all coordinates are given in format [y,x]
UpperCamelCase = [len(grid) - 1, len(grid[0]) - 1]
UpperCamelCase = 1
# the cost map which pushes the path closer to the goal
UpperCamelCase = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
UpperCamelCase = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
UpperCamelCase = 99
UpperCamelCase , UpperCamelCase = search(grid, init, goal, cost, heuristic)
print('ACTION MAP')
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 221 | 0 |
'''simple docstring'''
def _A ( A__ ):
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
__lowercase = grid[0]
for row_n in range(1 , len(A__ ) ):
__lowercase = grid[row_n]
__lowercase = fill_row(A__ , A__ )
__lowercase = grid[row_n]
return grid[-1][-1]
def _A ( A__ , A__ ):
"""simple docstring"""
current_row[0] += row_above[0]
for cell_n in range(1 , len(A__ ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 104 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ):
UpperCAmelCase = x
UpperCAmelCase = y
for step in range(lowercase_ ): # noqa: B007
UpperCAmelCase = a * a - b * b + x
UpperCAmelCase = 2 * a * b + y
UpperCAmelCase = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def _lowerCAmelCase ( lowercase_ ):
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def _lowerCAmelCase ( lowercase_ ):
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(lowercase_ , 1 , 1 ) )
def _lowerCAmelCase ( lowercase_ = 800 , lowercase_ = 600 , lowercase_ = -0.6 , lowercase_ = 0 , lowercase_ = 3.2 , lowercase_ = 50 , lowercase_ = True , ):
UpperCAmelCase = Image.new('RGB' , (image_width, image_height) )
UpperCAmelCase = img.load()
# loop through the image-coordinates
for image_x in range(lowercase_ ):
for image_y in range(lowercase_ ):
# determine the figure-coordinates based on the image-coordinates
UpperCAmelCase = figure_width / image_width * image_height
UpperCAmelCase = figure_center_x + (image_x / image_width - 0.5) * figure_width
UpperCAmelCase = figure_center_y + (image_y / image_height - 0.5) * figure_height
UpperCAmelCase = get_distance(lowercase_ , lowercase_ , lowercase_ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
UpperCAmelCase = get_color_coded_rgb(lowercase_ )
else:
UpperCAmelCase = get_black_and_white_rgb(lowercase_ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
snake_case_ = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 78 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class a ( UpperCAmelCase ):
_lowercase = 4_2
class a ( UpperCAmelCase , UpperCAmelCase ):
@register_to_config
def __init__( self , A_ = 3 , A_ = 3 , A_ = ("DownEncoderBlock2D",) , A_ = ("UpDecoderBlock2D",) , A_ = (64,) , A_ = 1 , A_ = "silu" , A_ = 3 , A_ = 32 , A_ = 256 , A_ = 32 , A_ = None , A_ = 0.1_82_15 , A_ = "group" , ):
'''simple docstring'''
super().__init__()
# pass init params to Encoder
_UpperCAmelCase : Optional[Any] = Encoder(
in_channels=A_ , out_channels=A_ , down_block_types=A_ , block_out_channels=A_ , layers_per_block=A_ , act_fn=A_ , norm_num_groups=A_ , double_z=A_ , )
_UpperCAmelCase : Union[str, Any] = vq_embed_dim if vq_embed_dim is not None else latent_channels
_UpperCAmelCase : Optional[Any] = nn.Convad(A_ , A_ , 1 )
_UpperCAmelCase : Any = VectorQuantizer(A_ , A_ , beta=0.25 , remap=A_ , sane_index_shape=A_ )
_UpperCAmelCase : Any = nn.Convad(A_ , A_ , 1 )
# pass init params to Decoder
_UpperCAmelCase : Tuple = Decoder(
in_channels=A_ , out_channels=A_ , up_block_types=A_ , block_out_channels=A_ , layers_per_block=A_ , act_fn=A_ , norm_num_groups=A_ , norm_type=A_ , )
@apply_forward_hook
def _UpperCAmelCase ( self , A_ , A_ = True ):
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = self.encoder(A_ )
_UpperCAmelCase : List[str] = self.quant_conv(A_ )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=A_ )
@apply_forward_hook
def _UpperCAmelCase ( self , A_ , A_ = False , A_ = True ):
'''simple docstring'''
if not force_not_quantize:
_UpperCAmelCase : Optional[int] = self.quantize(A_ )
else:
_UpperCAmelCase : List[str] = h
_UpperCAmelCase : List[Any] = self.post_quant_conv(A_ )
_UpperCAmelCase : Optional[int] = self.decoder(A_ , quant if self.config.norm_type == "spatial" else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=A_ )
def _UpperCAmelCase ( self , A_ , A_ = True ):
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = sample
_UpperCAmelCase : Any = self.encode(A_ ).latents
_UpperCAmelCase : Optional[int] = self.decode(A_ ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=A_ )
| 354 |
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str , lowerCAmelCase: str ) -> bool:
_UpperCAmelCase : Optional[Any] = len(lowerCAmelCase ) + 1
_UpperCAmelCase : Optional[int] = len(lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
_UpperCAmelCase : List[str] = [[0 for i in range(lowerCAmelCase )] for j in range(lowerCAmelCase )]
# since string of zero length match pattern of zero length
_UpperCAmelCase : List[Any] = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , lowerCAmelCase ):
_UpperCAmelCase : Dict = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , lowerCAmelCase ):
_UpperCAmelCase : Tuple = dp[0][j - 2] if pattern[j - 1] == "*" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , lowerCAmelCase ):
for j in range(1 , lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
_UpperCAmelCase : Optional[Any] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
_UpperCAmelCase : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
_UpperCAmelCase : str = dp[i - 1][j]
else:
_UpperCAmelCase : int = 0
else:
_UpperCAmelCase : List[Any] = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
SCREAMING_SNAKE_CASE_ = 'aab'
SCREAMING_SNAKE_CASE_ = 'c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F'''{input_string} matches the given pattern {pattern}''')
else:
print(F'''{input_string} does not match with the given pattern {pattern}''')
| 189 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __SCREAMING_SNAKE_CASE ( lowercase_ ):
A : Union[str, Any] = 'Speech2TextFeatureExtractor'
A : Dict = 'Speech2TextTokenizer'
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
super().__init__(__UpperCamelCase , __UpperCamelCase )
lowercase : List[Any] = self.feature_extractor
lowercase : Optional[Any] = False
def __call__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
if self._in_target_context_manager:
return self.current_processor(*__UpperCamelCase , **__UpperCamelCase )
if "raw_speech" in kwargs:
warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' )
lowercase : List[str] = kwargs.pop('''raw_speech''' )
else:
lowercase : str = kwargs.pop('''audio''' , __UpperCamelCase )
lowercase : List[Any] = kwargs.pop('''sampling_rate''' , __UpperCamelCase )
lowercase : Optional[int] = kwargs.pop('''text''' , __UpperCamelCase )
if len(__UpperCamelCase ) > 0:
lowercase : int = args[0]
lowercase : Any = args[1:]
if audio is None and text is None:
raise ValueError('''You need to specify either an `audio` or `text` input to process.''' )
if audio is not None:
lowercase : Tuple = self.feature_extractor(__UpperCamelCase , *__UpperCamelCase , sampling_rate=__UpperCamelCase , **__UpperCamelCase )
if text is not None:
lowercase : Tuple = self.tokenizer(__UpperCamelCase , **__UpperCamelCase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
lowercase : Dict = encodings['input_ids']
return inputs
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
return self.tokenizer.batch_decode(*__UpperCamelCase , **__UpperCamelCase )
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
return self.tokenizer.decode(*__UpperCamelCase , **__UpperCamelCase )
@contextmanager
def __lowerCamelCase ( self ):
warnings.warn(
'''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
'''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
'''your audio inputs, or in a separate call.''' )
lowercase : Optional[Any] = True
lowercase : Any = self.tokenizer
yield
lowercase : Optional[int] = self.feature_extractor
lowercase : Any = False
| 337 | """simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class UpperCamelCase ( unittest.TestCase ):
def __init__( self ,__UpperCamelCase ,__UpperCamelCase=13 ,__UpperCamelCase=30 ,__UpperCamelCase=2 ,__UpperCamelCase=3 ,__UpperCamelCase=True ,__UpperCamelCase=True ,__UpperCamelCase=32 ,__UpperCamelCase=5 ,__UpperCamelCase=4 ,__UpperCamelCase=37 ,__UpperCamelCase="gelu" ,__UpperCamelCase=0.1 ,__UpperCamelCase=0.1 ,__UpperCamelCase=10 ,__UpperCamelCase=0.02 ,) -> Tuple:
'''simple docstring'''
lowercase_ : Tuple = parent
lowercase_ : Union[str, Any] = batch_size
lowercase_ : int = image_size
lowercase_ : Tuple = patch_size
lowercase_ : Optional[int] = num_channels
lowercase_ : Union[str, Any] = is_training
lowercase_ : Dict = use_labels
lowercase_ : Optional[int] = hidden_size
lowercase_ : List[str] = num_hidden_layers
lowercase_ : Optional[Any] = num_attention_heads
lowercase_ : Optional[int] = intermediate_size
lowercase_ : Tuple = hidden_act
lowercase_ : int = hidden_dropout_prob
lowercase_ : str = attention_probs_dropout_prob
lowercase_ : str = type_sequence_label_size
lowercase_ : Optional[int] = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ : str = (image_size // patch_size) ** 2
lowercase_ : Optional[int] = num_patches + 1
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
lowercase_ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase_ : List[Any] = 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 ,)
return config, pixel_values
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ) -> Dict:
'''simple docstring'''
lowercase_ : List[Any] = FlaxViTModel(config=__UpperCamelCase )
lowercase_ : Dict = model(__UpperCamelCase )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
lowercase_ : Union[str, Any] = (self.image_size, self.image_size)
lowercase_ : List[Any] = (self.patch_size, self.patch_size)
lowercase_ : List[str] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, num_patches + 1, self.hidden_size) )
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ : List[Any] = self.type_sequence_label_size
lowercase_ : str = FlaxViTForImageClassification(config=__UpperCamelCase )
lowercase_ : Optional[Any] = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowercase_ : Union[str, Any] = 1
lowercase_ : Optional[int] = FlaxViTForImageClassification(__UpperCamelCase )
lowercase_ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase_ : str = model(__UpperCamelCase )
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
lowercase_ : Optional[int] = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) ,
) : List[Any] = config_and_inputs
lowercase_ : Tuple = {'pixel_values': pixel_values}
return config, inputs_dict
@require_flax
class UpperCamelCase ( lowercase_ , unittest.TestCase ):
lowercase = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def _UpperCAmelCase ( self ) -> None:
'''simple docstring'''
lowercase_ : Optional[Any] = FlaxViTModelTester(self )
lowercase_ : Union[str, Any] = ConfigTester(self ,config_class=__UpperCamelCase ,has_text_modality=__UpperCamelCase ,hidden_size=37 )
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
def _UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
lowercase_ , lowercase_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : Optional[Any] = model_class(__UpperCamelCase )
lowercase_ : Tuple = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ : Union[str, Any] = [*signature.parameters.keys()]
lowercase_ : str = ['pixel_values']
self.assertListEqual(arg_names[:1] ,__UpperCamelCase )
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ , lowercase_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowercase_ : Optional[Any] = self._prepare_for_class(__UpperCamelCase ,__UpperCamelCase )
lowercase_ : Dict = model_class(__UpperCamelCase )
@jax.jit
def model_jitted(__UpperCamelCase ,**__UpperCamelCase ):
return model(pixel_values=__UpperCamelCase ,**__UpperCamelCase )
with self.subTest('JIT Enabled' ):
lowercase_ : Optional[int] = model_jitted(**__UpperCamelCase ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
lowercase_ : List[str] = model_jitted(**__UpperCamelCase ).to_tuple()
self.assertEqual(len(__UpperCamelCase ) ,len(__UpperCamelCase ) )
for jitted_output, output in zip(__UpperCamelCase ,__UpperCamelCase ):
self.assertEqual(jitted_output.shape ,output.shape )
@slow
def _UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
lowercase_ : Optional[int] = model_class_name.from_pretrained('google/vit-base-patch16-224' )
lowercase_ : int = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(__UpperCamelCase )
| 213 | 0 |
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ = 1 , UpperCamelCase__ = 1_0_0_0 ) -> Optional[Any]:
UpperCAmelCase__ : List[Any] = 1
UpperCAmelCase__ : Optional[Any] = 0
for divide_by_number in range(UpperCamelCase__ , digit + 1 ):
UpperCAmelCase__ : list[int] = []
UpperCAmelCase__ : Union[str, Any] = numerator
for _ in range(1 , digit + 1 ):
if now_divide in has_been_divided:
if longest_list_length < len(UpperCamelCase__ ):
UpperCAmelCase__ : int = len(UpperCamelCase__ )
UpperCAmelCase__ : List[str] = divide_by_number
else:
has_been_divided.append(UpperCamelCase__ )
UpperCAmelCase__ : Optional[int] = now_divide * 1_0 % divide_by_number
return the_digit
# Tests
if __name__ == "__main__":
import doctest
doctest.testmod() | 357 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _snake_case ( a__ ):
lowerCAmelCase :Optional[int] = ['''image_processor''', '''tokenizer''']
lowerCAmelCase :Optional[int] = '''BridgeTowerImageProcessor'''
lowerCAmelCase :List[str] = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
def __init__( self , _lowerCamelCase , _lowerCamelCase):
super().__init__(_lowerCamelCase , _lowerCamelCase)
def __call__( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = True , _lowerCamelCase = False , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = 0 , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = False , _lowerCamelCase = False , _lowerCamelCase = False , _lowerCamelCase = False , _lowerCamelCase = True , _lowerCamelCase = None , **_lowerCamelCase , ):
UpperCAmelCase__ : List[str] = self.tokenizer(
text=_lowerCamelCase , add_special_tokens=_lowerCamelCase , padding=_lowerCamelCase , truncation=_lowerCamelCase , max_length=_lowerCamelCase , stride=_lowerCamelCase , pad_to_multiple_of=_lowerCamelCase , return_token_type_ids=_lowerCamelCase , return_attention_mask=_lowerCamelCase , return_overflowing_tokens=_lowerCamelCase , return_special_tokens_mask=_lowerCamelCase , return_offsets_mapping=_lowerCamelCase , return_length=_lowerCamelCase , verbose=_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase , )
# add pixel_values + pixel_mask
UpperCAmelCase__ : Optional[Any] = self.image_processor(
_lowerCamelCase , return_tensors=_lowerCamelCase , do_normalize=_lowerCamelCase , do_center_crop=_lowerCamelCase , **_lowerCamelCase)
encoding.update(_lowerCamelCase)
return encoding
def snake_case__ ( self , *_lowerCamelCase , **_lowerCamelCase):
return self.tokenizer.batch_decode(*_lowerCamelCase , **_lowerCamelCase)
def snake_case__ ( self , *_lowerCamelCase , **_lowerCamelCase):
return self.tokenizer.decode(*_lowerCamelCase , **_lowerCamelCase)
@property
def snake_case__ ( self):
UpperCAmelCase__ : Optional[int] = self.tokenizer.model_input_names
UpperCAmelCase__ : str = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) | 283 | 0 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__snake_case = logging.get_logger(__name__)
class lowercase__ ( _UpperCAmelCase ):
def __init__( self : Optional[int] , **UpperCAmelCase_ : Tuple ):
requires_backends(self , ['bs4'] )
super().__init__(**UpperCAmelCase_ )
def A_ ( self : Union[str, Any] , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
SCREAMING_SNAKE_CASE__ = parent.find_all(child.name , recursive=UpperCAmelCase_ )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(UpperCAmelCase_ ) else next(i for i, s in enumerate(UpperCAmelCase_ , 1 ) if s is child ) )
SCREAMING_SNAKE_CASE__ = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def A_ ( self : Optional[Any] , UpperCAmelCase_ : Optional[Any] ):
SCREAMING_SNAKE_CASE__ = BeautifulSoup(UpperCAmelCase_ , 'html.parser' )
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
for element in html_code.descendants:
if type(UpperCAmelCase_ ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
SCREAMING_SNAKE_CASE__ = html.unescape(UpperCAmelCase_ ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.xpath_soup(UpperCAmelCase_ )
stringaxtag_seq.append(UpperCAmelCase_ )
stringaxsubs_seq.append(UpperCAmelCase_ )
if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ):
raise ValueError('Number of doc strings and xtags does not correspond' )
if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ):
raise ValueError('Number of doc strings and xsubs does not correspond' )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def A_ ( self : Tuple , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE__ = ''
for tagname, subs in zip(UpperCAmelCase_ , UpperCAmelCase_ ):
xpath += F'/{tagname}'
if subs != 0:
xpath += F'[{subs}]'
return xpath
def __call__( self : Union[str, Any] , UpperCAmelCase_ : str ):
SCREAMING_SNAKE_CASE__ = False
# Check that strings has a valid type
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE__ = True
elif isinstance(UpperCAmelCase_ , (list, tuple) ):
if len(UpperCAmelCase_ ) == 0 or isinstance(html_strings[0] , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE__ = True
if not valid_strings:
raise ValueError(
'HTML strings must of type `str`, `List[str]` (batch of examples), '
F'but is of type {type(UpperCAmelCase_ )}.' )
SCREAMING_SNAKE_CASE__ = bool(isinstance(UpperCAmelCase_ , (list, tuple) ) and (isinstance(html_strings[0] , UpperCAmelCase_ )) )
if not is_batched:
SCREAMING_SNAKE_CASE__ = [html_strings]
# Get nodes + xpaths
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
for html_string in html_strings:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.get_three_from_single(UpperCAmelCase_ )
nodes.append(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = []
for node, tag_list, sub_list in zip(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE__ = self.construct_xpath(UpperCAmelCase_ , UpperCAmelCase_ )
xpath_strings.append(UpperCAmelCase_ )
xpaths.append(UpperCAmelCase_ )
# return as Dict
SCREAMING_SNAKE_CASE__ = {'nodes': nodes, 'xpaths': xpaths}
SCREAMING_SNAKE_CASE__ = BatchFeature(data=UpperCAmelCase_ , tensor_type=UpperCAmelCase_ )
return encoded_inputs
| 176 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
"""constant""": get_constant_schedule,
"""constant_w_warmup""": get_constant_schedule_with_warmup,
}
class lowercase__ ( _UpperCAmelCase ):
def __init__( self : str , UpperCAmelCase_ : int=None , UpperCAmelCase_ : List[str]=None , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Optional[Any] ):
super().__init__(*UpperCAmelCase_ , **UpperCAmelCase_ )
if config is None:
assert isinstance(self.model , UpperCAmelCase_ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F' {self.model.__class__}'
)
SCREAMING_SNAKE_CASE__ = self.model.config
else:
SCREAMING_SNAKE_CASE__ = config
SCREAMING_SNAKE_CASE__ = data_args
SCREAMING_SNAKE_CASE__ = self.config.tgt_vocab_size if isinstance(self.config , UpperCAmelCase_ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
' padding..' )
if self.args.label_smoothing == 0:
SCREAMING_SNAKE_CASE__ = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
SCREAMING_SNAKE_CASE__ = label_smoothed_nll_loss
def A_ ( self : Tuple , UpperCAmelCase_ : int ):
if self.optimizer is None:
SCREAMING_SNAKE_CASE__ = ['bias', 'LayerNorm.weight']
SCREAMING_SNAKE_CASE__ = [
{
'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'weight_decay': self.args.weight_decay,
},
{
'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'weight_decay': 0.0,
},
]
SCREAMING_SNAKE_CASE__ = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
SCREAMING_SNAKE_CASE__ = Adafactor
SCREAMING_SNAKE_CASE__ = {'scale_parameter': False, 'relative_step': False}
else:
SCREAMING_SNAKE_CASE__ = AdamW
SCREAMING_SNAKE_CASE__ = {
'betas': (self.args.adam_betaa, self.args.adam_betaa),
'eps': self.args.adam_epsilon,
}
SCREAMING_SNAKE_CASE__ = self.args.learning_rate
if self.sharded_ddp:
SCREAMING_SNAKE_CASE__ = OSS(
params=UpperCAmelCase_ , optim=UpperCAmelCase_ , **UpperCAmelCase_ , )
else:
SCREAMING_SNAKE_CASE__ = optimizer_cls(UpperCAmelCase_ , **UpperCAmelCase_ )
if self.lr_scheduler is None:
SCREAMING_SNAKE_CASE__ = self._get_lr_scheduler(UpperCAmelCase_ )
else: # ignoring --lr_scheduler
logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.' )
def A_ ( self : str , UpperCAmelCase_ : List[Any] ):
SCREAMING_SNAKE_CASE__ = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
SCREAMING_SNAKE_CASE__ = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
SCREAMING_SNAKE_CASE__ = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
SCREAMING_SNAKE_CASE__ = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=UpperCAmelCase_ )
return scheduler
def A_ ( self : List[str] ):
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def A_ ( self : Any , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Dict ):
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
SCREAMING_SNAKE_CASE__ = model(**UpperCAmelCase_ , use_cache=UpperCAmelCase_ )[0]
SCREAMING_SNAKE_CASE__ = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = model(**UpperCAmelCase_ , labels=UpperCAmelCase_ , use_cache=UpperCAmelCase_ )[:2]
else:
# compute label smoothed loss
SCREAMING_SNAKE_CASE__ = model(**UpperCAmelCase_ , use_cache=UpperCAmelCase_ )[0]
SCREAMING_SNAKE_CASE__ = torch.nn.functional.log_softmax(UpperCAmelCase_ , dim=-1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.loss_fn(UpperCAmelCase_ , UpperCAmelCase_ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def A_ ( self : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : List[Any] ):
SCREAMING_SNAKE_CASE__ = inputs.pop('labels' )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self._compute_loss(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
return loss
def A_ ( self : List[str] , UpperCAmelCase_ : nn.Module , UpperCAmelCase_ : Dict[str, Union[torch.Tensor, Any]] , UpperCAmelCase_ : bool , UpperCAmelCase_ : Optional[List[str]] = None , ):
SCREAMING_SNAKE_CASE__ = self._prepare_inputs(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = {
'max_length': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
SCREAMING_SNAKE_CASE__ = self.model.generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , **UpperCAmelCase_ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
SCREAMING_SNAKE_CASE__ = self._pad_tensors_to_max_len(UpperCAmelCase_ , gen_kwargs['max_length'] )
SCREAMING_SNAKE_CASE__ = inputs.pop('labels' )
with torch.no_grad():
# compute loss on predict data
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self._compute_loss(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
SCREAMING_SNAKE_CASE__ = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
SCREAMING_SNAKE_CASE__ = self._pad_tensors_to_max_len(UpperCAmelCase_ , gen_kwargs['max_length'] )
return (loss, logits, labels)
def A_ ( self : Union[str, Any] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : List[str] ):
# If PAD token is not defined at least EOS token has to be defined
SCREAMING_SNAKE_CASE__ = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
F' padded to `max_length`={max_length}' )
SCREAMING_SNAKE_CASE__ = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
SCREAMING_SNAKE_CASE__ = tensor
return padded_tensor
| 176 | 1 |
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if exponent == 1:
return base
if exponent % 2 == 0:
SCREAMING_SNAKE_CASE_ = _modexpt(__lowerCamelCase, exponent // 2, __lowerCamelCase ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(__lowerCamelCase, exponent - 1, __lowerCamelCase )) % modulo_value
def A__ ( __lowerCamelCase = 17_77, __lowerCamelCase = 18_55, __lowerCamelCase = 8 ):
SCREAMING_SNAKE_CASE_ = base
for _ in range(1, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = _modexpt(__lowerCamelCase, __lowerCamelCase, 10**digits )
return result
if __name__ == "__main__":
print(F"""{solution() = }""")
| 360 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if not arr:
return None, None, 0
if low == high:
return low, high, arr[low]
SCREAMING_SNAKE_CASE_ = (low + high) // 2
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = max_subarray(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = max_subarray(__lowerCamelCase, mid + 1, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = max_cross_sum(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
if left_sum >= right_sum and left_sum >= cross_sum:
return left_low, left_high, left_sum
elif right_sum >= left_sum and right_sum >= cross_sum:
return right_low, right_high, right_sum
return cross_left, cross_right, cross_sum
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = float('''-inf''' ), -1
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = float('''-inf''' ), -1
SCREAMING_SNAKE_CASE_ = 0
for i in range(__lowerCamelCase, low - 1, -1 ):
summ += arr[i]
if summ > left_sum:
SCREAMING_SNAKE_CASE_ = summ
SCREAMING_SNAKE_CASE_ = i
SCREAMING_SNAKE_CASE_ = 0
for i in range(mid + 1, high + 1 ):
summ += arr[i]
if summ > right_sum:
SCREAMING_SNAKE_CASE_ = summ
SCREAMING_SNAKE_CASE_ = i
return max_left, max_right, (left_sum + right_sum)
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = [randint(1, __lowerCamelCase ) for _ in range(__lowerCamelCase )]
SCREAMING_SNAKE_CASE_ = time.time()
max_subarray(__lowerCamelCase, 0, input_size - 1 )
SCREAMING_SNAKE_CASE_ = time.time()
return end - start
def A__ ( ):
SCREAMING_SNAKE_CASE_ = [10, 1_00, 10_00, 1_00_00, 5_00_00, 10_00_00, 20_00_00, 30_00_00, 40_00_00, 50_00_00]
SCREAMING_SNAKE_CASE_ = [time_max_subarray(__lowerCamelCase ) for input_size in input_sizes]
print('''No of Inputs\t\tTime Taken''' )
for input_size, runtime in zip(__lowerCamelCase, __lowerCamelCase ):
print(__lowerCamelCase, '''\t\t''', __lowerCamelCase )
plt.plot(__lowerCamelCase, __lowerCamelCase )
plt.xlabel('''Number of Inputs''' )
plt.ylabel('''Time taken in seconds''' )
plt.show()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 257 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class lowerCamelCase__( __lowerCamelCase , unittest.TestCase):
UpperCAmelCase__ : Union[str, Any] = RoFormerTokenizer
UpperCAmelCase__ : Dict = RoFormerTokenizerFast
UpperCAmelCase__ : Union[str, Any] = True
UpperCAmelCase__ : Tuple = True
def lowerCAmelCase__ ( self: str ):
super().setUp()
def lowerCAmelCase__ ( self: Optional[int] , **UpperCamelCase_: List[str] ):
return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" , **UpperCamelCase_ )
def lowerCAmelCase__ ( self: Dict , **UpperCamelCase_: str ):
return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" , **UpperCamelCase_ )
def lowerCAmelCase__ ( self: List[Any] ):
__lowerCamelCase = """永和服装饰品有限公司,今天天气非常好"""
__lowerCamelCase = """永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
return input_text, output_text
def lowerCAmelCase__ ( self: Optional[Any] ):
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase, __lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , UpperCamelCase_ )
def lowerCAmelCase__ ( self: Optional[Any] ):
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase, __lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , UpperCamelCase_ )
def lowerCAmelCase__ ( self: Any ):
pass
def lowerCAmelCase__ ( self: Union[str, Any] ):
pass
def lowerCAmelCase__ ( self: Any ):
pass
| 12 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
UpperCAmelCase_ = logging.get_logger(__name__)
class lowerCamelCase__:
def __init__( self: Any , UpperCamelCase_: str , UpperCamelCase_: Dict ):
__lowerCamelCase = question_encoder
__lowerCamelCase = generator
__lowerCamelCase = self.question_encoder
def lowerCAmelCase__ ( self: List[str] , UpperCamelCase_: Optional[Any] ):
if os.path.isfile(UpperCamelCase_ ):
raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file' )
os.makedirs(UpperCamelCase_ , exist_ok=UpperCamelCase_ )
__lowerCamelCase = os.path.join(UpperCamelCase_ , """question_encoder_tokenizer""" )
__lowerCamelCase = os.path.join(UpperCamelCase_ , """generator_tokenizer""" )
self.question_encoder.save_pretrained(UpperCamelCase_ )
self.generator.save_pretrained(UpperCamelCase_ )
@classmethod
def lowerCAmelCase__ ( cls: List[Any] , UpperCamelCase_: Dict , **UpperCamelCase_: Union[str, Any] ):
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
__lowerCamelCase = kwargs.pop("""config""" , UpperCamelCase_ )
if config is None:
__lowerCamelCase = RagConfig.from_pretrained(UpperCamelCase_ )
__lowerCamelCase = AutoTokenizer.from_pretrained(
UpperCamelCase_ , config=config.question_encoder , subfolder="""question_encoder_tokenizer""" )
__lowerCamelCase = AutoTokenizer.from_pretrained(
UpperCamelCase_ , config=config.generator , subfolder="""generator_tokenizer""" )
return cls(question_encoder=UpperCamelCase_ , generator=UpperCamelCase_ )
def __call__( self: Tuple , *UpperCamelCase_: int , **UpperCamelCase_: int ):
return self.current_tokenizer(*UpperCamelCase_ , **UpperCamelCase_ )
def lowerCAmelCase__ ( self: Tuple , *UpperCamelCase_: List[Any] , **UpperCamelCase_: List[Any] ):
return self.generator.batch_decode(*UpperCamelCase_ , **UpperCamelCase_ )
def lowerCAmelCase__ ( self: Optional[Any] , *UpperCamelCase_: str , **UpperCamelCase_: Union[str, Any] ):
return self.generator.decode(*UpperCamelCase_ , **UpperCamelCase_ )
def lowerCAmelCase__ ( self: str ):
__lowerCamelCase = self.question_encoder
def lowerCAmelCase__ ( self: Optional[int] ):
__lowerCamelCase = self.generator
def lowerCAmelCase__ ( self: Tuple , UpperCamelCase_: List[str] , UpperCamelCase_: Optional[List[str]] = None , UpperCamelCase_: Optional[int] = None , UpperCamelCase_: Optional[int] = None , UpperCamelCase_: str = "longest" , UpperCamelCase_: str = None , UpperCamelCase_: bool = True , **UpperCamelCase_: int , ):
warnings.warn(
"""`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
"""regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
"""context manager to prepare your targets. See the documentation of your specific tokenizer for more """
"""details""" , UpperCamelCase_ , )
if max_length is None:
__lowerCamelCase = self.current_tokenizer.model_max_length
__lowerCamelCase = self(
UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , return_tensors=UpperCamelCase_ , max_length=UpperCamelCase_ , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , **UpperCamelCase_ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
__lowerCamelCase = self.current_tokenizer.model_max_length
__lowerCamelCase = self(
text_target=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , return_tensors=UpperCamelCase_ , padding=UpperCamelCase_ , max_length=UpperCamelCase_ , truncation=UpperCamelCase_ , **UpperCamelCase_ , )
__lowerCamelCase = labels["""input_ids"""]
return model_inputs
| 12 | 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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'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_ ):
__UpperCAmelCase : int = 'swin'
__UpperCAmelCase : str = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__(self : Dict , __UpperCAmelCase : Dict=2_2_4 , __UpperCAmelCase : Tuple=4 , __UpperCAmelCase : Dict=3 , __UpperCAmelCase : str=9_6 , __UpperCAmelCase : Union[str, Any]=[2, 2, 6, 2] , __UpperCAmelCase : int=[3, 6, 1_2, 2_4] , __UpperCAmelCase : Tuple=7 , __UpperCAmelCase : Tuple=4.0 , __UpperCAmelCase : Dict=True , __UpperCAmelCase : List[str]=0.0 , __UpperCAmelCase : Union[str, Any]=0.0 , __UpperCAmelCase : Optional[Any]=0.1 , __UpperCAmelCase : List[Any]="gelu" , __UpperCAmelCase : List[str]=False , __UpperCAmelCase : List[Any]=0.02 , __UpperCAmelCase : Any=1E-5 , __UpperCAmelCase : str=3_2 , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Any=None , **__UpperCAmelCase : Any , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**__UpperCAmelCase )
UpperCAmelCase__ = image_size
UpperCAmelCase__ = patch_size
UpperCAmelCase__ = num_channels
UpperCAmelCase__ = embed_dim
UpperCAmelCase__ = depths
UpperCAmelCase__ = len(__UpperCAmelCase )
UpperCAmelCase__ = num_heads
UpperCAmelCase__ = window_size
UpperCAmelCase__ = mlp_ratio
UpperCAmelCase__ = qkv_bias
UpperCAmelCase__ = hidden_dropout_prob
UpperCAmelCase__ = attention_probs_dropout_prob
UpperCAmelCase__ = drop_path_rate
UpperCAmelCase__ = hidden_act
UpperCAmelCase__ = use_absolute_embeddings
UpperCAmelCase__ = layer_norm_eps
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = 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
UpperCAmelCase__ = int(embed_dim * 2 ** (len(__UpperCAmelCase ) - 1) )
UpperCAmelCase__ = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(__UpperCAmelCase ) + 1 )]
UpperCAmelCase__ , UpperCAmelCase__ = get_aligned_output_features_output_indices(
out_features=__UpperCAmelCase , out_indices=__UpperCAmelCase , stage_names=self.stage_names )
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : List[str] = version.parse('1.11' )
@property
def lowercase_ (self : str ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def lowercase_ (self : str ) -> float:
"""simple docstring"""
return 1E-4
| 143 | import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
UpperCamelCase__ = logging.getLogger(__name__)
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ = argparse.ArgumentParser(
description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset." )
parser.add_argument(
"--dataset_name", type=__A, default="wikitext", help="Name of the training. Explore datasets at: hf.co/datasets.", )
parser.add_argument(
"--dataset_config", type=__A, default="wikitext-103-raw-v1", help="Configuration name of the dataset." )
parser.add_argument(
"--tokenizer_name_or_path", type=__A, default="sayakpaul/unigram-tokenizer-wikitext", help="Tokenizer identifier. Can be a local filepath or a Hub identifier.", )
parser.add_argument(
"--shard_size", type=__A, default=1_000, help="Number of entries to go in a single shard.", )
parser.add_argument("--split", type=__A, default="train", choices=["train", "test", "validation"] )
parser.add_argument(
"--limit", default=__A, type=__A, help="Limit the number of shards (used for debugging).", )
parser.add_argument(
"--max_length", type=__A, default=512, help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
" sequence length that is a multiple of 8.", )
parser.add_argument(
"--output_dir", default="tf-tpu", type=__A, help="Output directory where the TFRecord shards will be saved. If the"
" path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
" shards will be directly saved to a Google Cloud Storage bucket.", )
UpperCAmelCase__ = parser.parse_args()
return args
def lowerCAmelCase_ ( __A ) -> Optional[int]:
'''simple docstring'''
def fn(__A ):
return tokenizer(examples["text"] )
return fn
def lowerCAmelCase_ ( __A ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ = []
for i in range(len(tokenized_data["input_ids"] ) ):
UpperCAmelCase__ = {
"input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i] ) ),
"attention_mask": tf.train.Feature(
intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i] ) ),
}
UpperCAmelCase__ = tf.train.Features(feature=__A )
UpperCAmelCase__ = tf.train.Example(features=__A )
UpperCAmelCase__ = example.SerializeToString()
records.append(__A )
return records
def lowerCAmelCase_ ( __A ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ = datasets.load_dataset(args.dataset_name, args.dataset_config, split=args.split )
if args.limit is not None:
UpperCAmelCase__ = min(len(__A ), args.limit )
UpperCAmelCase__ = dataset.select(range(__A ) )
print(f"""Limiting the dataset to {args.limit} entries.""" )
UpperCAmelCase__ = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path )
# Handle output directory creation.
# For serializing into a Google Cloud Storage Bucket, one needs to first
# create a bucket.
if "gs" not in args.output_dir:
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
UpperCAmelCase__ = os.path.join(args.output_dir, args.split )
if not os.path.exists(__A ):
os.makedirs(__A )
else:
UpperCAmelCase__ = os.path.join(args.output_dir, args.split )
# Tokenize the whole dataset at once.
UpperCAmelCase__ = tokenize_function(__A )
UpperCAmelCase__ = dataset.map(__A, batched=__A, num_proc=4, remove_columns=["text"] )
# We need to concatenate all our texts together, and then split the result
# into chunks of a fixed size, which we will call block_size. To do this, we
# will use the map method again, with the option batched=True. When we use batched=True,
# the function we pass to map() will be passed multiple inputs at once, allowing us
# to group them into more or fewer examples than we had in the input.
# This allows us to create our new fixed-length samples. The advantage of this
# method is that we don't lose a whole lot of content from the dataset compared to the
# case where we simply tokenize with a pre-defined max_length.
def group_texts(__A ):
# Concatenate all texts.
UpperCAmelCase__ = {k: sum(examples[k], [] ) for k in examples.keys()}
UpperCAmelCase__ = len(concatenated_examples[list(examples.keys() )[0]] )
# We drop the small remainder, though you could add padding instead if the model supports it
# In this, as in all things, we advise you to follow your heart 🫀
UpperCAmelCase__ = (total_length // args.max_length) * args.max_length
# Split by chunks of max_len.
UpperCAmelCase__ = {
k: [t[i : i + args.max_length] for i in range(0, __A, args.max_length )]
for k, t in concatenated_examples.items()
}
return result
UpperCAmelCase__ = dataset_tokenized.map(__A, batched=__A, batch_size=1_000, num_proc=4 )
UpperCAmelCase__ = 0
UpperCAmelCase__ = 0
for shard in range(0, len(__A ), args.shard_size ):
UpperCAmelCase__ = grouped_dataset[shard : shard + args.shard_size]
UpperCAmelCase__ = len(dataset_snapshot["input_ids"] )
UpperCAmelCase__ = os.path.join(__A, f"""dataset-{shard_count}-{records_containing}.tfrecord""" )
UpperCAmelCase__ = get_serialized_examples(__A )
with tf.io.TFRecordWriter(__A ) as out_file:
for i in range(len(__A ) ):
UpperCAmelCase__ = serialized_examples[i]
out_file.write(__A )
print("Wrote file {} containing {} records".format(__A, __A ) )
shard_count += 1
total_records += records_containing
with open(f"""split-{args.split}-records-count.txt""", "w" ) as f:
print(f"""Total {args.split} records: {total_records}""", file=__A )
if __name__ == "__main__":
UpperCamelCase__ = parse_args()
main(args)
| 143 | 1 |
"""simple docstring"""
import os
import sys
import unittest
lowerCAmelCase__ : Tuple = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
lowerCAmelCase__ : Tuple = os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
lowerCAmelCase__ : Optional[Any] = os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : List[str] ):
UpperCAmelCase__ = get_test_to_tester_mapping(lowerCamelCase__ )
UpperCAmelCase__ = get_test_to_tester_mapping(lowerCamelCase__ )
UpperCAmelCase__ = {'BertModelTest': 'BertModelTester'}
UpperCAmelCase__ = {
'BlipModelTest': 'BlipModelTester',
'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
'BlipTextModelTest': 'BlipTextModelTester',
'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
'BlipVQAModelTest': 'BlipVQAModelTester',
'BlipVisionModelTest': 'BlipVisionModelTester',
}
self.assertEqual(get_test_info.to_json(lowerCamelCase__ ) ,lowerCamelCase__ )
self.assertEqual(get_test_info.to_json(lowerCamelCase__ ) ,lowerCamelCase__ )
def __lowerCAmelCase ( self : Optional[Any] ):
UpperCAmelCase__ = get_model_to_test_mapping(lowerCamelCase__ )
UpperCAmelCase__ = get_model_to_test_mapping(lowerCamelCase__ )
UpperCAmelCase__ = {
'BertForMaskedLM': ['BertModelTest'],
'BertForMultipleChoice': ['BertModelTest'],
'BertForNextSentencePrediction': ['BertModelTest'],
'BertForPreTraining': ['BertModelTest'],
'BertForQuestionAnswering': ['BertModelTest'],
'BertForSequenceClassification': ['BertModelTest'],
'BertForTokenClassification': ['BertModelTest'],
'BertLMHeadModel': ['BertModelTest'],
'BertModel': ['BertModelTest'],
}
UpperCAmelCase__ = {
'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
'BlipForQuestionAnswering': ['BlipVQAModelTest'],
'BlipModel': ['BlipModelTest'],
'BlipTextModel': ['BlipTextModelTest'],
'BlipVisionModel': ['BlipVisionModelTest'],
}
self.assertEqual(get_test_info.to_json(lowerCamelCase__ ) ,lowerCamelCase__ )
self.assertEqual(get_test_info.to_json(lowerCamelCase__ ) ,lowerCamelCase__ )
def __lowerCAmelCase ( self : int ):
UpperCAmelCase__ = get_model_to_tester_mapping(lowerCamelCase__ )
UpperCAmelCase__ = get_model_to_tester_mapping(lowerCamelCase__ )
UpperCAmelCase__ = {
'BertForMaskedLM': ['BertModelTester'],
'BertForMultipleChoice': ['BertModelTester'],
'BertForNextSentencePrediction': ['BertModelTester'],
'BertForPreTraining': ['BertModelTester'],
'BertForQuestionAnswering': ['BertModelTester'],
'BertForSequenceClassification': ['BertModelTester'],
'BertForTokenClassification': ['BertModelTester'],
'BertLMHeadModel': ['BertModelTester'],
'BertModel': ['BertModelTester'],
}
UpperCAmelCase__ = {
'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
'BlipForQuestionAnswering': ['BlipVQAModelTester'],
'BlipModel': ['BlipModelTester'],
'BlipTextModel': ['BlipTextModelTester'],
'BlipVisionModel': ['BlipVisionModelTester'],
}
self.assertEqual(get_test_info.to_json(lowerCamelCase__ ) ,lowerCamelCase__ )
self.assertEqual(get_test_info.to_json(lowerCamelCase__ ) ,lowerCamelCase__ )
| 98 | """simple docstring"""
def a_ ( lowerCamelCase ):
return str(lowerCamelCase ) == str(lowerCamelCase )[::-1]
def a_ ( lowerCamelCase ):
return int(lowerCamelCase ) + int(str(lowerCamelCase )[::-1] )
def a_ ( lowerCamelCase = 1_0_0_0_0 ):
UpperCAmelCase__ = []
for num in range(1 , lowerCamelCase ):
UpperCAmelCase__ = 0
UpperCAmelCase__ = num
while iterations < 5_0:
UpperCAmelCase__ = sum_reverse(lowerCamelCase )
iterations += 1
if is_palindrome(lowerCamelCase ):
break
else:
lychrel_nums.append(lowerCamelCase )
return len(lowerCamelCase )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 98 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
UpperCamelCase : List[Any] = analyze_text(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = list(''' ''' + ascii_lowercase )
# what is our total sum of probabilities.
UpperCamelCase : str = sum(single_char_strings.values() )
# one length string
UpperCamelCase : str = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
UpperCamelCase : Dict = single_char_strings[ch]
UpperCamelCase : str = my_str / all_sum
my_fir_sum += prob * math.loga(SCREAMING_SNAKE_CASE_ ) # entropy formula.
# print entropy
print(F"""{round(-1 * my_fir_sum ):.1f}""" )
# two len string
UpperCamelCase : Tuple = sum(two_char_strings.values() )
UpperCamelCase : Any = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
UpperCamelCase : List[Any] = cha + cha
if sequence in two_char_strings:
UpperCamelCase : Optional[Any] = two_char_strings[sequence]
UpperCamelCase : Any = int(SCREAMING_SNAKE_CASE_ ) / all_sum
my_sec_sum += prob * math.loga(SCREAMING_SNAKE_CASE_ )
# print second entropy
print(F"""{round(-1 * my_sec_sum ):.1f}""" )
# print the difference between them
print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
UpperCamelCase : Optional[int] = Counter() # type: ignore
UpperCamelCase : List[str] = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def a ( ):
"""simple docstring"""
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| 370 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase : Union[str, Any] = {
"configuration_mgp_str": ["MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP", "MgpstrConfig"],
"processing_mgp_str": ["MgpstrProcessor"],
"tokenization_mgp_str": ["MgpstrTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : Union[str, Any] = [
"MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST",
"MgpstrModel",
"MgpstrPreTrainedModel",
"MgpstrForSceneTextRecognition",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
__UpperCAmelCase : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 315 | 0 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case_ = _modexpt(SCREAMING_SNAKE_CASE__ , exponent // 2 , SCREAMING_SNAKE_CASE__ ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(SCREAMING_SNAKE_CASE__ , exponent - 1 , SCREAMING_SNAKE_CASE__ )) % modulo_value
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ = 1777 , SCREAMING_SNAKE_CASE__ = 1855 , SCREAMING_SNAKE_CASE__ = 8 ):
snake_case_ = base
for _ in range(1 , SCREAMING_SNAKE_CASE__ ):
snake_case_ = _modexpt(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 10**digits )
return result
if __name__ == "__main__":
print(f"""{solution() = }""") | 8 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"""The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"""
)
__UpperCamelCase : Union[str, Any] = None
__UpperCamelCase : Any = {
"""7B""": 11008,
"""13B""": 13824,
"""30B""": 17920,
"""65B""": 22016,
"""70B""": 28672,
}
__UpperCamelCase : Optional[Any] = {
"""7B""": 1,
"""7Bf""": 1,
"""13B""": 2,
"""13Bf""": 2,
"""30B""": 4,
"""65B""": 8,
"""70B""": 8,
"""70Bf""": 8,
}
def a_ ( _A , _A=1 , _A=256 ) -> str:
"""simple docstring"""
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def a_ ( _A ) -> int:
"""simple docstring"""
with open(_A , 'r' ) as f:
return json.load(_A )
def a_ ( _A , _A ) -> int:
"""simple docstring"""
with open(_A , 'w' ) as f:
json.dump(_A , _A )
def a_ ( _A , _A , _A , _A=True ) -> List[str]:
"""simple docstring"""
os.makedirs(_A , exist_ok=_A )
snake_case__ = os.path.join(_A , 'tmp' )
os.makedirs(_A , exist_ok=_A )
snake_case__ = read_json(os.path.join(_A , 'params.json' ) )
snake_case__ = NUM_SHARDS[model_size]
snake_case__ = params['n_layers']
snake_case__ = params['n_heads']
snake_case__ = n_heads // num_shards
snake_case__ = params['dim']
snake_case__ = dim // n_heads
snake_case__ = 10000.0
snake_case__ = 1.0 / (base ** (torch.arange(0 , _A , 2 ).float() / dims_per_head))
if "n_kv_heads" in params:
snake_case__ = params['n_kv_heads'] # for GQA / MQA
snake_case__ = n_heads_per_shard // num_key_value_heads
snake_case__ = dim // num_key_value_heads
else: # compatibility with other checkpoints
snake_case__ = n_heads
snake_case__ = n_heads_per_shard
snake_case__ = dim
# permute for sliced rotary
def permute(_A , _A=n_heads , _A=dim , _A=dim ):
return w.view(_A , dima // n_heads // 2 , 2 , _A ).transpose(1 , 2 ).reshape(_A , _A )
print(f'''Fetching all parameters from the checkpoint at {input_base_path}.''' )
# Load weights
if model_size == "7B":
# Not sharded
# (The sharded implementation would also work, but this is simpler.)
snake_case__ = torch.load(os.path.join(_A , 'consolidated.00.pth' ) , map_location='cpu' )
else:
# Sharded
snake_case__ = [
torch.load(os.path.join(_A , f'''consolidated.{i:02d}.pth''' ) , map_location='cpu' )
for i in range(_A )
]
snake_case__ = 0
snake_case__ = {'weight_map': {}}
for layer_i in range(_A ):
snake_case__ = f'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
if model_size == "7B":
# Unsharded
snake_case__ = {
f'''model.layers.{layer_i}.self_attn.q_proj.weight''': permute(
loaded[f'''layers.{layer_i}.attention.wq.weight'''] ),
f'''model.layers.{layer_i}.self_attn.k_proj.weight''': permute(
loaded[f'''layers.{layer_i}.attention.wk.weight'''] ),
f'''model.layers.{layer_i}.self_attn.v_proj.weight''': loaded[f'''layers.{layer_i}.attention.wv.weight'''],
f'''model.layers.{layer_i}.self_attn.o_proj.weight''': loaded[f'''layers.{layer_i}.attention.wo.weight'''],
f'''model.layers.{layer_i}.mlp.gate_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w1.weight'''],
f'''model.layers.{layer_i}.mlp.down_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w2.weight'''],
f'''model.layers.{layer_i}.mlp.up_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w3.weight'''],
f'''model.layers.{layer_i}.input_layernorm.weight''': loaded[f'''layers.{layer_i}.attention_norm.weight'''],
f'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[f'''layers.{layer_i}.ffn_norm.weight'''],
}
else:
# Sharded
# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
snake_case__ = {
f'''model.layers.{layer_i}.input_layernorm.weight''': loaded[0][
f'''layers.{layer_i}.attention_norm.weight'''
].clone(),
f'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[0][
f'''layers.{layer_i}.ffn_norm.weight'''
].clone(),
}
snake_case__ = permute(
torch.cat(
[
loaded[i][f'''layers.{layer_i}.attention.wq.weight'''].view(_A , _A , _A )
for i in range(_A )
] , dim=0 , ).reshape(_A , _A ) )
snake_case__ = permute(
torch.cat(
[
loaded[i][f'''layers.{layer_i}.attention.wk.weight'''].view(
_A , _A , _A )
for i in range(_A )
] , dim=0 , ).reshape(_A , _A ) , _A , _A , _A , )
snake_case__ = torch.cat(
[
loaded[i][f'''layers.{layer_i}.attention.wv.weight'''].view(
_A , _A , _A )
for i in range(_A )
] , dim=0 , ).reshape(_A , _A )
snake_case__ = torch.cat(
[loaded[i][f'''layers.{layer_i}.attention.wo.weight'''] for i in range(_A )] , dim=1 )
snake_case__ = torch.cat(
[loaded[i][f'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(_A )] , dim=0 )
snake_case__ = torch.cat(
[loaded[i][f'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(_A )] , dim=1 )
snake_case__ = torch.cat(
[loaded[i][f'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(_A )] , dim=0 )
snake_case__ = inv_freq
for k, v in state_dict.items():
snake_case__ = filename
param_count += v.numel()
torch.save(_A , os.path.join(_A , _A ) )
snake_case__ = f'''pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'''
if model_size == "7B":
# Unsharded
snake_case__ = {
'model.embed_tokens.weight': loaded['tok_embeddings.weight'],
'model.norm.weight': loaded['norm.weight'],
'lm_head.weight': loaded['output.weight'],
}
else:
snake_case__ = {
'model.norm.weight': loaded[0]['norm.weight'],
'model.embed_tokens.weight': torch.cat(
[loaded[i]['tok_embeddings.weight'] for i in range(_A )] , dim=1 ),
'lm_head.weight': torch.cat([loaded[i]['output.weight'] for i in range(_A )] , dim=0 ),
}
for k, v in state_dict.items():
snake_case__ = filename
param_count += v.numel()
torch.save(_A , os.path.join(_A , _A ) )
# Write configs
snake_case__ = {'total_size': param_count * 2}
write_json(_A , os.path.join(_A , 'pytorch_model.bin.index.json' ) )
snake_case__ = params['ffn_dim_multiplier'] if 'ffn_dim_multiplier' in params else 1
snake_case__ = params['multiple_of'] if 'multiple_of' in params else 256
snake_case__ = LlamaConfig(
hidden_size=_A , intermediate_size=compute_intermediate_size(_A , _A , _A ) , num_attention_heads=params['n_heads'] , num_hidden_layers=params['n_layers'] , rms_norm_eps=params['norm_eps'] , num_key_value_heads=_A , )
config.save_pretrained(_A )
# Make space so we can load the model properly now.
del state_dict
del loaded
gc.collect()
print('Loading the checkpoint in a Llama model.' )
snake_case__ = LlamaForCausalLM.from_pretrained(_A , torch_dtype=torch.floataa , low_cpu_mem_usage=_A )
# Avoid saving this as part of the config.
del model.config._name_or_path
print('Saving in the Transformers format.' )
model.save_pretrained(_A , safe_serialization=_A )
shutil.rmtree(_A )
def a_ ( _A , _A ) -> Tuple:
"""simple docstring"""
# Initialize the tokenizer based on the `spm` model
snake_case__ = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(f'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.''' )
snake_case__ = tokenizer_class(_A )
tokenizer.save_pretrained(_A )
def a_ ( ) -> str:
"""simple docstring"""
snake_case__ = argparse.ArgumentParser()
parser.add_argument(
'--input_dir' , help='Location of LLaMA weights, which contains tokenizer.model and model folders' , )
parser.add_argument(
'--model_size' , choices=['7B', '7Bf', '13B', '13Bf', '30B', '65B', '70B', '70Bf', 'tokenizer_only'] , )
parser.add_argument(
'--output_dir' , help='Location to write HF model and tokenizer' , )
parser.add_argument('--safe_serialization' , type=_A , help='Whether or not to save using `safetensors`.' )
snake_case__ = parser.parse_args()
if args.model_size != "tokenizer_only":
write_model(
model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , )
snake_case__ = os.path.join(args.input_dir , 'tokenizer.model' )
write_tokenizer(args.output_dir , _A )
if __name__ == "__main__":
main()
| 307 | 0 |
'''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def snake_case_ (UpperCamelCase : np.ndarray , UpperCamelCase : np.ndarray , UpperCamelCase : np.ndarray , UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
_a = cva.getAffineTransform(UpperCamelCase , UpperCamelCase )
return cva.warpAffine(UpperCamelCase , UpperCamelCase , (rows, cols) )
if __name__ == "__main__":
# read original image
_snake_case : Optional[Any] = cva.imread(
str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
)
# turn image in gray scale value
_snake_case : Union[str, Any] = cva.cvtColor(image, cva.COLOR_BGR2GRAY)
# get image shape
_snake_case , _snake_case : str = gray_img.shape
# set different points to rotate image
_snake_case : Tuple = np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
_snake_case : str = np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
_snake_case : Optional[int] = np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
_snake_case : int = np.array([[10, 100], [80, 50], [180, 250]], np.floataa)
# add all rotated images in a list
_snake_case : Optional[int] = [
gray_img,
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
]
# plot different image rotations
_snake_case : Optional[Any] = plt.figure(1)
_snake_case : int = ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
for i, image in enumerate(images):
plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
plt.title(titles[i])
plt.axis('off')
plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
plt.show()
| 179 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_snake_case : int = 'docs/source/en/_toctree.yml'
def snake_case_ (UpperCamelCase : Optional[int] ):
'''simple docstring'''
_a = defaultdict(UpperCamelCase )
for doc in model_doc:
counts[doc["local"]] += 1
_a = [key for key, value in counts.items() if value > 1]
_a = []
for duplicate_key in duplicates:
_a = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(UpperCamelCase ) > 1:
raise ValueError(
f'{duplicate_key} is present several times in the documentation table of content at '
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(UpperCamelCase , key=lambda UpperCamelCase : s["title"].lower() )
def snake_case_ (UpperCamelCase : str=False ):
'''simple docstring'''
with open(UpperCamelCase , encoding='''utf-8''' ) as f:
_a = yaml.safe_load(f.read() )
# Get to the API doc
_a = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_a = content[api_idx]['''sections''']
# Then to the model doc
_a = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
_a = api_doc[model_idx]['''sections''']
_a = [(idx, section) for idx, section in enumerate(UpperCamelCase ) if '''sections''' in section]
_a = False
for idx, modality_doc in modalities_docs:
_a = modality_doc['''sections''']
_a = clean_model_doc_toc(UpperCamelCase )
if old_modality_doc != new_modality_doc:
_a = True
if overwrite:
_a = new_modality_doc
if diff:
if overwrite:
_a = model_doc
_a = api_doc
with open(UpperCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(UpperCamelCase , allow_unicode=UpperCamelCase ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
_snake_case : List[str] = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
_snake_case : List[Any] = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 179 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : List[str] = MobileBertConfig.from_json_file(_UpperCAmelCase )
print(f"""Building PyTorch model from configuration: {config}""" )
A_ : List[Any] = MobileBertForPreTraining(_UpperCAmelCase )
# Load weights from tf checkpoint
A_ : List[str] = load_tf_weights_in_mobilebert(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , _UpperCAmelCase )
if __name__ == "__main__":
lowerCamelCase_ : Optional[Any] = 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_ : List[Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path) | 286 |
"""simple docstring"""
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('\'str\' object cannot be interpreted as an integer' )
if num == 0:
return "0b0"
A_ : str = False
if num < 0:
A_ : Dict = True
A_ : Union[str, Any] = -num
A_ : list[int] = []
while num > 0:
binary.insert(0 , num % 2 )
num >>= 1
if negative:
return "-0b" + "".join(str(_UpperCAmelCase ) for e in binary )
return "0b" + "".join(str(_UpperCAmelCase ) for e in binary )
if __name__ == "__main__":
import doctest
doctest.testmod() | 286 | 1 |
'''simple docstring'''
import unittest
import numpy as np
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = None , ):
'''simple docstring'''
A : List[Any] = np.shape(snake_case__ )
A : Optional[Any] = np.shape(snake_case__ )
A : Any = np.shape(snake_case__ )
if shape_a[0] != shape_b[0]:
A : Tuple = (
'''Expected the same number of rows for A and B. '''
F'Instead found A of size {shape_a} and B of size {shape_b}'
)
raise ValueError(snake_case__ )
if shape_b[1] != shape_c[1]:
A : List[Any] = (
'''Expected the same number of columns for B and C. '''
F'Instead found B of size {shape_b} and C of size {shape_c}'
)
raise ValueError(snake_case__ )
A : str = pseudo_inv
if a_inv is None:
try:
A : str = np.linalg.inv(snake_case__ )
except np.linalg.LinAlgError:
raise ValueError(
'''Input matrix A is not invertible. Cannot compute Schur complement.''' )
return mat_c - mat_b.T @ a_inv @ mat_b
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> None:
"""simple docstring"""
A : Optional[Any] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
A : Any = np.array([[0, 3], [3, 0], [2, 3]] )
A : Optional[int] = np.array([[2, 1], [6, 3]] )
A : int = schur_complement(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
A : Union[str, Any] = np.block([[a, b], [b.T, c]] )
A : Any = np.linalg.det(SCREAMING_SNAKE_CASE )
A : List[str] = np.linalg.det(SCREAMING_SNAKE_CASE )
A : Tuple = np.linalg.det(SCREAMING_SNAKE_CASE )
self.assertAlmostEqual(SCREAMING_SNAKE_CASE , det_a * det_s )
def __lowerCAmelCase ( self ) -> None:
"""simple docstring"""
A : Union[str, Any] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
A : str = np.array([[0, 3], [3, 0], [2, 3]] )
A : Tuple = np.array([[2, 1], [6, 3]] )
with self.assertRaises(SCREAMING_SNAKE_CASE ):
schur_complement(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> None:
"""simple docstring"""
A : Dict = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
A : Optional[int] = np.array([[0, 3], [3, 0], [2, 3]] )
A : int = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(SCREAMING_SNAKE_CASE ):
schur_complement(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 311 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class A :
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=13 , SCREAMING_SNAKE_CASE=30 , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=2 , 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=10 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=2 , ) -> List[str]:
"""simple docstring"""
A : List[str] = parent
A : Optional[Any] = batch_size
A : Tuple = image_size
A : int = patch_size
A : Optional[int] = num_channels
A : str = is_training
A : List[Any] = use_labels
A : Any = hidden_size
A : Any = num_hidden_layers
A : Optional[int] = num_attention_heads
A : Any = intermediate_size
A : List[str] = hidden_act
A : str = hidden_dropout_prob
A : Tuple = attention_probs_dropout_prob
A : Any = type_sequence_label_size
A : Optional[int] = initializer_range
A : Dict = scope
A : Tuple = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
A : List[Any] = (image_size // patch_size) ** 2
A : Tuple = num_patches + 2
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A : Tuple = None
if self.use_labels:
A : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A : Tuple = self.get_config()
return config, pixel_values, labels
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
return DeiTConfig(
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=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
A : Any = TFDeiTModel(config=SCREAMING_SNAKE_CASE )
A : str = 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 ) -> int:
"""simple docstring"""
A : Tuple = TFDeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE )
A : List[Any] = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A : Optional[int] = 1
A : str = TFDeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE )
A : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A : Tuple = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
A : str = self.type_sequence_label_size
A : Optional[Any] = TFDeiTForImageClassification(SCREAMING_SNAKE_CASE )
A : Optional[Any] = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A : Optional[Any] = 1
A : List[str] = TFDeiTForImageClassification(SCREAMING_SNAKE_CASE )
A : Any = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A : Optional[int] = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
A : Optional[int] = self.prepare_config_and_inputs()
A, A, A : Tuple = config_and_inputs
A : Any = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class A ( __snake_case , __snake_case , unittest.TestCase ):
__magic_name__ = (
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
__magic_name__ = (
{
'''feature-extraction''': TFDeiTModel,
'''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
A : Tuple = TFDeiTModelTester(self )
A : Dict = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE , has_text_modality=SCREAMING_SNAKE_CASE , hidden_size=37 )
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''DeiT does not use inputs_embeds''' )
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
pass
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
A, A : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A : Any = model_class(SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
A : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE , tf.keras.layers.Dense ) )
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
A, A : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A : Any = model_class(SCREAMING_SNAKE_CASE )
A : str = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A : Union[str, Any] = [*signature.parameters.keys()]
A : List[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
A : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
A : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ) -> Tuple:
"""simple docstring"""
A : Union[str, Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , return_labels=SCREAMING_SNAKE_CASE )
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A : List[str] = TFDeiTModel.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
def lowerCAmelCase_ ( ):
'''simple docstring'''
A : str = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class A ( unittest.TestCase ):
@cached_property
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224''' )
if is_vision_available()
else None
)
@slow
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : Union[str, Any] = TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224''' )
A : Dict = self.default_image_processor
A : List[str] = prepare_img()
A : Any = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
# forward pass
A : Optional[int] = model(**SCREAMING_SNAKE_CASE )
# verify the logits
A : List[Any] = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE )
A : str = tf.constant([-1.0_266, 0.1_912, -1.2_861] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1e-4 ) )
| 311 | 1 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
A : str = TypeVar('T')
A : Dict = Union[List[T], Tuple[T, ...]]
A : Union[str, Any] = Union[T, List[T], Dict[str, T]]
A : int = Union[str, bytes, os.PathLike] | 6 |
from typing import Dict
from .base import GenericTensor, Pipeline
class __A( a ):
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=None , _snake_case=None , _snake_case=None , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
if tokenize_kwargs is None:
__a = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)''' )
__a = truncation
__a = tokenize_kwargs
__a = {}
if return_tensors is not None:
__a = return_tensors
return preprocess_params, {}, postprocess_params
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , **_snake_case ) -> Dict[str, GenericTensor]:
'''simple docstring'''
__a = self.framework
__a = self.tokenizer(_snake_case , return_tensors=_snake_case , **_snake_case )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = self.model(**_snake_case )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case=False ) -> Optional[int]:
'''simple docstring'''
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self , *_snake_case , **_snake_case ) -> Any:
'''simple docstring'''
return super().__call__(*_snake_case , **_snake_case ) | 6 | 1 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelerator
def _lowercase ( __snake_case ) -> Optional[Any]:
__lowerCAmelCase : str = fname.split(os.path.sep )[-1]
return re.search(r"^(.*)_\d+\.jpg$" ,__snake_case ).groups()[0]
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self: Tuple , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Dict=None , _SCREAMING_SNAKE_CASE: Any=None) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : str = file_names
__lowerCAmelCase : Optional[int] = image_transform
__lowerCAmelCase : List[Any] = label_to_id
def __len__( self: Union[str, Any]) -> int:
"""simple docstring"""
return len(self.file_names)
def __getitem__( self: List[Any] , _SCREAMING_SNAKE_CASE: Optional[Any]) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : int = self.file_names[idx]
__lowerCAmelCase : List[str] = PIL.Image.open(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Tuple = raw_image.convert("RGB")
if self.image_transform is not None:
__lowerCAmelCase : Union[str, Any] = self.image_transform(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = extract_label(_SCREAMING_SNAKE_CASE)
if self.label_to_id is not None:
__lowerCAmelCase : str = self.label_to_id[label]
return {"image": image, "label": label}
def _lowercase ( __snake_case ,__snake_case ) -> Optional[int]:
# Initialize accelerator
if args.with_tracking:
__lowerCAmelCase : str = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,log_with="all" ,project_dir=args.project_dir )
else:
__lowerCAmelCase : Any = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCAmelCase : int = config["lr"]
__lowerCAmelCase : Union[str, Any] = int(config["num_epochs"] )
__lowerCAmelCase : Tuple = int(config["seed"] )
__lowerCAmelCase : Tuple = int(config["batch_size"] )
__lowerCAmelCase : int = config["image_size"]
if not isinstance(__snake_case ,(list, tuple) ):
__lowerCAmelCase : Tuple = (image_size, image_size)
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps ,"isdigit" ):
if args.checkpointing_steps == "epoch":
__lowerCAmelCase : Optional[Any] = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
__lowerCAmelCase : Dict = int(args.checkpointing_steps )
else:
raise ValueError(
F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.""" )
else:
__lowerCAmelCase : int = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
__lowerCAmelCase : Dict = os.path.split(__snake_case )[-1].split("." )[0]
accelerator.init_trackers(__snake_case ,__snake_case )
# Grab all the image filenames
__lowerCAmelCase : Union[str, Any] = [os.path.join(args.data_dir ,__snake_case ) for fname in os.listdir(args.data_dir ) if fname.endswith(".jpg" )]
# Build the label correspondences
__lowerCAmelCase : Union[str, Any] = [extract_label(__snake_case ) for fname in file_names]
__lowerCAmelCase : Any = list(set(__snake_case ) )
id_to_label.sort()
__lowerCAmelCase : Optional[Any] = {lbl: i for i, lbl in enumerate(__snake_case )}
# Set the seed before splitting the data.
np.random.seed(__snake_case )
torch.manual_seed(__snake_case )
torch.cuda.manual_seed_all(__snake_case )
# Split our filenames between train and validation
__lowerCAmelCase : List[str] = np.random.permutation(len(__snake_case ) )
__lowerCAmelCase : Dict = int(0.8 * len(__snake_case ) )
__lowerCAmelCase : str = random_perm[:cut]
__lowerCAmelCase : Optional[int] = random_perm[cut:]
# For training we use a simple RandomResizedCrop
__lowerCAmelCase : str = Compose([RandomResizedCrop(__snake_case ,scale=(0.5, 1.0) ), ToTensor()] )
__lowerCAmelCase : List[str] = PetsDataset(
[file_names[i] for i in train_split] ,image_transform=__snake_case ,label_to_id=__snake_case )
# For evaluation, we use a deterministic Resize
__lowerCAmelCase : Union[str, Any] = Compose([Resize(__snake_case ), ToTensor()] )
__lowerCAmelCase : List[str] = PetsDataset([file_names[i] for i in eval_split] ,image_transform=__snake_case ,label_to_id=__snake_case )
# Instantiate dataloaders.
__lowerCAmelCase : Union[str, Any] = DataLoader(__snake_case ,shuffle=__snake_case ,batch_size=__snake_case ,num_workers=4 )
__lowerCAmelCase : Any = DataLoader(__snake_case ,shuffle=__snake_case ,batch_size=__snake_case ,num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCAmelCase : int = create_model("resnet50d" ,pretrained=__snake_case ,num_classes=len(__snake_case ) )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowerCAmelCase : List[str] = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
__lowerCAmelCase : Any = False
for param in model.get_classifier().parameters():
__lowerCAmelCase : List[Any] = True
# We normalize the batches of images to be a bit faster.
__lowerCAmelCase : Optional[Any] = torch.tensor(model.default_cfg["mean"] )[None, :, None, None].to(accelerator.device )
__lowerCAmelCase : int = torch.tensor(model.default_cfg["std"] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
__lowerCAmelCase : int = torch.optim.Adam(params=model.parameters() ,lr=lr / 25 )
# Instantiate learning rate scheduler
__lowerCAmelCase : List[Any] = OneCycleLR(optimizer=__snake_case ,max_lr=__snake_case ,epochs=__snake_case ,steps_per_epoch=len(__snake_case ) )
# 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.
__lowerCAmelCase : Tuple = accelerator.prepare(
__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
# We need to keep track of how many total steps we have iterated over
__lowerCAmelCase : Dict = 0
# We also need to keep track of the starting epoch so files are named properly
__lowerCAmelCase : List[str] = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}""" )
accelerator.load_state(args.resume_from_checkpoint )
__lowerCAmelCase : Optional[Any] = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
__lowerCAmelCase : Optional[int] = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
__lowerCAmelCase : Optional[Any] = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
__lowerCAmelCase : str = os.path.splitext(__snake_case )[0]
if "epoch" in training_difference:
__lowerCAmelCase : Dict = int(training_difference.replace("epoch_" ,"" ) ) + 1
__lowerCAmelCase : Optional[Any] = None
else:
__lowerCAmelCase : Any = int(training_difference.replace("step_" ,"" ) )
__lowerCAmelCase : Optional[int] = resume_step // len(__snake_case )
resume_step -= starting_epoch * len(__snake_case )
# Now we train the model
for epoch in range(__snake_case ,__snake_case ):
model.train()
if args.with_tracking:
__lowerCAmelCase : Any = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
__lowerCAmelCase : Optional[int] = accelerator.skip_first_batches(__snake_case ,__snake_case )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
__lowerCAmelCase : Optional[int] = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
__lowerCAmelCase : List[Any] = {k: v.to(accelerator.device ) for k, v in batch.items()}
__lowerCAmelCase : Union[str, Any] = (batch["image"] - mean) / std
__lowerCAmelCase : Optional[int] = model(__snake_case )
__lowerCAmelCase : List[str] = torch.nn.functional.cross_entropy(__snake_case ,batch["label"] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(__snake_case )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(__snake_case ,__snake_case ):
__lowerCAmelCase : List[Any] = F"""step_{overall_step}"""
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
__lowerCAmelCase : Tuple = os.path.join(args.output_dir ,__snake_case )
accelerator.save_state(__snake_case )
model.eval()
__lowerCAmelCase : int = 0
__lowerCAmelCase : Optional[int] = 0
for step, batch in enumerate(__snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
__lowerCAmelCase : Tuple = {k: v.to(accelerator.device ) for k, v in batch.items()}
__lowerCAmelCase : Optional[Any] = (batch["image"] - mean) / std
with torch.no_grad():
__lowerCAmelCase : Optional[Any] = model(__snake_case )
__lowerCAmelCase : List[str] = outputs.argmax(dim=-1 )
__lowerCAmelCase : Optional[int] = accelerator.gather_for_metrics((predictions, batch["label"]) )
__lowerCAmelCase : str = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
__lowerCAmelCase : Optional[Any] = accurate.item() / num_elems
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}""" )
if args.with_tracking:
accelerator.log(
{
"accuracy": 100 * eval_metric,
"train_loss": total_loss.item() / len(__snake_case ),
"epoch": epoch,
} ,step=__snake_case ,)
if checkpointing_steps == "epoch":
__lowerCAmelCase : Tuple = F"""epoch_{epoch}"""
if args.output_dir is not None:
__lowerCAmelCase : Optional[Any] = os.path.join(args.output_dir ,__snake_case )
accelerator.save_state(__snake_case )
if args.with_tracking:
accelerator.end_training()
def _lowercase ( ) -> Tuple:
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument("--data_dir" ,required=__snake_case ,help="The data folder on disk." )
parser.add_argument("--fp16" ,action="store_true" ,help="If passed, will use FP16 training." )
parser.add_argument(
"--mixed_precision" ,type=__snake_case ,default=__snake_case ,choices=["no", "fp16", "bf16", "fp8"] ,help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." ,)
parser.add_argument("--cpu" ,action="store_true" ,help="If passed, will train on the CPU." )
parser.add_argument(
"--checkpointing_steps" ,type=__snake_case ,default=__snake_case ,help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch." ,)
parser.add_argument(
"--output_dir" ,type=__snake_case ,default="." ,help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." ,)
parser.add_argument(
"--resume_from_checkpoint" ,type=__snake_case ,default=__snake_case ,help="If the training should continue from a checkpoint folder." ,)
parser.add_argument(
"--with_tracking" ,action="store_true" ,help="Whether to load in all available experiment trackers from the environment and use them for logging." ,)
parser.add_argument(
"--project_dir" ,type=__snake_case ,default="logs" ,help="Location on where to store experiment tracking logs` and relevent project information" ,)
__lowerCAmelCase : List[Any] = parser.parse_args()
__lowerCAmelCase : List[Any] = {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
training_function(__snake_case ,__snake_case )
if __name__ == "__main__":
main() | 358 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class A__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _SCREAMING_SNAKE_CASE ( self: Dict) -> Any:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small" , return_dict=_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[int] = AutoTokenizer.from_pretrained("google/mt5-small")
__lowerCAmelCase : Tuple = tokenizer("Hello there" , return_tensors="pt").input_ids
__lowerCAmelCase : List[str] = tokenizer("Hi I am" , return_tensors="pt").input_ids
__lowerCAmelCase : List[str] = model(input_ids.to(_SCREAMING_SNAKE_CASE) , labels=labels.to(_SCREAMING_SNAKE_CASE)).loss
__lowerCAmelCase : Optional[int] = -(labels.shape[-1] * loss.item())
__lowerCAmelCase : List[str] = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4) | 58 | 0 |
def A ( _SCREAMING_SNAKE_CASE ) -> list:
if n_term == "":
return []
lowerCamelCase : list = []
for temp in range(int(_SCREAMING_SNAKE_CASE ) ):
series.append(f'''1/{temp + 1}''' if series else "1" )
return series
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : Dict = input('Enter the last number (nth term) of the Harmonic Series')
print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
print(harmonic_series(nth_term))
| 48 | from ...processing_utils import ProcessorMixin
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = ["""image_processor""", """feature_extractor"""]
UpperCAmelCase_ : Optional[int] = """TvltImageProcessor"""
UpperCAmelCase_ : Optional[int] = """TvltFeatureExtractor"""
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ->Optional[int]:
super().__init__(image_processor=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE )
lowerCAmelCase = image_processor
lowerCAmelCase = feature_extractor
def __call__( self , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , ) ->List[Any]:
if images is None and audio is None:
raise ValueError('''You need to specify either an `images` or `audio` input to process.''' )
lowerCAmelCase = None
if images is not None:
lowerCAmelCase = self.image_processor(__SCREAMING_SNAKE_CASE , mask_pixel=__SCREAMING_SNAKE_CASE , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
if images_mixed is not None:
lowerCAmelCase = self.image_processor(__SCREAMING_SNAKE_CASE , is_mixed=__SCREAMING_SNAKE_CASE , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
if audio is not None:
lowerCAmelCase = self.feature_extractor(
__SCREAMING_SNAKE_CASE , *__SCREAMING_SNAKE_CASE , sampling_rate=__SCREAMING_SNAKE_CASE , mask_audio=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
lowerCAmelCase = {}
if audio is not None:
output_dict.update(__SCREAMING_SNAKE_CASE )
if images is not None:
output_dict.update(__SCREAMING_SNAKE_CASE )
if images_mixed_dict is not None:
output_dict.update(__SCREAMING_SNAKE_CASE )
return output_dict
@property
def SCREAMING_SNAKE_CASE_ ( self ) ->Any:
lowerCAmelCase = self.image_processor.model_input_names
lowerCAmelCase = self.feature_extractor.model_input_names
return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names ) )
| 338 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_A = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ["""SpeechEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ["""FlaxSpeechEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
_A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 365 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {"""vocab_file""": """vocab.json"""}
_A = {
"""vocab_file""": {
"""mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
}
}
_A = {"""mgp-str""": 2_7}
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Dict = VOCAB_FILES_NAMES
_lowerCamelCase :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase :Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Optional[int] , UpperCamelCase : int , UpperCamelCase : Union[str, Any]="[GO]" , UpperCamelCase : Any="[GO]" , UpperCamelCase : Tuple="[s]" , UpperCamelCase : List[Any]="[GO]" , **UpperCamelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
unk_token=UpperCamelCase , bos_token=UpperCamelCase , eos_token=UpperCamelCase , pad_token=UpperCamelCase , **UpperCamelCase , )
with open(UpperCamelCase , encoding="""utf-8""" ) as vocab_handle:
lowerCAmelCase__ : Optional[Any] = json.load(UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = {v: k for k, v in self.vocab.items()}
@property
def _lowerCAmelCase ( self : int ) -> Dict:
"""simple docstring"""
return len(self.vocab )
def _lowerCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
return dict(self.vocab , **self.added_tokens_encoder )
def _lowerCAmelCase ( self : List[Any] , UpperCamelCase : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = []
for s in text:
char_tokens.extend(UpperCamelCase )
return char_tokens
def _lowerCAmelCase ( self : List[Any] , UpperCamelCase : str ) -> Tuple:
"""simple docstring"""
return self.vocab.get(UpperCamelCase , self.vocab.get(self.unk_token ) )
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : int ) -> Any:
"""simple docstring"""
return self.decoder.get(UpperCamelCase )
def _lowerCAmelCase ( self : Dict , UpperCamelCase : str , UpperCamelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(UpperCamelCase ):
logger.error("""Vocabulary path ({}) should be a directory""".format(UpperCamelCase ) )
return
lowerCAmelCase__ : Union[str, Any] = os.path.join(
UpperCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
with open(UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.vocab , indent=2 , sort_keys=UpperCamelCase , ensure_ascii=UpperCamelCase ) + """\n""" )
return (vocab_file,)
| 212 | 0 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class UpperCAmelCase_ :
def __init__( self, __a, __a=13, __a=7, __a=True, __a=True, __a=True, __a=True, __a=99, __a=64, __a=5, __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, ):
'''simple docstring'''
_lowerCAmelCase : Tuple = parent
_lowerCAmelCase : Optional[int] = batch_size
_lowerCAmelCase : Optional[int] = seq_length
_lowerCAmelCase : List[Any] = is_training
_lowerCAmelCase : Any = use_input_mask
_lowerCAmelCase : List[Any] = use_token_type_ids
_lowerCAmelCase : str = use_labels
_lowerCAmelCase : Optional[int] = vocab_size
_lowerCAmelCase : Optional[Any] = hidden_size
_lowerCAmelCase : int = num_hidden_layers
_lowerCAmelCase : str = num_attention_heads
_lowerCAmelCase : Any = intermediate_size
_lowerCAmelCase : Optional[Any] = hidden_act
_lowerCAmelCase : Optional[int] = hidden_dropout_prob
_lowerCAmelCase : List[Any] = attention_probs_dropout_prob
_lowerCAmelCase : List[Any] = max_position_embeddings
_lowerCAmelCase : Union[str, Any] = type_vocab_size
_lowerCAmelCase : str = type_sequence_label_size
_lowerCAmelCase : Optional[Any] = initializer_range
_lowerCAmelCase : Dict = num_labels
_lowerCAmelCase : Any = num_choices
_lowerCAmelCase : Optional[int] = scope
_lowerCAmelCase : List[str] = vocab_size - 1
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
_lowerCAmelCase : Union[str, Any] = None
if self.use_input_mask:
_lowerCAmelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length])
_lowerCAmelCase : Any = None
if self.use_labels:
_lowerCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
_lowerCAmelCase : Optional[Any] = self.get_config()
return config, input_ids, input_mask, token_labels
def snake_case__ ( self):
'''simple docstring'''
return GPTNeoXConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=__a, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, )
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] = self.prepare_config_and_inputs()
_lowerCAmelCase : str = True
return config, input_ids, input_mask, token_labels
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[str] = GPTNeoXModel(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Optional[int] = model(__a, attention_mask=__a)
_lowerCAmelCase : str = model(__a)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = True
_lowerCAmelCase : List[Any] = GPTNeoXModel(__a)
model.to(__a)
model.eval()
_lowerCAmelCase : List[Any] = model(__a, attention_mask=__a)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def snake_case__ ( self, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : int = GPTNeoXForCausalLM(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Tuple = model(__a, attention_mask=__a, labels=__a)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def snake_case__ ( self, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.num_labels
_lowerCAmelCase : Union[str, Any] = GPTNeoXForQuestionAnswering(__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Tuple = model(__a, attention_mask=__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 snake_case__ ( self, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : str = self.num_labels
_lowerCAmelCase : str = GPTNeoXForSequenceClassification(__a)
model.to(__a)
model.eval()
_lowerCAmelCase : List[str] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowerCAmelCase : Dict = model(__a, attention_mask=__a, labels=__a)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def snake_case__ ( self, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : str = self.num_labels
_lowerCAmelCase : Dict = GPTNeoXForTokenClassification(__a)
model.to(__a)
model.eval()
_lowerCAmelCase : str = model(__a, attention_mask=__a, labels=__a)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = True
_lowerCAmelCase : Dict = GPTNeoXForCausalLM(config=__a)
model.to(__a)
model.eval()
# first forward pass
_lowerCAmelCase : List[Any] = model(__a, attention_mask=__a, use_cache=__a)
_lowerCAmelCase : Optional[int] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_lowerCAmelCase : Optional[Any] = ids_tensor((self.batch_size, 3), config.vocab_size)
_lowerCAmelCase : Dict = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
_lowerCAmelCase : int = torch.cat([input_ids, next_tokens], dim=-1)
_lowerCAmelCase : str = torch.cat([input_mask, next_mask], dim=-1)
_lowerCAmelCase : List[str] = model(__a, attention_mask=__a, output_hidden_states=__a)
_lowerCAmelCase : List[str] = output_from_no_past["hidden_states"][0]
_lowerCAmelCase : List[Any] = model(
__a, attention_mask=__a, past_key_values=__a, output_hidden_states=__a, )["hidden_states"][0]
# select random slice
_lowerCAmelCase : int = ids_tensor((1,), output_from_past.shape[-1]).item()
_lowerCAmelCase : str = output_from_no_past[:, -3:, random_slice_idx].detach()
_lowerCAmelCase : Union[str, Any] = 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(__a, __a, atol=1E-3))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] = config_and_inputs
_lowerCAmelCase : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( a , a , a , unittest.TestCase):
lowerCamelCase__ = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase__ = (GPTNeoXForCausalLM,) if is_torch_available() else ()
lowerCamelCase__ = (
{
'feature-extraction': GPTNeoXModel,
'question-answering': GPTNeoXForQuestionAnswering,
'text-classification': GPTNeoXForSequenceClassification,
'text-generation': GPTNeoXForCausalLM,
'token-classification': GPTNeoXForTokenClassification,
'zero-shot': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = GPTNeoXModelTester(self)
_lowerCAmelCase : Union[str, Any] = ConfigTester(self, config_class=__a, hidden_size=64, num_attention_heads=8)
def snake_case__ ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(__a, __a, __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(__a, __a, __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
_lowerCAmelCase : List[str] = None
self.model_tester.create_and_check_model_as_decoder(__a, __a, __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(__a, __a, __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a)
@unittest.skip(reason="Feed forward chunking is not implemented")
def snake_case__ ( self):
'''simple docstring'''
pass
@parameterized.expand([("linear",), ("dynamic",)])
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase : Optional[Any] = ids_tensor([1, 10], config.vocab_size)
_lowerCAmelCase : List[str] = 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 : List[Any] = GPTNeoXModel(__a)
original_model.to(__a)
original_model.eval()
_lowerCAmelCase : Union[str, Any] = original_model(__a).last_hidden_state
_lowerCAmelCase : Union[str, Any] = original_model(__a).last_hidden_state
set_seed(42) # Fixed seed at init time so the two models get the same random weights
_lowerCAmelCase : int = {"type": scaling_type, "factor": 10.0}
_lowerCAmelCase : Optional[int] = GPTNeoXModel(__a)
scaled_model.to(__a)
scaled_model.eval()
_lowerCAmelCase : Optional[Any] = scaled_model(__a).last_hidden_state
_lowerCAmelCase : Optional[Any] = scaled_model(__a).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(__a, __a, atol=1E-5))
else:
self.assertFalse(torch.allclose(__a, __a, atol=1E-5))
# The output should be different for long inputs
self.assertFalse(torch.allclose(__a, __a, atol=1E-5))
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped")
for checkpointing in [True, False]:
_lowerCAmelCase : List[str] = GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped")
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(__a)
_lowerCAmelCase : str = tokenizer("My favorite food is", return_tensors="pt").to(__a)
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
_lowerCAmelCase : str = "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"
_lowerCAmelCase : Any = model.generate(**__a, do_sample=__a, max_new_tokens=20)
_lowerCAmelCase : str = tokenizer.batch_decode(__a)[0]
self.assertEqual(__a, __a)
| 36 |
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
import inspect
import unittest
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def A_ ( self : str ) -> Union[str, Any]:
"""simple docstring"""
try:
import diffusers # noqa: F401
except ImportError:
assert False
def A_ ( self : List[str] ) -> List[str]:
"""simple docstring"""
import diffusers
from diffusers.dependency_versions_table import deps
SCREAMING_SNAKE_CASE__ : str = 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":
SCREAMING_SNAKE_CASE__ : Any = "k-diffusion"
elif backend == "invisible_watermark":
SCREAMING_SNAKE_CASE__ : Dict = "invisible-watermark"
assert backend in deps, F'''{backend} is not in the deps table!'''
| 370 |
def _a ( SCREAMING_SNAKE_CASE__ : int = 50_00_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = set()
SCREAMING_SNAKE_CASE__ : Dict = int((limit - 24) ** (1 / 2) )
SCREAMING_SNAKE_CASE__ : Any = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , prime_square_limit + 1 , SCREAMING_SNAKE_CASE__ ) ) )
for primea in primes:
SCREAMING_SNAKE_CASE__ : Optional[int] = primea * primea
for primea in primes:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = primea * primea * primea
if square + cube >= limit - 16:
break
for primea in primes:
SCREAMING_SNAKE_CASE__ : List[str] = primea * primea * primea * primea
SCREAMING_SNAKE_CASE__ : Optional[int] = square + cube + tetr
if total >= limit:
break
ret.add(SCREAMING_SNAKE_CASE__ )
return len(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
print(f"{solution() = }")
| 191 | 0 |
"""simple docstring"""
def lowercase ( __snake_case : int , __snake_case : list[int] , __snake_case : int ):
def count_of_possible_combinations(__snake_case : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(__snake_case )
def lowercase ( __snake_case : int , __snake_case : list[int] , __snake_case : int ):
def count_of_possible_combinations_with_dp_array(
__snake_case : int , __snake_case : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
lowercase_ : Optional[int] = sum(
count_of_possible_combinations_with_dp_array(target - item , __snake_case )
for item in array )
lowercase_ : Union[str, Any] = answer
return answer
lowercase_ : Dict = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(__snake_case , __snake_case )
def lowercase ( __snake_case : int , __snake_case : list[int] , __snake_case : int ):
lowercase_ : List[Any] = [0] * (target + 1)
lowercase_ : Optional[Any] = 1
for i in range(1 , target + 1 ):
for j in range(__snake_case ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
__A : int = 3
__A : Union[str, Any] = 5
__A : List[str] = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 33 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : int = {
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}
class lowercase ( __UpperCAmelCase):
__lowerCAmelCase : Union[str, Any] = """transfo-xl"""
__lowerCAmelCase : Optional[Any] = ["""mems"""]
__lowerCAmelCase : List[str] = {
"""n_token""": """vocab_size""",
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : int , _lowerCamelCase : List[Any]=26_77_35 , _lowerCamelCase : Any=[2_00_00, 4_00_00, 20_00_00] , _lowerCamelCase : str=10_24 , _lowerCamelCase : Union[str, Any]=10_24 , _lowerCamelCase : Union[str, Any]=16 , _lowerCamelCase : int=64 , _lowerCamelCase : Optional[int]=40_96 , _lowerCamelCase : Optional[int]=4 , _lowerCamelCase : str=False , _lowerCamelCase : Union[str, Any]=18 , _lowerCamelCase : Optional[Any]=16_00 , _lowerCamelCase : Optional[int]=10_00 , _lowerCamelCase : Optional[int]=True , _lowerCamelCase : Any=True , _lowerCamelCase : Tuple=0 , _lowerCamelCase : List[Any]=-1 , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : List[str]=0.1 , _lowerCamelCase : List[Any]=0.0 , _lowerCamelCase : List[Any]=True , _lowerCamelCase : List[str]="normal" , _lowerCamelCase : int=0.01 , _lowerCamelCase : List[str]=0.01 , _lowerCamelCase : Optional[Any]=0.02 , _lowerCamelCase : int=1E-5 , _lowerCamelCase : int=0 , **_lowerCamelCase : Union[str, Any] , ):
"""simple docstring"""
A_ : Optional[Any] = vocab_size
A_ : str = []
self.cutoffs.extend(_lowerCamelCase )
if proj_share_all_but_first:
A_ : str = [False] + [True] * len(self.cutoffs )
else:
A_ : str = [False] + [False] * len(self.cutoffs )
A_ : Optional[Any] = d_model
A_ : Dict = d_embed
A_ : List[str] = d_head
A_ : List[Any] = d_inner
A_ : Dict = div_val
A_ : int = pre_lnorm
A_ : Optional[Any] = n_layer
A_ : List[Any] = n_head
A_ : List[Any] = mem_len
A_ : Dict = same_length
A_ : Optional[Any] = attn_type
A_ : Any = clamp_len
A_ : Dict = sample_softmax
A_ : List[Any] = adaptive
A_ : Union[str, Any] = dropout
A_ : List[Any] = dropatt
A_ : Any = untie_r
A_ : Optional[int] = init
A_ : int = init_range
A_ : List[Any] = proj_init_std
A_ : Union[str, Any] = init_std
A_ : List[Any] = layer_norm_epsilon
super().__init__(eos_token_id=_lowerCamelCase , **_lowerCamelCase )
@property
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def a_ ( self : Any , _lowerCamelCase : int ):
"""simple docstring"""
raise NotImplementedError(
F"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 167 | 0 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCamelCase :
'''simple docstring'''
def __init__( self , UpperCamelCase_ ):
lowercase_ :Optional[Any] = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
lowercase_ :str = len(UpperCamelCase_ ) - 1
def UpperCamelCase ( self , UpperCamelCase_ ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
lowercase_ :list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , UpperCamelCase_ ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(UpperCamelCase_ ) , 5 ) == 1
return output_values
def UpperCamelCase ( self , UpperCamelCase_ ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
lowercase_ :Optional[Any] = self.basis_function(UpperCamelCase_ )
lowercase_ :Union[str, Any] = 0.0
lowercase_ :Tuple = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def UpperCamelCase ( self , UpperCamelCase_ = 0.01 ):
from matplotlib import pyplot as plt # type: ignore
lowercase_ :list[float] = [] # x coordinates of points to plot
lowercase_ :list[float] = [] # y coordinates of points to plot
lowercase_ :Optional[Any] = 0.0
while t <= 1:
lowercase_ :Tuple = self.bezier_curve_function(UpperCamelCase_ )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
lowercase_ :List[str] = [i[0] for i in self.list_of_points]
lowercase_ :Optional[int] = [i[1] for i in self.list_of_points]
plt.plot(
UpperCamelCase_ , UpperCamelCase_ , color='''blue''' , label='''Curve of Degree ''' + str(self.degree ) , )
plt.scatter(UpperCamelCase_ , UpperCamelCase_ , color='''red''' , label='''Control Points''' )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 252 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class UpperCamelCase ( lowercase__ ):
'''simple docstring'''
lowercase : Optional[int] ="""decision_transformer"""
lowercase : Dict =["""past_key_values"""]
lowercase : Any ={
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , UpperCamelCase_=17 , UpperCamelCase_=4 , UpperCamelCase_=128 , UpperCamelCase_=4096 , UpperCamelCase_=True , UpperCamelCase_=1 , UpperCamelCase_=1024 , UpperCamelCase_=3 , UpperCamelCase_=1 , UpperCamelCase_=None , UpperCamelCase_="relu" , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=1E-5 , UpperCamelCase_=0.02 , UpperCamelCase_=True , UpperCamelCase_=True , UpperCamelCase_=5_0256 , UpperCamelCase_=5_0256 , UpperCamelCase_=False , UpperCamelCase_=False , **UpperCamelCase_ , ):
lowercase_ :Any = state_dim
lowercase_ :List[str] = act_dim
lowercase_ :List[str] = hidden_size
lowercase_ :int = max_ep_len
lowercase_ :List[str] = action_tanh
lowercase_ :Any = vocab_size
lowercase_ :List[Any] = n_positions
lowercase_ :List[str] = n_layer
lowercase_ :Optional[Any] = n_head
lowercase_ :int = n_inner
lowercase_ :List[str] = activation_function
lowercase_ :List[str] = resid_pdrop
lowercase_ :Dict = embd_pdrop
lowercase_ :List[Any] = attn_pdrop
lowercase_ :Union[str, Any] = layer_norm_epsilon
lowercase_ :List[str] = initializer_range
lowercase_ :Any = scale_attn_weights
lowercase_ :Union[str, Any] = use_cache
lowercase_ :Any = scale_attn_by_inverse_layer_idx
lowercase_ :Tuple = reorder_and_upcast_attn
lowercase_ :int = bos_token_id
lowercase_ :List[str] = eos_token_id
super().__init__(bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_ )
| 252 | 1 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
_lowerCamelCase : Optional[Any] = 'pt'
elif is_tf_available():
_lowerCamelCase : List[str] = 'tf'
else:
_lowerCamelCase : Tuple = 'jax'
class lowercase ( __UpperCAmelCase , unittest.TestCase):
__lowerCAmelCase : Tuple = ByTaTokenizer
__lowerCAmelCase : Optional[int] = False
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
super().setUp()
A_ : Tuple = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def a_ ( self : Any ):
"""simple docstring"""
return ByTaTokenizer.from_pretrained('''google/byt5-small''' )
def a_ ( self : List[Any] , **_lowerCamelCase : Dict ):
"""simple docstring"""
return self.tokenizer_class.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def a_ ( self : Optional[int] , _lowerCamelCase : List[Any] , _lowerCamelCase : List[str]=False , _lowerCamelCase : Optional[Any]=20 , _lowerCamelCase : Union[str, Any]=5 ):
"""simple docstring"""
A_ : int = []
for i in range(len(_lowerCamelCase ) ):
try:
A_ : Optional[int] = tokenizer.decode([i] , clean_up_tokenization_spaces=_lowerCamelCase )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
A_ : Dict = list(filter(lambda _lowerCamelCase : re.match(r'''^[ a-zA-Z]+$''' , t[1] ) , _lowerCamelCase ) )
A_ : Dict = list(filter(lambda _lowerCamelCase : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=_lowerCamelCase ) , _lowerCamelCase ) )
if max_length is not None and len(_lowerCamelCase ) > max_length:
A_ : List[str] = toks[:max_length]
if min_length is not None and len(_lowerCamelCase ) < min_length and len(_lowerCamelCase ) > 0:
while len(_lowerCamelCase ) < min_length:
A_ : int = toks + toks
# toks_str = [t[1] for t in toks]
A_ : int = [t[0] for t in toks]
# Ensure consistency
A_ : List[str] = tokenizer.decode(_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase )
if " " not in output_txt and len(_lowerCamelCase ) > 1:
A_ : Union[str, Any] = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=_lowerCamelCase )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=_lowerCamelCase )
)
if with_prefix_space:
A_ : Tuple = ''' ''' + output_txt
A_ : Any = tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
return output_txt, output_ids
def a_ ( self : Optional[int] ):
"""simple docstring"""
A_ : Any = self.ta_base_tokenizer
A_ : Any = tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>'''] )
A_ : Dict = tokenizer(['''hi''', '''I went to the gym''', ''''''] )
self.assertListEqual(batch_with_eos_added['''input_ids'''] , batch_without_eos_added['''input_ids'''] )
def a_ ( self : int ):
"""simple docstring"""
A_ : Dict = self.ta_base_tokenizer
A_ : Dict = '''Unicode €.'''
A_ : Dict = tokenizer(_lowerCamelCase )
A_ : str = [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
self.assertEqual(encoded['''input_ids'''] , _lowerCamelCase )
# decoding
A_ : Optional[Any] = tokenizer.decode(_lowerCamelCase )
self.assertEqual(_lowerCamelCase , '''Unicode €.</s>''' )
A_ : Optional[Any] = tokenizer('''e è é ê ë''' )
A_ : str = [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
self.assertEqual(encoded['''input_ids'''] , _lowerCamelCase )
# decoding
A_ : Union[str, Any] = tokenizer.decode(_lowerCamelCase )
self.assertEqual(_lowerCamelCase , '''e è é ê ë</s>''' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) ) , '''e è é ê ë</s>''' )
def a_ ( self : Optional[Any] ):
"""simple docstring"""
A_ : int = self.ta_base_tokenizer
A_ : str = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
# fmt: off
A_ : Optional[Any] = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
# fmt: on
A_ : Dict = tokenizer(_lowerCamelCase , padding=_lowerCamelCase , return_tensors=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
if FRAMEWORK != "jax":
A_ : Union[str, Any] = list(batch.input_ids.numpy()[0] )
else:
A_ : Optional[int] = list(batch.input_ids.tolist()[0] )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
self.assertEqual((2, 37) , batch.input_ids.shape )
self.assertEqual((2, 37) , batch.attention_mask.shape )
def a_ ( self : Optional[int] ):
"""simple docstring"""
A_ : Tuple = self.ta_base_tokenizer
A_ : Any = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
A_ : List[Any] = tokenizer(_lowerCamelCase , padding=_lowerCamelCase , return_tensors=_lowerCamelCase )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('''input_ids''' , _lowerCamelCase )
self.assertIn('''attention_mask''' , _lowerCamelCase )
self.assertNotIn('''decoder_input_ids''' , _lowerCamelCase )
self.assertNotIn('''decoder_attention_mask''' , _lowerCamelCase )
def a_ ( self : Optional[int] ):
"""simple docstring"""
A_ : Dict = self.ta_base_tokenizer
A_ : Any = [
'''Summary of the text.''',
'''Another summary.''',
]
A_ : Dict = tokenizer(
text_target=_lowerCamelCase , max_length=32 , padding='''max_length''' , truncation=_lowerCamelCase , return_tensors=_lowerCamelCase )
self.assertEqual(32 , targets['''input_ids'''].shape[1] )
def a_ ( self : str ):
"""simple docstring"""
A_ : int = self.ta_base_tokenizer
A_ : Dict = ['''A long paragraph for summarization. </s>''']
A_ : str = ['''Summary of the text. </s>''']
# fmt: off
A_ : Tuple = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
A_ : Optional[int] = [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
# fmt: on
A_ : Any = tokenizer(_lowerCamelCase , text_target=_lowerCamelCase )
self.assertEqual(_lowerCamelCase , batch['''input_ids'''][0] )
self.assertEqual(_lowerCamelCase , batch['''labels'''][0] )
def a_ ( self : Any ):
"""simple docstring"""
A_ : Tuple = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
A_ : Optional[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
A_ : List[str] = tempfile.mkdtemp()
A_ : Dict = ''' He is very happy, UNwant\u00E9d,running'''
A_ : Optional[Any] = tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
tokenizer.save_pretrained(_lowerCamelCase )
A_ : List[Any] = tokenizer.__class__.from_pretrained(_lowerCamelCase )
A_ : str = after_tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
shutil.rmtree(_lowerCamelCase )
A_ : Any = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
A_ : List[Any] = tempfile.mkdtemp()
A_ : Union[str, Any] = ''' He is very happy, UNwant\u00E9d,running'''
tokenizer.add_tokens(['''bim''', '''bambam'''] )
A_ : Union[str, Any] = tokenizer.additional_special_tokens
additional_special_tokens.append('''new_additional_special_token''' )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
A_ : List[Any] = tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
tokenizer.save_pretrained(_lowerCamelCase )
A_ : str = tokenizer.__class__.from_pretrained(_lowerCamelCase )
A_ : Optional[Any] = after_tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
self.assertIn('''new_additional_special_token''' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
A_ : Tuple = tokenizer.__class__.from_pretrained(_lowerCamelCase , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(_lowerCamelCase )
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
A_ : List[Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(_lowerCamelCase )
with open(os.path.join(_lowerCamelCase , '''special_tokens_map.json''' ) , encoding='''utf-8''' ) as json_file:
A_ : Optional[int] = json.load(_lowerCamelCase )
with open(os.path.join(_lowerCamelCase , '''tokenizer_config.json''' ) , encoding='''utf-8''' ) as json_file:
A_ : List[str] = json.load(_lowerCamelCase )
A_ : Optional[Any] = [F"""<extra_id_{i}>""" for i in range(1_25 )]
A_ : Dict = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
A_ : Optional[Any] = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
with open(os.path.join(_lowerCamelCase , '''special_tokens_map.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(_lowerCamelCase , _lowerCamelCase )
with open(os.path.join(_lowerCamelCase , '''tokenizer_config.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(_lowerCamelCase , _lowerCamelCase )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
A_ : List[Any] = tokenizer_class.from_pretrained(
_lowerCamelCase , )
self.assertIn(
'''an_additional_special_token''' , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
['''an_additional_special_token'''] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token'''] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
A_ : Union[str, Any] = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' , lstrip=_lowerCamelCase )]
A_ : List[Any] = tokenizer_class.from_pretrained(
_lowerCamelCase , additional_special_tokens=_lowerCamelCase , )
self.assertIn('''a_new_additional_special_token''' , tokenizer.additional_special_tokens )
self.assertEqual(
['''a_new_additional_special_token'''] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token'''] ) ) , )
def a_ ( self : Dict ):
"""simple docstring"""
A_ : str = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(_lowerCamelCase )
A_ : List[str] = tokenizer_class.from_pretrained(_lowerCamelCase )
self.assertTrue(tokenizer.decode([2_55] ) == '''''' )
def a_ ( self : int ):
"""simple docstring"""
pass
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
pass
def a_ ( self : str ):
"""simple docstring"""
pass
def a_ ( self : Optional[Any] ):
"""simple docstring"""
pass
def a_ ( self : Tuple ):
"""simple docstring"""
A_ : str = self.get_tokenizers(fast=_lowerCamelCase , do_lower_case=_lowerCamelCase )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
A_ : Tuple = ['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
A_ : int = tokenizer.convert_tokens_to_string(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Optional[int] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
A_ : str = [
'''bos_token''',
'''eos_token''',
'''unk_token''',
'''sep_token''',
'''pad_token''',
'''cls_token''',
'''mask_token''',
]
A_ : str = 0
A_ : Dict = tokenizer.convert_ids_to_tokens(
_lowerCamelCase , skip_special_tokens=_lowerCamelCase )
for attr in attributes_list:
setattr(_lowerCamelCase , attr + '''_id''' , _lowerCamelCase )
self.assertEqual(getattr(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(getattr(_lowerCamelCase , attr + '''_id''' ) , _lowerCamelCase )
setattr(_lowerCamelCase , attr + '''_id''' , _lowerCamelCase )
self.assertEqual(getattr(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(getattr(_lowerCamelCase , attr + '''_id''' ) , _lowerCamelCase )
setattr(_lowerCamelCase , '''additional_special_tokens_ids''' , [] )
self.assertListEqual(getattr(_lowerCamelCase , '''additional_special_tokens''' ) , [] )
self.assertListEqual(getattr(_lowerCamelCase , '''additional_special_tokens_ids''' ) , [] )
setattr(_lowerCamelCase , '''additional_special_tokens_ids''' , [token_id_to_test_setters] )
self.assertListEqual(getattr(_lowerCamelCase , '''additional_special_tokens''' ) , [token_to_test_setters] )
self.assertListEqual(getattr(_lowerCamelCase , '''additional_special_tokens_ids''' ) , [token_id_to_test_setters] )
| 167 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase ( __UpperCAmelCase):
__lowerCAmelCase : str = ["""image_processor""", """tokenizer"""]
__lowerCAmelCase : Optional[Any] = """OwlViTImageProcessor"""
__lowerCAmelCase : Any = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self : Union[str, Any] , _lowerCamelCase : str=None , _lowerCamelCase : Tuple=None , **_lowerCamelCase : List[Any] ):
"""simple docstring"""
A_ : Optional[Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , _lowerCamelCase , )
A_ : List[Any] = kwargs.pop('''feature_extractor''' )
A_ : Any = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(_lowerCamelCase , _lowerCamelCase )
def __call__( self : Optional[int] , _lowerCamelCase : List[Any]=None , _lowerCamelCase : Dict=None , _lowerCamelCase : int=None , _lowerCamelCase : str="max_length" , _lowerCamelCase : List[Any]="np" , **_lowerCamelCase : Optional[int] ):
"""simple docstring"""
if text is None and query_images is None and images is None:
raise ValueError(
'''You have to specify at least one text or query image or image. All three cannot be none.''' )
if text is not None:
if isinstance(_lowerCamelCase , _lowerCamelCase ) or (isinstance(_lowerCamelCase , _lowerCamelCase ) and not isinstance(text[0] , _lowerCamelCase )):
A_ : List[str] = [self.tokenizer(_lowerCamelCase , padding=_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )]
elif isinstance(_lowerCamelCase , _lowerCamelCase ) and isinstance(text[0] , _lowerCamelCase ):
A_ : Optional[int] = []
# Maximum number of queries across batch
A_ : Any = max([len(_lowerCamelCase ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(_lowerCamelCase ) != max_num_queries:
A_ : Optional[int] = t + [''' '''] * (max_num_queries - len(_lowerCamelCase ))
A_ : Tuple = self.tokenizer(_lowerCamelCase , padding=_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
encodings.append(_lowerCamelCase )
else:
raise TypeError('''Input text should be a string, a list of strings or a nested list of strings''' )
if return_tensors == "np":
A_ : Union[str, Any] = np.concatenate([encoding['''input_ids'''] for encoding in encodings] , axis=0 )
A_ : Dict = np.concatenate([encoding['''attention_mask'''] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
A_ : List[Any] = jnp.concatenate([encoding['''input_ids'''] for encoding in encodings] , axis=0 )
A_ : Any = jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
A_ : Any = torch.cat([encoding['''input_ids'''] for encoding in encodings] , dim=0 )
A_ : Union[str, Any] = torch.cat([encoding['''attention_mask'''] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
A_ : Tuple = tf.stack([encoding['''input_ids'''] for encoding in encodings] , axis=0 )
A_ : str = tf.stack([encoding['''attention_mask'''] for encoding in encodings] , axis=0 )
else:
raise ValueError('''Target return tensor type could not be returned''' )
A_ : Any = BatchEncoding()
A_ : Optional[Any] = input_ids
A_ : str = attention_mask
if query_images is not None:
A_ : Union[str, Any] = BatchEncoding()
A_ : Optional[Any] = self.image_processor(
_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase ).pixel_values
A_ : Dict = query_pixel_values
if images is not None:
A_ : int = self.image_processor(_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
if text is not None and images is not None:
A_ : str = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
A_ : Union[str, Any] = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**_lowerCamelCase ) , tensor_type=_lowerCamelCase )
def a_ ( self : Optional[Any] , *_lowerCamelCase : int , **_lowerCamelCase : Dict ):
"""simple docstring"""
return self.image_processor.post_process(*_lowerCamelCase , **_lowerCamelCase )
def a_ ( self : Optional[Any] , *_lowerCamelCase : Optional[Any] , **_lowerCamelCase : Dict ):
"""simple docstring"""
return self.image_processor.post_process_object_detection(*_lowerCamelCase , **_lowerCamelCase )
def a_ ( self : List[Any] , *_lowerCamelCase : List[str] , **_lowerCamelCase : Optional[int] ):
"""simple docstring"""
return self.image_processor.post_process_image_guided_detection(*_lowerCamelCase , **_lowerCamelCase )
def a_ ( self : str , *_lowerCamelCase : Tuple , **_lowerCamelCase : List[Any] ):
"""simple docstring"""
return self.tokenizer.batch_decode(*_lowerCamelCase , **_lowerCamelCase )
def a_ ( self : Dict , *_lowerCamelCase : Any , **_lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
return self.tokenizer.decode(*_lowerCamelCase , **_lowerCamelCase )
@property
def a_ ( self : List[str] ):
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , _lowerCamelCase , )
return self.image_processor_class
@property
def a_ ( self : Any ):
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , _lowerCamelCase , )
return self.image_processor
| 167 | 1 |
'''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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
"microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}
class _A ( __lowercase , __lowercase ):
lowercase__: str = '''resnet'''
lowercase__: Dict = ['''basic''', '''bottleneck''']
def __init__( self : Dict , __magic_name__ : int=3 , __magic_name__ : Optional[int]=64 , __magic_name__ : Optional[int]=[2_56, 5_12, 10_24, 20_48] , __magic_name__ : Optional[int]=[3, 4, 6, 3] , __magic_name__ : List[Any]="bottleneck" , __magic_name__ : int="relu" , __magic_name__ : Union[str, Any]=False , __magic_name__ : Tuple=None , __magic_name__ : Dict=None , **__magic_name__ : Any , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**__magic_name__ )
if layer_type not in self.layer_types:
raise ValueError(f'''layer_type={layer_type} is not one of {",".join(self.layer_types )}''' )
__snake_case : Tuple = num_channels
__snake_case : List[Any] = embedding_size
__snake_case : int = hidden_sizes
__snake_case : List[Any] = depths
__snake_case : int = layer_type
__snake_case : Optional[Any] = hidden_act
__snake_case : List[Any] = downsample_in_first_stage
__snake_case : Tuple = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(__magic_name__ ) + 1 )]
__snake_case , __snake_case : List[str] = get_aligned_output_features_output_indices(
out_features=__magic_name__ , out_indices=__magic_name__ , stage_names=self.stage_names )
class _A ( __lowercase ):
lowercase__: List[str] = version.parse('''1.11''' )
@property
def lowercase__ ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowercase__ ( self : str ) -> float:
"""simple docstring"""
return 1E-3
| 13 |
'''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
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class _A ( __lowercase ):
lowercase__: str = '''codegen'''
lowercase__: Optional[int] = {
'''max_position_embeddings''': '''n_positions''',
'''hidden_size''': '''n_embd''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Union[str, Any] , __magic_name__ : Optional[Any]=5_04_00 , __magic_name__ : Any=20_48 , __magic_name__ : List[str]=20_48 , __magic_name__ : Union[str, Any]=40_96 , __magic_name__ : Tuple=28 , __magic_name__ : Dict=16 , __magic_name__ : List[str]=64 , __magic_name__ : str=None , __magic_name__ : Tuple="gelu_new" , __magic_name__ : Tuple=0.0 , __magic_name__ : Tuple=0.0 , __magic_name__ : Dict=0.0 , __magic_name__ : Optional[Any]=1E-5 , __magic_name__ : int=0.02 , __magic_name__ : List[Any]=True , __magic_name__ : int=5_02_56 , __magic_name__ : int=5_02_56 , __magic_name__ : Any=False , **__magic_name__ : Optional[int] , ) -> int:
"""simple docstring"""
__snake_case : List[str] = vocab_size
__snake_case : Union[str, Any] = n_ctx
__snake_case : int = n_positions
__snake_case : str = n_embd
__snake_case : Dict = n_layer
__snake_case : List[Any] = n_head
__snake_case : Any = n_inner
__snake_case : str = rotary_dim
__snake_case : List[str] = activation_function
__snake_case : Tuple = resid_pdrop
__snake_case : Dict = embd_pdrop
__snake_case : int = attn_pdrop
__snake_case : Tuple = layer_norm_epsilon
__snake_case : Union[str, Any] = initializer_range
__snake_case : Optional[Any] = use_cache
__snake_case : Dict = bos_token_id
__snake_case : Union[str, Any] = eos_token_id
super().__init__(
bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , tie_word_embeddings=__magic_name__ , **__magic_name__ )
class _A ( __lowercase ):
def __init__( self : int , __magic_name__ : PretrainedConfig , __magic_name__ : str = "default" , __magic_name__ : List[PatchingSpec] = None , __magic_name__ : bool = False , ) -> Tuple:
"""simple docstring"""
super().__init__(__magic_name__ , task=__magic_name__ , patching_specs=__magic_name__ , use_past=__magic_name__ )
if not getattr(self._config , """pad_token_id""" , __magic_name__ ):
# TODO: how to do that better?
__snake_case : List[str] = 0
@property
def lowercase__ ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
__snake_case : Dict = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(__magic_name__ , direction="""inputs""" )
__snake_case : Optional[Any] = {0: """batch""", 1: """past_sequence + sequence"""}
else:
__snake_case : Union[str, Any] = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def lowercase__ ( self : Tuple ) -> int:
"""simple docstring"""
return self._config.n_layer
@property
def lowercase__ ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return self._config.n_head
def lowercase__ ( self : Dict , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
__snake_case : Tuple = super(__magic_name__ , self ).generate_dummy_inputs(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
# We need to order the input in the way they appears in the forward()
__snake_case : Union[str, Any] = 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 : str = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
__snake_case : Tuple = seqlen + 2
__snake_case : Union[str, Any] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__snake_case : List[str] = [
(torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) for _ in range(self.num_layers )
]
__snake_case : Optional[int] = common_inputs["""attention_mask"""]
if self.use_past:
__snake_case : Union[str, Any] = ordered_inputs["""attention_mask"""].dtype
__snake_case : Optional[Any] = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(__magic_name__ , __magic_name__ , dtype=__magic_name__ )] , dim=1 )
return ordered_inputs
@property
def lowercase__ ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return 13
| 13 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ : Union[str, Any] = {
'''configuration_blenderbot''': [
'''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlenderbotConfig''',
'''BlenderbotOnnxConfig''',
],
'''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : List[Any] = ['''BlenderbotTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : List[str] = [
'''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlenderbotForCausalLM''',
'''BlenderbotForConditionalGeneration''',
'''BlenderbotModel''',
'''BlenderbotPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : int = [
'''TFBlenderbotForConditionalGeneration''',
'''TFBlenderbotModel''',
'''TFBlenderbotPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : List[str] = [
'''FlaxBlenderbotForConditionalGeneration''',
'''FlaxBlenderbotModel''',
'''FlaxBlenderbotPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 143 | import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class __snake_case ( unittest.TestCase ):
def __a ( self , __UpperCamelCase ) -> Dict:
'''simple docstring'''
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] , model_result['ss'] ):
snake_case__ : Optional[int] = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(__UpperCamelCase )
def __a ( self ) -> Dict:
'''simple docstring'''
snake_case__ : List[str] = 'sshleifer/tiny-gpt2'
snake_case__ : str = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
snake_case__ : Optional[Any] = PyTorchBenchmark(__UpperCamelCase )
snake_case__ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __a ( self ) -> Tuple:
'''simple docstring'''
snake_case__ : List[Any] = 'sgugger/tiny-distilbert-classification'
snake_case__ : str = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , only_pretrain_model=__UpperCamelCase , )
snake_case__ : Dict = PyTorchBenchmark(__UpperCamelCase )
snake_case__ : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ : Dict = 'sshleifer/tiny-gpt2'
snake_case__ : List[str] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , torchscript=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
snake_case__ : Optional[Any] = PyTorchBenchmark(__UpperCamelCase )
snake_case__ : str = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == 'cpu' , 'Cant do half precision' )
def __a ( self ) -> int:
'''simple docstring'''
snake_case__ : Any = 'sshleifer/tiny-gpt2'
snake_case__ : Dict = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , fpaa=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
snake_case__ : Optional[int] = PyTorchBenchmark(__UpperCamelCase )
snake_case__ : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __a ( self ) -> List[Any]:
'''simple docstring'''
snake_case__ : Union[str, Any] = 'sshleifer/tiny-gpt2'
snake_case__ : Optional[int] = AutoConfig.from_pretrained(__UpperCamelCase )
# set architectures equal to `None`
snake_case__ : Union[str, Any] = None
snake_case__ : List[str] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
snake_case__ : List[Any] = PyTorchBenchmark(__UpperCamelCase , configs=[config] )
snake_case__ : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __a ( self ) -> Any:
'''simple docstring'''
snake_case__ : Optional[Any] = 'sshleifer/tiny-gpt2'
snake_case__ : str = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
snake_case__ : str = PyTorchBenchmark(__UpperCamelCase )
snake_case__ : Optional[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == 'cpu' , 'Can\'t do half precision' )
def __a ( self ) -> Tuple:
'''simple docstring'''
snake_case__ : List[str] = 'sshleifer/tiny-gpt2'
snake_case__ : Dict = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , fpaa=__UpperCamelCase , multi_process=__UpperCamelCase , )
snake_case__ : List[Any] = PyTorchBenchmark(__UpperCamelCase )
snake_case__ : int = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __a ( self ) -> int:
'''simple docstring'''
snake_case__ : Union[str, Any] = 'sshleifer/tiny-gpt2'
snake_case__ : Optional[Any] = AutoConfig.from_pretrained(__UpperCamelCase )
snake_case__ : str = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
snake_case__ : List[str] = PyTorchBenchmark(__UpperCamelCase , configs=[config] )
snake_case__ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __a ( self ) -> Dict:
'''simple docstring'''
snake_case__ : List[str] = 'sshleifer/tinier_bart'
snake_case__ : List[str] = AutoConfig.from_pretrained(__UpperCamelCase )
snake_case__ : Any = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
snake_case__ : int = PyTorchBenchmark(__UpperCamelCase , configs=[config] )
snake_case__ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def __a ( self ) -> int:
'''simple docstring'''
snake_case__ : Any = 'sshleifer/tiny-gpt2'
snake_case__ : Tuple = AutoConfig.from_pretrained(__UpperCamelCase )
snake_case__ : List[Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
snake_case__ : int = PyTorchBenchmark(__UpperCamelCase , configs=[config] )
snake_case__ : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __a ( self ) -> Tuple:
'''simple docstring'''
snake_case__ : Optional[int] = 'sshleifer/tinier_bart'
snake_case__ : Union[str, Any] = AutoConfig.from_pretrained(__UpperCamelCase )
snake_case__ : Union[str, Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__UpperCamelCase , )
snake_case__ : Union[str, Any] = PyTorchBenchmark(__UpperCamelCase , configs=[config] )
snake_case__ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def __a ( self ) -> Optional[int]:
'''simple docstring'''
snake_case__ : Union[str, Any] = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ : List[str] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , save_to_csv=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(__UpperCamelCase , 'inf_time.csv' ) , train_memory_csv_file=os.path.join(__UpperCamelCase , 'train_mem.csv' ) , inference_memory_csv_file=os.path.join(__UpperCamelCase , 'inf_mem.csv' ) , train_time_csv_file=os.path.join(__UpperCamelCase , 'train_time.csv' ) , env_info_csv_file=os.path.join(__UpperCamelCase , 'env.csv' ) , multi_process=__UpperCamelCase , )
snake_case__ : Union[str, Any] = PyTorchBenchmark(__UpperCamelCase )
benchmark.run()
self.assertTrue(Path(os.path.join(__UpperCamelCase , 'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__UpperCamelCase , 'train_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__UpperCamelCase , 'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__UpperCamelCase , 'train_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__UpperCamelCase , 'env.csv' ) ).exists() )
def __a ( self ) -> Tuple:
'''simple docstring'''
snake_case__ : Optional[int] = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(__UpperCamelCase ):
self.assertTrue(hasattr(__UpperCamelCase , 'sequential' ) )
self.assertTrue(hasattr(__UpperCamelCase , 'cumulative' ) )
self.assertTrue(hasattr(__UpperCamelCase , 'current' ) )
self.assertTrue(hasattr(__UpperCamelCase , 'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ : Tuple = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__UpperCamelCase , inference=__UpperCamelCase , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(__UpperCamelCase , 'log.txt' ) , log_print=__UpperCamelCase , trace_memory_line_by_line=__UpperCamelCase , multi_process=__UpperCamelCase , )
snake_case__ : int = PyTorchBenchmark(__UpperCamelCase )
snake_case__ : Optional[Any] = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(__UpperCamelCase , 'log.txt' ) ).exists() )
| 143 | 1 |
"""simple docstring"""
from __future__ import annotations
def __lowerCamelCase ( a_ : Optional[Any] , a_ : Optional[Any] , a_ : int , a_ : Dict ) -> Dict: # noqa: E741
while r - l > 1:
__SCREAMING_SNAKE_CASE :Union[str, Any] = (l + r) // 2
if v[m] >= key:
__SCREAMING_SNAKE_CASE :Any = m
else:
__SCREAMING_SNAKE_CASE :List[Any] = m # noqa: E741
return r
def __lowerCamelCase ( a_ : list[int] ) -> int:
if len(a_ ) == 0:
return 0
__SCREAMING_SNAKE_CASE :str = [0] * len(a_ )
__SCREAMING_SNAKE_CASE :Optional[Any] = 1
__SCREAMING_SNAKE_CASE :Optional[int] = v[0]
for i in range(1 , len(a_ ) ):
if v[i] < tail[0]:
__SCREAMING_SNAKE_CASE :List[Any] = v[i]
elif v[i] > tail[length - 1]:
__SCREAMING_SNAKE_CASE :List[str] = v[i]
length += 1
else:
__SCREAMING_SNAKE_CASE :List[Any] = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod() | 239 |
"""simple docstring"""
from __future__ import annotations
import math
def __lowerCamelCase ( a_ : int , a_ : int , a_ : bool , a_ : list[int] , a_ : float ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if len(a_ ) == 0:
raise ValueError('''Scores cannot be empty''' )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 , node_index * 2 , a_ , a_ , a_ ) , minimax(depth + 1 , node_index * 2 + 1 , a_ , a_ , a_ ) , )
return min(
minimax(depth + 1 , node_index * 2 , a_ , a_ , a_ ) , minimax(depth + 1 , node_index * 2 + 1 , a_ , a_ , a_ ) , )
def __lowerCamelCase ( ) -> None:
__SCREAMING_SNAKE_CASE :Dict = [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
__SCREAMING_SNAKE_CASE :Optional[int] = math.log(len(a_ ) , 2 )
print('''Optimal value : ''' , end='''''' )
print(minimax(0 , 0 , a_ , a_ , a_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main() | 239 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class _a :
def __init__( self: List[Any] , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: List[Any]=14 , UpperCamelCase_: Dict=7 , UpperCamelCase_: str=True , UpperCamelCase_: str=True , UpperCamelCase_: Tuple=False , UpperCamelCase_: List[Any]=True , UpperCamelCase_: int=99 , UpperCamelCase_: int=32 , UpperCamelCase_: str=4 , UpperCamelCase_: List[Any]=4 , UpperCamelCase_: List[Any]=4 , UpperCamelCase_: Optional[int]=37 , UpperCamelCase_: Any="gelu" , UpperCamelCase_: Optional[int]=0.1 , UpperCamelCase_: Any=0.1 , UpperCamelCase_: Tuple=512 , UpperCamelCase_: int=0.02 , ) -> Tuple:
"""simple docstring"""
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = seq_length
lowercase__ = is_training
lowercase__ = use_input_mask
lowercase__ = use_token_type_ids
lowercase__ = use_labels
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = rotary_dim
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = initializer_range
lowercase__ = None
lowercase__ = vocab_size - 1
lowercase__ = vocab_size - 1
lowercase__ = vocab_size - 1
def lowerCamelCase_ ( self: str ) -> str:
"""simple docstring"""
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ = None
if self.use_input_mask:
lowercase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=UpperCamelCase_ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def lowerCamelCase_ ( self: Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase_: Optional[int] , UpperCamelCase_: List[Any] , UpperCamelCase_: int , UpperCamelCase_: int ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = 20
lowercase__ = model_class_name(UpperCamelCase_ )
lowercase__ = model.init_cache(input_ids.shape[0] , UpperCamelCase_ )
lowercase__ = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
lowercase__ = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
lowercase__ = model(
input_ids[:, :-1] , attention_mask=UpperCamelCase_ , past_key_values=UpperCamelCase_ , position_ids=UpperCamelCase_ , )
lowercase__ = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
lowercase__ = model(
input_ids[:, -1:] , attention_mask=UpperCamelCase_ , past_key_values=outputs_cache.past_key_values , position_ids=UpperCamelCase_ , )
lowercase__ = model(UpperCamelCase_ )
lowercase__ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f'Max diff is {diff}' )
def lowerCamelCase_ ( self: Dict , UpperCamelCase_: List[Any] , UpperCamelCase_: Any , UpperCamelCase_: str , UpperCamelCase_: Tuple ) -> Any:
"""simple docstring"""
lowercase__ = 20
lowercase__ = model_class_name(UpperCamelCase_ )
lowercase__ = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
lowercase__ = model.init_cache(input_ids.shape[0] , UpperCamelCase_ )
lowercase__ = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
lowercase__ = model(
input_ids[:, :-1] , attention_mask=UpperCamelCase_ , past_key_values=UpperCamelCase_ , position_ids=UpperCamelCase_ , )
lowercase__ = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
lowercase__ = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=UpperCamelCase_ , position_ids=UpperCamelCase_ , )
lowercase__ = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ )
lowercase__ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f'Max diff is {diff}' )
@require_flax
class _a ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
_lowercase : List[Any] = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
_lowercase : List[Any] = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def lowerCamelCase_ ( self: Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase__ = FlaxGPTJModelTester(self )
def lowerCamelCase_ ( self: Any ) -> Tuple:
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase__ , lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
def lowerCamelCase_ ( self: int ) -> Optional[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase__ , lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
@tooslow
def lowerCamelCase_ ( self: Optional[int] ) -> List[str]:
"""simple docstring"""
lowercase__ = GPTaTokenizer.from_pretrained('''gpt2''' , pad_token='''<|endoftext|>''' , padding_side='''left''' )
lowercase__ = tokenizer(['''Hello this is a long string''', '''Hey'''] , return_tensors='''np''' , padding=UpperCamelCase_ , truncation=UpperCamelCase_ )
lowercase__ = FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''' )
lowercase__ = False
lowercase__ = model.config.eos_token_id
lowercase__ = jax.jit(model.generate )
lowercase__ = jit_generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , pad_token_id=tokenizer.pad_token_id ).sequences
lowercase__ = tokenizer.batch_decode(UpperCamelCase_ , skip_special_tokens=UpperCamelCase_ )
lowercase__ = [
'''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
'''Hey, I\'m a little late to the party. I\'m going to''',
]
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
@is_pt_flax_cross_test
def lowerCamelCase_ ( self: List[str] ) -> str:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
lowercase__ = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
lowercase__ = model_class.__name__[4:] # Skip the "Flax" at the beginning
lowercase__ = getattr(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ , lowercase__ = pt_inputs['''input_ids'''].shape
lowercase__ = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(UpperCamelCase_ ):
lowercase__ = 0
lowercase__ = 1
lowercase__ = 0
lowercase__ = 1
lowercase__ = pt_model_class(UpperCamelCase_ ).eval()
lowercase__ = model_class(UpperCamelCase_ , dtype=jnp.floataa )
lowercase__ = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , UpperCamelCase_ )
lowercase__ = fx_state
with torch.no_grad():
lowercase__ = pt_model(**UpperCamelCase_ ).to_tuple()
lowercase__ = fx_model(**UpperCamelCase_ ).to_tuple()
self.assertEqual(len(UpperCamelCase_ ) , len(UpperCamelCase_ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(UpperCamelCase_ , UpperCamelCase_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(UpperCamelCase_ )
lowercase__ = model_class.from_pretrained(UpperCamelCase_ , from_pt=UpperCamelCase_ )
lowercase__ = fx_model_loaded(**UpperCamelCase_ ).to_tuple()
self.assertEqual(
len(UpperCamelCase_ ) , len(UpperCamelCase_ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output_loaded, pt_output in zip(UpperCamelCase_ , UpperCamelCase_ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@is_pt_flax_cross_test
def lowerCamelCase_ ( self: Dict ) -> str:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
lowercase__ = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
lowercase__ = model_class.__name__[4:] # Skip the "Flax" at the beginning
lowercase__ = getattr(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = pt_model_class(UpperCamelCase_ ).eval()
lowercase__ = model_class(UpperCamelCase_ , dtype=jnp.floataa )
lowercase__ = load_flax_weights_in_pytorch_model(UpperCamelCase_ , fx_model.params )
lowercase__ , lowercase__ = pt_inputs['''input_ids'''].shape
lowercase__ = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(UpperCamelCase_ ):
lowercase__ = 0
lowercase__ = 1
lowercase__ = 0
lowercase__ = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
lowercase__ = pt_model(**UpperCamelCase_ ).to_tuple()
lowercase__ = fx_model(**UpperCamelCase_ ).to_tuple()
self.assertEqual(len(UpperCamelCase_ ) , len(UpperCamelCase_ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(UpperCamelCase_ , UpperCamelCase_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(UpperCamelCase_ )
lowercase__ = pt_model_class.from_pretrained(UpperCamelCase_ , from_flax=UpperCamelCase_ )
with torch.no_grad():
lowercase__ = pt_model_loaded(**UpperCamelCase_ ).to_tuple()
self.assertEqual(
len(UpperCamelCase_ ) , len(UpperCamelCase_ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(UpperCamelCase_ , UpperCamelCase_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@tooslow
def lowerCamelCase_ ( self: Optional[Any] ) -> int:
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase__ = model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''' )
lowercase__ = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCamelCase_ )
| 110 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = 0
lowercase__ = len(SCREAMING_SNAKE_CASE ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
lowercase__ = i + 1
else:
lowercase__ = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"""{two_pointer([2, 7, 11, 15], 9) = }""")
| 110 | 1 |
from math import factorial, radians
def _UpperCamelCase (a__ :float , a__ :int = 18 , a__ :int = 10 ):
"""simple docstring"""
UpperCamelCase__ = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
UpperCamelCase__ = radians(a__ )
UpperCamelCase__ = angle_in_radians
UpperCamelCase__ = 3
UpperCamelCase__ = -1
for _ in range(a__ ):
result += (b * (angle_in_radians**a)) / factorial(a__ )
UpperCamelCase__ = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(a__ , a__ )
if __name__ == "__main__":
__import__("doctest").testmod()
| 87 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
"SwiftFormerOnnxConfig",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"SwiftFormerForImageClassification",
"SwiftFormerModel",
"SwiftFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 87 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
A__: Any = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class A__ ( UpperCAmelCase__ , unittest.TestCase ):
__UpperCamelCase : List[str] = ReformerTokenizer
__UpperCamelCase : Any = ReformerTokenizerFast
__UpperCamelCase : str = True
__UpperCamelCase : Dict = False
__UpperCamelCase : Any = True
def __UpperCAmelCase ( self :Any ) -> Any:
'''simple docstring'''
super().setUp()
_a : Union[str, Any] =ReformerTokenizer(SCREAMING_SNAKE_CASE , keep_accents=SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCAmelCase ( self :Optional[int] ) -> Dict:
'''simple docstring'''
_a : List[str] ="""<s>"""
_a : List[str] =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self :Dict ) -> Optional[Any]:
'''simple docstring'''
_a : Union[str, Any] =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , 1_0_0_0 )
def __UpperCAmelCase ( self :Union[str, Any] ) -> Tuple:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_0 )
def __UpperCAmelCase ( self :Tuple ) -> Optional[Any]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
_a : Tuple =self.get_tokenizer()
_a : List[str] =self.get_rust_tokenizer()
_a : List[Any] ="""I was born in 92000, and this is falsé."""
_a : Union[str, Any] =tokenizer.tokenize(SCREAMING_SNAKE_CASE )
_a : str =rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
_a : Dict =tokenizer.encode(SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE )
_a : Union[str, Any] =rust_tokenizer.encode(SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
_a : str =self.get_rust_tokenizer()
_a : int =tokenizer.encode(SCREAMING_SNAKE_CASE )
_a : Optional[int] =rust_tokenizer.encode(SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self :int , SCREAMING_SNAKE_CASE :str=1_5 ) -> Union[str, Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_a : Dict =self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
# Simple input
_a : List[Any] ="""This is a simple input"""
_a : Union[str, Any] =["""This is a simple input 1""", """This is a simple input 2"""]
_a : Tuple =("""This is a simple input""", """This is a pair""")
_a : str =[
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
self.assertRaises(SCREAMING_SNAKE_CASE , tokenizer_r.encode , SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE , padding="""max_length""" )
# Simple input
self.assertRaises(SCREAMING_SNAKE_CASE , tokenizer_r.encode_plus , SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE , padding="""max_length""" )
# Simple input
self.assertRaises(
SCREAMING_SNAKE_CASE , tokenizer_r.batch_encode_plus , SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE , padding="""max_length""" , )
# Pair input
self.assertRaises(SCREAMING_SNAKE_CASE , tokenizer_r.encode , SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE , padding="""max_length""" )
# Pair input
self.assertRaises(SCREAMING_SNAKE_CASE , tokenizer_r.encode_plus , SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE , padding="""max_length""" )
# Pair input
self.assertRaises(
SCREAMING_SNAKE_CASE , tokenizer_r.batch_encode_plus , SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE , padding="""max_length""" , )
def __UpperCAmelCase ( self :Optional[int] ) -> Optional[int]:
'''simple docstring'''
pass
def __UpperCAmelCase ( self :Any ) -> Union[str, Any]:
'''simple docstring'''
_a : List[str] =ReformerTokenizer(SCREAMING_SNAKE_CASE , keep_accents=SCREAMING_SNAKE_CASE )
_a : Union[str, Any] =tokenizer.tokenize("""This is a test""" )
self.assertListEqual(SCREAMING_SNAKE_CASE , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE ) , [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2] , )
_a : Tuple =tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
_a : List[Any] =tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE )
self.assertListEqual(
SCREAMING_SNAKE_CASE , [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4] , )
_a : Union[str, Any] =tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE )
self.assertListEqual(
SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
@cached_property
def __UpperCAmelCase ( self :Optional[int] ) -> List[str]:
'''simple docstring'''
return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment""" )
@slow
def __UpperCAmelCase ( self :Any ) -> int:
'''simple docstring'''
_a : Any ="""Hello World!"""
_a : List[str] =[1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]
self.assertListEqual(SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE ) )
@slow
def __UpperCAmelCase ( self :Optional[Any] ) -> List[Any]:
'''simple docstring'''
_a : Tuple =(
"""This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
""" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
)
_a : Optional[Any] =[
1_0_8,
2_6_5,
2_4,
1_1_1,
4,
2_5_8,
1_5_6,
3_5,
2_8,
2_7_5,
3,
2_5_9,
2_9_7,
2_6_0,
8_4,
4,
3_5,
1_1_0,
4_4,
8,
2_5_9,
9_1,
2_6_8,
2_1,
1_1,
2_0_9,
2_7_4,
1_0_9,
2_6_6,
2_7_7,
1_1_7,
8_6,
9_3,
3_1_5,
2_5_8,
2_7_8,
2_5_8,
2_7_7,
2_5_8,
0,
2_5_8,
2_8_8,
2_5_8,
3_1_9,
2_5_8,
0,
2_5_8,
0,
2_5_8,
0,
2_5_8,
0,
2_5_8,
2_8_7,
2_5_8,
3_1_5,
2_5_8,
2_8_9,
2_5_8,
2_7_8,
9_9,
2_6_9,
2_6_6,
2_6_2,
8,
2_5_9,
2_4_1,
4,
2_1_7,
2_3_0,
2_6_8,
2_6_6,
5_5,
1_6_8,
1_0_6,
7_5,
1_9_3,
2_6_6,
2_2_3,
2_7,
4_9,
2_6,
2_8_2,
2_5,
2_6_4,
2_9_9,
1_9,
2_6,
0,
2_5_8,
2_7_7,
1_1_7,
8_6,
9_3,
1_7_6,
1_8_3,
2_7_0,
1_1,
2_6_2,
4_2,
6_1,
2_6_5,
]
self.assertListEqual(SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE ) )
@require_torch
@slow
def __UpperCAmelCase ( self :str ) -> List[str]:
'''simple docstring'''
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
_a : Dict =list(self.big_tokenizer.get_vocab().keys() )[:1_0]
_a : Dict =""" """.join(SCREAMING_SNAKE_CASE )
_a : Optional[int] =self.big_tokenizer.encode_plus(SCREAMING_SNAKE_CASE , return_tensors="""pt""" )
_a : Optional[int] =self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors="""pt""" )
_a : str =ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
_a : Union[str, Any] =encoded_sequence["""input_ids"""].shape
_a : List[str] =ReformerModel(SCREAMING_SNAKE_CASE )
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**SCREAMING_SNAKE_CASE )
model(**SCREAMING_SNAKE_CASE )
@slow
def __UpperCAmelCase ( self :int ) -> str:
'''simple docstring'''
# fmt: off
_a : Any ={"""input_ids""": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
_a : str =[
"""This is a very simple sentence.""",
"""The quick brown fox jumps over the lazy dog.""",
]
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE , model_name="""google/reformer-crime-and-punishment""" , revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a""" , padding=SCREAMING_SNAKE_CASE , sequences=SCREAMING_SNAKE_CASE , )
| 276 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class A__ ( UpperCAmelCase__ ):
__UpperCamelCase : str
__UpperCamelCase : int
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> list[str]:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
return [s[i:] + s[:i] for i in range(len(_UpperCAmelCase ) )]
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> BWTTransformDict:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
if not s:
raise ValueError("""The parameter s must not be empty.""" )
_a : List[Any] =all_rotations(_UpperCAmelCase )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
_a : BWTTransformDict ={
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(_UpperCAmelCase ),
}
return response
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ,_UpperCAmelCase : int ) -> str:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise TypeError("""The parameter bwt_string type must be str.""" )
if not bwt_string:
raise ValueError("""The parameter bwt_string must not be empty.""" )
try:
_a : List[str] =int(_UpperCAmelCase )
except ValueError:
raise TypeError(
"""The parameter idx_original_string type must be int or passive"""
""" of cast to int.""" )
if idx_original_string < 0:
raise ValueError("""The parameter idx_original_string must not be lower than 0.""" )
if idx_original_string >= len(_UpperCAmelCase ):
raise ValueError(
"""The parameter idx_original_string must be lower than""" """ len(bwt_string).""" )
_a : Optional[int] =[""""""] * len(_UpperCAmelCase )
for _ in range(len(_UpperCAmelCase ) ):
for i in range(len(_UpperCAmelCase ) ):
_a : int =bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
A__: Any = '''Provide a string that I will generate its BWT transform: '''
A__: Union[str, Any] = input(entry_msg).strip()
A__: Optional[int] = bwt_transform(s)
print(
F"Burrows Wheeler transform for string '{s}' results "
F"in '{result['bwt_string']}'"
)
A__: Union[str, Any] = reverse_bwt(result['''bwt_string'''], result['''idx_original_string'''])
print(
F"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
F"we get original string '{original_string}'"
)
| 276 | 1 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
@require_torch
def _UpperCamelCase ( self : List[str] ) -> Dict:
_UpperCamelCase = pipeline(
task='''zero-shot-audio-classification''' , model='''hf-internal-testing/tiny-clap-htsat-unfused''' )
_UpperCamelCase = load_dataset('''ashraq/esc50''' )
_UpperCamelCase = dataset['train']['audio'][-1]['array']
_UpperCamelCase = audio_classifier(__UpperCamelCase , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(__UpperCamelCase ) , [{'''score''': 0.5_0_1, '''label''': '''Sound of a dog'''}, {'''score''': 0.4_9_9, '''label''': '''Sound of vaccum cleaner'''}] , )
@unittest.skip('''No models are available in TF''' )
def _UpperCamelCase ( self : int ) -> Optional[int]:
pass
@slow
@require_torch
def _UpperCamelCase ( self : int ) -> Union[str, Any]:
_UpperCamelCase = pipeline(
task='''zero-shot-audio-classification''' , model='''laion/clap-htsat-unfused''' , )
# This is an audio of a dog
_UpperCamelCase = load_dataset('''ashraq/esc50''' )
_UpperCamelCase = dataset['train']['audio'][-1]['array']
_UpperCamelCase = audio_classifier(__UpperCamelCase , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(__UpperCamelCase ) , [
{'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
{'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
] , )
_UpperCamelCase = audio_classifier([audio] * 5 , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(__UpperCamelCase ) , [
[
{'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
{'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
],
]
* 5 , )
_UpperCamelCase = audio_classifier(
[audio] * 5 , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] , batch_size=5 )
self.assertEqual(
nested_simplify(__UpperCamelCase ) , [
[
{'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
{'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
],
]
* 5 , )
@unittest.skip('''No models are available in TF''' )
def _UpperCamelCase ( self : str ) -> int:
pass
| 367 | """simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
def lowercase ( a__ : str ) -> int:
_UpperCamelCase = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
_UpperCamelCase = [144, 192, 240]
_UpperCamelCase = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
_UpperCamelCase = [96, 120, 144]
_UpperCamelCase = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
_UpperCamelCase = [64, 80, 96]
_UpperCamelCase = [16, 16, 24, 48, 64, 80, 320]
_UpperCamelCase = 0.05
_UpperCamelCase = 2.0
if mobilevit_name.startswith('''deeplabv3_''' ):
_UpperCamelCase = 512
_UpperCamelCase = 16
_UpperCamelCase = 21
_UpperCamelCase = '''pascal-voc-id2label.json'''
else:
_UpperCamelCase = 1000
_UpperCamelCase = '''imagenet-1k-id2label.json'''
_UpperCamelCase = '''huggingface/label-files'''
_UpperCamelCase = json.load(open(hf_hub_download(a__ , a__ , repo_type='''dataset''' ) , '''r''' ) )
_UpperCamelCase = {int(a__ ): v for k, v in idalabel.items()}
_UpperCamelCase = idalabel
_UpperCamelCase = {v: k for k, v in idalabel.items()}
return config
def lowercase ( a__ : Union[str, Any] , a__ : Optional[int]=False ) -> int:
for i in range(1 , 6 ):
if F'''layer_{i}.''' in name:
_UpperCamelCase = name.replace(F'''layer_{i}.''' , F'''encoder.layer.{i - 1}.''' )
if "conv_1." in name:
_UpperCamelCase = name.replace('''conv_1.''' , '''conv_stem.''' )
if ".block." in name:
_UpperCamelCase = name.replace('''.block.''' , '''.''' )
if "exp_1x1" in name:
_UpperCamelCase = name.replace('''exp_1x1''' , '''expand_1x1''' )
if "red_1x1" in name:
_UpperCamelCase = name.replace('''red_1x1''' , '''reduce_1x1''' )
if ".local_rep.conv_3x3." in name:
_UpperCamelCase = name.replace('''.local_rep.conv_3x3.''' , '''.conv_kxk.''' )
if ".local_rep.conv_1x1." in name:
_UpperCamelCase = name.replace('''.local_rep.conv_1x1.''' , '''.conv_1x1.''' )
if ".norm." in name:
_UpperCamelCase = name.replace('''.norm.''' , '''.normalization.''' )
if ".conv." in name:
_UpperCamelCase = name.replace('''.conv.''' , '''.convolution.''' )
if ".conv_proj." in name:
_UpperCamelCase = name.replace('''.conv_proj.''' , '''.conv_projection.''' )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F'''.{i}.{j}.''' in name:
_UpperCamelCase = name.replace(F'''.{i}.{j}.''' , F'''.{i}.layer.{j}.''' )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F'''.{i}.{j}.''' in name:
_UpperCamelCase = name.replace(F'''.{i}.{j}.''' , F'''.{i}.''' )
if "expand_1x1" in name:
_UpperCamelCase = name.replace('''expand_1x1''' , '''downsampling_layer.expand_1x1''' )
if "conv_3x3" in name:
_UpperCamelCase = name.replace('''conv_3x3''' , '''downsampling_layer.conv_3x3''' )
if "reduce_1x1" in name:
_UpperCamelCase = name.replace('''reduce_1x1''' , '''downsampling_layer.reduce_1x1''' )
for i in range(2 , 5 ):
if F'''.global_rep.{i}.weight''' in name:
_UpperCamelCase = name.replace(F'''.global_rep.{i}.weight''' , '''.layernorm.weight''' )
if F'''.global_rep.{i}.bias''' in name:
_UpperCamelCase = name.replace(F'''.global_rep.{i}.bias''' , '''.layernorm.bias''' )
if ".global_rep." in name:
_UpperCamelCase = name.replace('''.global_rep.''' , '''.transformer.''' )
if ".pre_norm_mha.0." in name:
_UpperCamelCase = name.replace('''.pre_norm_mha.0.''' , '''.layernorm_before.''' )
if ".pre_norm_mha.1.out_proj." in name:
_UpperCamelCase = name.replace('''.pre_norm_mha.1.out_proj.''' , '''.attention.output.dense.''' )
if ".pre_norm_ffn.0." in name:
_UpperCamelCase = name.replace('''.pre_norm_ffn.0.''' , '''.layernorm_after.''' )
if ".pre_norm_ffn.1." in name:
_UpperCamelCase = name.replace('''.pre_norm_ffn.1.''' , '''.intermediate.dense.''' )
if ".pre_norm_ffn.4." in name:
_UpperCamelCase = name.replace('''.pre_norm_ffn.4.''' , '''.output.dense.''' )
if ".transformer." in name:
_UpperCamelCase = name.replace('''.transformer.''' , '''.transformer.layer.''' )
if ".aspp_layer." in name:
_UpperCamelCase = name.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in name:
_UpperCamelCase = name.replace('''.aspp_pool.''' , '''.''' )
if "seg_head." in name:
_UpperCamelCase = name.replace('''seg_head.''' , '''segmentation_head.''' )
if "segmentation_head.classifier.classifier." in name:
_UpperCamelCase = name.replace('''segmentation_head.classifier.classifier.''' , '''segmentation_head.classifier.''' )
if "classifier.fc." in name:
_UpperCamelCase = name.replace('''classifier.fc.''' , '''classifier.''' )
elif (not base_model) and ("segmentation_head." not in name):
_UpperCamelCase = '''mobilevit.''' + name
return name
def lowercase ( a__ : Union[str, Any] , a__ : List[Any] , a__ : Tuple=False ) -> Optional[Any]:
if base_model:
_UpperCamelCase = ''''''
else:
_UpperCamelCase = '''mobilevit.'''
for key in orig_state_dict.copy().keys():
_UpperCamelCase = orig_state_dict.pop(a__ )
if key[:8] == "encoder.":
_UpperCamelCase = key[8:]
if "qkv" in key:
_UpperCamelCase = key.split('''.''' )
_UpperCamelCase = int(key_split[0][6:] ) - 1
_UpperCamelCase = int(key_split[3] )
_UpperCamelCase = model.get_submodule(F'''{model_prefix}encoder.layer.{layer_num}''' )
_UpperCamelCase = layer.transformer.layer[transformer_num].attention.attention.all_head_size
_UpperCamelCase = (
F'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
)
if "weight" in key:
_UpperCamelCase = val[:dim, :]
_UpperCamelCase = val[dim : dim * 2, :]
_UpperCamelCase = val[-dim:, :]
else:
_UpperCamelCase = val[:dim]
_UpperCamelCase = val[dim : dim * 2]
_UpperCamelCase = val[-dim:]
else:
_UpperCamelCase = val
return orig_state_dict
def lowercase ( ) -> Dict:
_UpperCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_UpperCamelCase = Image.open(requests.get(a__ , stream=a__ ).raw )
return im
@torch.no_grad()
def lowercase ( a__ : Dict , a__ : Optional[int] , a__ : Any , a__ : Tuple=False ) -> Any:
_UpperCamelCase = get_mobilevit_config(a__ )
# load original state_dict
_UpperCamelCase = torch.load(a__ , map_location='''cpu''' )
# load 🤗 model
if mobilevit_name.startswith('''deeplabv3_''' ):
_UpperCamelCase = MobileViTForSemanticSegmentation(a__ ).eval()
else:
_UpperCamelCase = MobileViTForImageClassification(a__ ).eval()
_UpperCamelCase = convert_state_dict(a__ , a__ )
model.load_state_dict(a__ )
# Check outputs on an image, prepared by MobileViTImageProcessor
_UpperCamelCase = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
_UpperCamelCase = image_processor(images=prepare_img() , return_tensors='''pt''' )
_UpperCamelCase = model(**a__ )
_UpperCamelCase = outputs.logits
if mobilevit_name.startswith('''deeplabv3_''' ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
_UpperCamelCase = torch.tensor(
[
[[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
[[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
[[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
_UpperCamelCase = torch.tensor(
[
[[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
[[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
[[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
_UpperCamelCase = torch.tensor(
[
[[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
[[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
[[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
] )
else:
raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}''' )
assert torch.allclose(logits[0, :3, :3, :3] , a__ , atol=1e-4 )
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
_UpperCamelCase = torch.tensor([-0.9866, 0.2392, -1.1241] )
elif mobilevit_name == "mobilevit_xs":
_UpperCamelCase = torch.tensor([-2.4761, -0.9399, -1.9587] )
elif mobilevit_name == "mobilevit_xxs":
_UpperCamelCase = torch.tensor([-1.9364, -1.2327, -0.4653] )
else:
raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}''' )
assert torch.allclose(logits[0, :3] , a__ , atol=1e-4 )
Path(a__ ).mkdir(exist_ok=a__ )
print(F'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(a__ )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(a__ )
if push_to_hub:
_UpperCamelCase = {
'''mobilevit_s''': '''mobilevit-small''',
'''mobilevit_xs''': '''mobilevit-x-small''',
'''mobilevit_xxs''': '''mobilevit-xx-small''',
'''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
'''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
'''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
}
print('''Pushing to the hub...''' )
_UpperCamelCase = model_mapping[mobilevit_name]
image_processor.push_to_hub(a__ , organization='''apple''' )
model.push_to_hub(a__ , organization='''apple''' )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--mobilevit_name""",
default="""mobilevit_s""",
type=str,
help=(
"""Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
""" 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
),
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
UpperCAmelCase = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 54 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A_ ( lowerCAmelCase_ ):
@staticmethod
@abstractmethod
def lowercase ( snake_case_ : ArgumentParser ):
raise NotImplementedError()
@abstractmethod
def lowercase ( self : Optional[Any] ):
raise NotImplementedError()
| 22 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
A__ = ['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']
class a ( __lowerCamelCase ):
def __init__( self :str ,__lowercase :Dict ,__lowercase :str ,__lowercase :Any=None ,__lowercase :Union[str, Any]=1 ):
snake_case__ : Optional[int] = tokenizer
snake_case__ : List[Any] = dataset
snake_case__ : Any = len(__lowercase ) if n_tasks is None else n_tasks
snake_case__ : List[Any] = n_copies
def __iter__( self :Tuple ):
snake_case__ : Union[str, Any] = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip() )
snake_case__ : Optional[int] = self.tokenizer(__lowercase ,padding=__lowercase ,return_tensors='''pt''' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class a ( __lowerCamelCase ):
def __init__( self :Union[str, Any] ,__lowercase :List[Any] ,__lowercase :int ,__lowercase :List[str] ):
snake_case__ : List[str] = start_length
snake_case__ : Any = eof_strings
snake_case__ : Optional[Any] = tokenizer
def __call__( self :str ,__lowercase :str ,__lowercase :Any ,**__lowercase :Optional[int] ):
snake_case__ : Tuple = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
snake_case__ : Union[str, Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(__lowercase )
def _lowerCAmelCase ( __lowerCAmelCase ) -> List[Any]:
"""simple docstring"""
snake_case__ : List[Any] = re.split('''(%s)''' % '''|'''.join(__lowerCAmelCase ) , __lowerCAmelCase )
# last string should be ""
return "".join(string_list[:-2] )
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=20 , **__lowerCAmelCase ) -> List[Any]:
"""simple docstring"""
snake_case__ : Optional[int] = defaultdict(__lowerCAmelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(__lowerCAmelCase ) ):
with torch.no_grad():
snake_case__ : List[Any] = batch['''ids'''].shape[-1]
snake_case__ : str = accelerator.unwrap_model(__lowerCAmelCase ).generate(
input_ids=batch['''ids'''][:, : batch['''input_len''']] , num_return_sequences=__lowerCAmelCase , **__lowerCAmelCase )
# each task is generated batch_size times
snake_case__ : str = batch['''task_id'''].repeat(__lowerCAmelCase )
snake_case__ : Optional[int] = accelerator.pad_across_processes(
__lowerCAmelCase , dim=1 , pad_index=tokenizer.pad_token_id )
snake_case__ , snake_case__ : Any = accelerator.gather((generated_tokens, generated_tasks) )
snake_case__ : Union[str, Any] = generated_tokens.cpu().numpy()
snake_case__ : Optional[Any] = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(__lowerCAmelCase , __lowerCAmelCase ):
gen_token_dict[task].append(__lowerCAmelCase )
snake_case__ : List[Any] = [[] for _ in range(__lowerCAmelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
snake_case__ : int = tokenizer.decode(__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase )
code_gens[task].append(remove_last_block(__lowerCAmelCase ) )
return code_gens
def _lowerCAmelCase ( ) -> Optional[int]:
"""simple docstring"""
snake_case__ : List[str] = HfArgumentParser(__lowerCAmelCase )
snake_case__ : Optional[Any] = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
snake_case__ : Union[str, Any] = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
snake_case__ : Union[str, Any] = '''false'''
if args.num_workers is None:
snake_case__ : Union[str, Any] = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
snake_case__ : Dict = Accelerator()
set_seed(args.seed , device_specific=__lowerCAmelCase )
# Load model and tokenizer
snake_case__ : str = AutoTokenizer.from_pretrained(args.model_ckpt )
snake_case__ : List[str] = tokenizer.eos_token
snake_case__ : Optional[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
snake_case__ : List[Any] = {
'''do_sample''': args.do_sample,
'''temperature''': args.temperature,
'''max_new_tokens''': args.max_new_tokens,
'''top_p''': args.top_p,
'''top_k''': args.top_k,
'''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0 , __lowerCAmelCase , __lowerCAmelCase )] ),
}
# Load evaluation dataset and metric
snake_case__ : List[str] = load_dataset('''openai_humaneval''' )
snake_case__ : str = load_metric('''code_eval''' )
snake_case__ : List[Any] = args.num_tasks if args.num_tasks is not None else len(human_eval['''test'''] )
snake_case__ : str = args.n_samples // args.batch_size
snake_case__ : Tuple = TokenizedDataset(__lowerCAmelCase , human_eval['''test'''] , n_copies=__lowerCAmelCase , n_tasks=__lowerCAmelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
snake_case__ : Tuple = DataLoader(__lowerCAmelCase , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
snake_case__ : Optional[Any] = code_eval_metric.compute(references=[''''''] , predictions=[['''''']] )
except ValueError as exception:
print(
'''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
''' flag to enable code evaluation.''' )
raise exception
snake_case__ , snake_case__ : Optional[Any] = accelerator.prepare(__lowerCAmelCase , __lowerCAmelCase )
snake_case__ : Dict = complete_code(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , n_tasks=__lowerCAmelCase , batch_size=args.batch_size , **__lowerCAmelCase , )
if accelerator.is_main_process:
snake_case__ : Optional[Any] = []
for task in tqdm(range(__lowerCAmelCase ) ):
snake_case__ : str = human_eval['''test'''][task]['''test''']
snake_case__ : int = f"""check({human_eval['test'][task]['entry_point']})"""
references.append('''\n''' + test_func + '''\n''' + entry_point )
# Evaluate completions with "code_eval" metric
snake_case__ , snake_case__ : Any = code_eval_metric.compute(
references=__lowerCAmelCase , predictions=__lowerCAmelCase , num_workers=args.num_workers )
print(f"""Results: {pass_at_k}""" )
# Save results to json file
with open(args.output_file , '''w''' ) as fp:
json.dump(__lowerCAmelCase , __lowerCAmelCase )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 230 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
_A : str = logging.get_logger(__name__)
def _a ( UpperCAmelCase ) -> List[List[ImageInput]]:
"""simple docstring"""
if isinstance(UpperCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(UpperCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(UpperCAmelCase ):
return [[videos]]
raise ValueError(f"Could not make batched video from {videos}" )
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
_UpperCAmelCase : Optional[int] = ["pixel_values"]
def __init__( self : int , A : bool = True , A : Dict[str, int] = None , A : PILImageResampling = PILImageResampling.BILINEAR , A : bool = True , A : Dict[str, int] = None , A : bool = True , A : Union[int, float] = 1 / 2_5_5 , A : bool = True , A : bool = True , A : Optional[Union[float, List[float]]] = None , A : Optional[Union[float, List[float]]] = None , **A : List[str] , ) ->None:
super().__init__(**A )
lowerCamelCase__ : str = size if size is not None else {'''shortest_edge''': 2_5_6}
lowerCamelCase__ : Optional[Any] = get_size_dict(A , default_to_square=A )
lowerCamelCase__ : Dict = crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
lowerCamelCase__ : Union[str, Any] = get_size_dict(A , param_name='''crop_size''' )
lowerCamelCase__ : int = do_resize
lowerCamelCase__ : int = size
lowerCamelCase__ : Tuple = do_center_crop
lowerCamelCase__ : int = crop_size
lowerCamelCase__ : List[Any] = resample
lowerCamelCase__ : Any = do_rescale
lowerCamelCase__ : Any = rescale_factor
lowerCamelCase__ : str = offset
lowerCamelCase__ : Optional[int] = do_normalize
lowerCamelCase__ : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCamelCase__ : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __lowerCamelCase ( self : Optional[Any] , A : np.ndarray , A : Dict[str, int] , A : PILImageResampling = PILImageResampling.BILINEAR , A : Optional[Union[str, ChannelDimension]] = None , **A : str , ) ->np.ndarray:
lowerCamelCase__ : Any = get_size_dict(A , default_to_square=A )
if "shortest_edge" in size:
lowerCamelCase__ : Dict = get_resize_output_image_size(A , size['''shortest_edge'''] , default_to_square=A )
elif "height" in size and "width" in size:
lowerCamelCase__ : List[Any] = (size['''height'''], size['''width'''])
else:
raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(A , size=A , resample=A , data_format=A , **A )
def __lowerCamelCase ( self : Optional[int] , A : np.ndarray , A : Dict[str, int] , A : Optional[Union[str, ChannelDimension]] = None , **A : Dict , ) ->np.ndarray:
lowerCamelCase__ : Optional[Any] = get_size_dict(A )
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(A , size=(size['''height'''], size['''width''']) , data_format=A , **A )
def __lowerCamelCase ( self : List[str] , A : np.ndarray , A : Union[int, float] , A : bool = True , A : Optional[Union[str, ChannelDimension]] = None , **A : List[Any] , ) ->Optional[Any]:
lowerCamelCase__ : Optional[int] = image.astype(np.floataa )
if offset:
lowerCamelCase__ : int = image - (scale / 2)
return rescale(A , scale=A , data_format=A , **A )
def __lowerCamelCase ( self : List[str] , A : np.ndarray , A : Union[float, List[float]] , A : Union[float, List[float]] , A : Optional[Union[str, ChannelDimension]] = None , **A : List[str] , ) ->np.ndarray:
return normalize(A , mean=A , std=A , data_format=A , **A )
def __lowerCamelCase ( self : Any , A : ImageInput , A : bool = None , A : Dict[str, int] = None , A : PILImageResampling = None , A : bool = None , A : Dict[str, int] = None , A : bool = None , A : float = None , A : bool = None , A : bool = None , A : Optional[Union[float, List[float]]] = None , A : Optional[Union[float, List[float]]] = None , A : Optional[ChannelDimension] = ChannelDimension.FIRST , ) ->np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
if offset and not do_rescale:
raise ValueError('''For offset, do_rescale must also be set to True.''' )
# All transformations expect numpy arrays.
lowerCamelCase__ : Optional[int] = to_numpy_array(A )
if do_resize:
lowerCamelCase__ : Union[str, Any] = self.resize(image=A , size=A , resample=A )
if do_center_crop:
lowerCamelCase__ : int = self.center_crop(A , size=A )
if do_rescale:
lowerCamelCase__ : Tuple = self.rescale(image=A , scale=A , offset=A )
if do_normalize:
lowerCamelCase__ : Dict = self.normalize(image=A , mean=A , std=A )
lowerCamelCase__ : Dict = to_channel_dimension_format(A , A )
return image
def __lowerCamelCase ( self : List[str] , A : ImageInput , A : bool = None , A : Dict[str, int] = None , A : PILImageResampling = None , A : bool = None , A : Dict[str, int] = None , A : bool = None , A : float = None , A : bool = None , A : bool = None , A : Optional[Union[float, List[float]]] = None , A : Optional[Union[float, List[float]]] = None , A : Optional[Union[str, TensorType]] = None , A : ChannelDimension = ChannelDimension.FIRST , **A : Optional[int] , ) ->PIL.Image.Image:
lowerCamelCase__ : List[Any] = do_resize if do_resize is not None else self.do_resize
lowerCamelCase__ : str = resample if resample is not None else self.resample
lowerCamelCase__ : Optional[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCamelCase__ : Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase__ : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase__ : Dict = offset if offset is not None else self.offset
lowerCamelCase__ : List[str] = do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase__ : str = image_mean if image_mean is not None else self.image_mean
lowerCamelCase__ : Dict = image_std if image_std is not None else self.image_std
lowerCamelCase__ : int = size if size is not None else self.size
lowerCamelCase__ : Union[str, Any] = get_size_dict(A , default_to_square=A )
lowerCamelCase__ : List[Any] = crop_size if crop_size is not None else self.crop_size
lowerCamelCase__ : int = get_size_dict(A , param_name='''crop_size''' )
if not valid_images(A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
lowerCamelCase__ : Union[str, Any] = make_batched(A )
lowerCamelCase__ : Optional[Any] = [
[
self._preprocess_image(
image=A , do_resize=A , size=A , resample=A , do_center_crop=A , crop_size=A , do_rescale=A , rescale_factor=A , offset=A , do_normalize=A , image_mean=A , image_std=A , data_format=A , )
for img in video
]
for video in videos
]
lowerCamelCase__ : List[str] = {'''pixel_values''': videos}
return BatchFeature(data=A , tensor_type=A )
| 265 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_A : Tuple = logging.get_logger(__name__)
_A : List[str] = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ,lowerCAmelCase_ ):
_UpperCAmelCase : Dict = "nat"
_UpperCAmelCase : Tuple = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : List[Any] , A : Union[str, Any]=4 , A : str=3 , A : List[Any]=6_4 , A : Optional[Any]=[3, 4, 6, 5] , A : int=[2, 4, 8, 1_6] , A : Optional[int]=7 , A : List[Any]=3.0 , A : str=True , A : str=0.0 , A : Any=0.0 , A : int=0.1 , A : Tuple="gelu" , A : List[Any]=0.02 , A : str=1e-5 , A : Optional[int]=0.0 , A : Optional[Any]=None , A : Dict=None , **A : str , ) ->Union[str, Any]:
super().__init__(**A )
lowerCamelCase__ : Any = patch_size
lowerCamelCase__ : Optional[Any] = num_channels
lowerCamelCase__ : Any = embed_dim
lowerCamelCase__ : str = depths
lowerCamelCase__ : Union[str, Any] = len(A )
lowerCamelCase__ : int = num_heads
lowerCamelCase__ : Optional[int] = kernel_size
lowerCamelCase__ : Optional[int] = mlp_ratio
lowerCamelCase__ : List[Any] = qkv_bias
lowerCamelCase__ : Tuple = hidden_dropout_prob
lowerCamelCase__ : List[str] = attention_probs_dropout_prob
lowerCamelCase__ : List[Any] = drop_path_rate
lowerCamelCase__ : Union[str, Any] = hidden_act
lowerCamelCase__ : Union[str, Any] = layer_norm_eps
lowerCamelCase__ : List[Any] = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCamelCase__ : str = int(embed_dim * 2 ** (len(A ) - 1) )
lowerCamelCase__ : Dict = layer_scale_init_value
lowerCamelCase__ : Dict = ['''stem'''] + [F"stage{idx}" for idx in range(1 , len(A ) + 1 )]
lowerCamelCase__ , lowerCamelCase__ : str = get_aligned_output_features_output_indices(
out_features=A , out_indices=A , stage_names=self.stage_names )
| 265 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase : Tuple = {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
'''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A : int = '''roberta'''
def __init__( self : Union[str, Any] , __a : Optional[Any]=50265 , __a : int=768 , __a : Optional[int]=12 , __a : int=12 , __a : List[Any]=3072 , __a : int="gelu" , __a : List[Any]=0.1 , __a : str=0.1 , __a : Optional[int]=512 , __a : str=2 , __a : str=0.02 , __a : Dict=1E-12 , __a : int=1 , __a : Union[str, Any]=0 , __a : Union[str, Any]=2 , __a : List[str]="absolute" , __a : Tuple=True , __a : Any=None , **__a : Optional[int] , ) -> Optional[int]:
"""simple docstring"""
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
__lowercase : Any = vocab_size
__lowercase : int = hidden_size
__lowercase : Dict = num_hidden_layers
__lowercase : List[str] = num_attention_heads
__lowercase : int = hidden_act
__lowercase : int = intermediate_size
__lowercase : Union[str, Any] = hidden_dropout_prob
__lowercase : str = attention_probs_dropout_prob
__lowercase : Dict = max_position_embeddings
__lowercase : Optional[int] = type_vocab_size
__lowercase : List[Any] = initializer_range
__lowercase : Optional[int] = layer_norm_eps
__lowercase : str = position_embedding_type
__lowercase : str = use_cache
__lowercase : int = classifier_dropout
class lowerCAmelCase ( __a ):
'''simple docstring'''
@property
def lowerCAmelCase ( self : List[str] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
__lowercase : Any = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__lowercase : List[str] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] ) | 233 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def snake_case_ ( lowerCAmelCase_ : int = 8 ):
__lowercase : str = ascii_letters + digits + punctuation
return "".join(secrets.choice(lowerCAmelCase_ ) for _ in range(lowerCAmelCase_ ) )
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : int ):
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(lowerCAmelCase_ )
__lowercase : List[Any] = i // 3
__lowercase : int = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
__lowercase : str = (
chars_incl
+ random(lowerCAmelCase_ , quotient + remainder )
+ random(lowerCAmelCase_ , lowerCAmelCase_ )
+ random(lowerCAmelCase_ , lowerCAmelCase_ )
)
__lowercase : int = list(lowerCAmelCase_ )
shuffle(lowerCAmelCase_ )
return "".join(lowerCAmelCase_ )
# random is a generalised function for letters, characters and numbers
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : int ):
return "".join(secrets.choice(lowerCAmelCase_ ) for _ in range(lowerCAmelCase_ ) )
def snake_case_ ( lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : int ):
pass # Put your code here...
def snake_case_ ( lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[Any] ):
pass # Put your code here...
def snake_case_ ( lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[int] ):
pass # Put your code here...
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : int = 8 ):
if len(lowerCAmelCase_ ) < min_length:
# Your Password must be at least 8 characters long
return False
__lowercase : Tuple = any(char in ascii_uppercase for char in password )
__lowercase : Union[str, Any] = any(char in ascii_lowercase for char in password )
__lowercase : Dict = any(char in digits for char in password )
__lowercase : Tuple = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def snake_case_ ( ):
__lowercase : Union[str, Any] = int(input("""Please indicate the max length of your password: """ ).strip() )
__lowercase : List[str] = input(
"""Please indicate the characters that must be in your password: """ ).strip()
print("""Password generated:""" , password_generator(lowerCAmelCase_ ) )
print(
"""Alternative Password generated:""" , alternative_password_generator(lowerCAmelCase_ , lowerCAmelCase_ ) , )
print("""[If you are thinking of using this passsword, You better save it.]""" )
if __name__ == "__main__":
main() | 233 | 1 |
from __future__ import annotations
def __lowerCAmelCase ( a__ , a__ , a__ ) -> float:
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('''daily_interest_rate must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return principal * daily_interest_rate * days_between_payments
def __lowerCAmelCase ( a__ , a__ , a__ , ) -> float:
if number_of_compounding_periods <= 0:
raise ValueError('''number_of_compounding_periods must be > 0''' )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError('''nominal_annual_interest_rate_percentage must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def __lowerCAmelCase ( a__ , a__ , a__ , ) -> float:
if number_of_years <= 0:
raise ValueError('''number_of_years must be > 0''' )
if nominal_annual_percentage_rate < 0:
raise ValueError('''nominal_annual_percentage_rate must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return compound_interest(
a__ , nominal_annual_percentage_rate / 365 , number_of_years * 365 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 363 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
A : str = (
'https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'
)
A : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def __lowerCAmelCase ( ) -> Tuple:
__a = '''https://pypi.org/pypi/diffusers/json'''
__a = json.loads(request.urlopen(a__ ).read() )['''releases'''].keys()
return sorted(a__ , key=lambda a__ : version.Version(a__ ) )
def __lowerCAmelCase ( ) -> List[Any]:
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(a__ )
os.makedirs(a__ , exist_ok=a__ )
__a = Path(a__ ) / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def __lowerCAmelCase ( a__ ) -> Dict:
init_hf_modules()
__a = Path(a__ ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(a__ , exist_ok=a__ )
__a = dynamic_module_path / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def __lowerCAmelCase ( a__ ) -> Dict:
with open(a__ , '''r''' , encoding='''utf-8''' ) as f:
__a = f.read()
# Imports of the form `import .xxx`
__a = re.findall('''^\s*import\s+\.(\S+)\s*$''' , a__ , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import''' , a__ , flags=re.MULTILINE )
# Unique-ify
return list(set(a__ ) )
def __lowerCAmelCase ( a__ ) -> Any:
__a = False
__a = [module_file]
__a = []
# Let's recurse through all relative imports
while not no_change:
__a = []
for f in files_to_check:
new_imports.extend(get_relative_imports(a__ ) )
__a = Path(a__ ).parent
__a = [str(module_path / m ) for m in new_imports]
__a = [f for f in new_import_files if f not in all_relative_imports]
__a = [F"""{f}.py""" for f in new_import_files]
__a = len(a__ ) == 0
all_relative_imports.extend(a__ )
return all_relative_imports
def __lowerCAmelCase ( a__ ) -> str:
with open(a__ , '''r''' , encoding='''utf-8''' ) as f:
__a = f.read()
# Imports of the form `import xxx`
__a = re.findall('''^\s*import\s+(\S+)\s*$''' , a__ , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall('''^\s*from\s+(\S+)\s+import''' , a__ , flags=re.MULTILINE )
# Only keep the top-level module
__a = [imp.split('''.''' )[0] for imp in imports if not imp.startswith('''.''' )]
# Unique-ify and test we got them all
__a = list(set(a__ ) )
__a = []
for imp in imports:
try:
importlib.import_module(a__ )
except ImportError:
missing_packages.append(a__ )
if len(a__ ) > 0:
raise ImportError(
'''This modeling file requires the following packages that were not found in your environment: '''
F"""{', '.join(a__ )}. Run `pip install {' '.join(a__ )}`""" )
return get_relative_imports(a__ )
def __lowerCAmelCase ( a__ , a__ ) -> Dict:
__a = module_path.replace(os.path.sep , '''.''' )
__a = importlib.import_module(a__ )
if class_name is None:
return find_pipeline_class(a__ )
return getattr(a__ , a__ )
def __lowerCAmelCase ( a__ ) -> Optional[Any]:
from ..pipelines import DiffusionPipeline
__a = dict(inspect.getmembers(a__ , inspect.isclass ) )
__a = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , a__ )
and cls.__module__.split('''.''' )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
F"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"""
F""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"""
F""" {loaded_module}.""" )
__a = cls
return pipeline_class
def __lowerCAmelCase ( a__ , a__ , a__ = None , a__ = False , a__ = False , a__ = None , a__ = None , a__ = None , a__ = False , ) -> Tuple:
__a = str(a__ )
__a = os.path.join(a__ , a__ )
if os.path.isfile(a__ ):
__a = module_file_or_url
__a = '''local'''
elif pretrained_model_name_or_path.count('''/''' ) == 0:
__a = get_diffusers_versions()
# cut ".dev0"
__a = '''v''' + '''.'''.join(__version__.split('''.''' )[:3] )
# retrieve github version that matches
if revision is None:
__a = latest_version if latest_version[1:] in available_versions else '''main'''
logger.info(F"""Defaulting to latest_version: {revision}.""" )
elif revision in available_versions:
__a = F"""v{revision}"""
elif revision == "main":
__a = revision
else:
raise ValueError(
F"""`custom_revision`: {revision} does not exist. Please make sure to choose one of"""
F""" {', '.join(available_versions + ['main'] )}.""" )
# community pipeline on GitHub
__a = COMMUNITY_PIPELINES_URL.format(revision=a__ , pipeline=a__ )
try:
__a = cached_download(
a__ , cache_dir=a__ , force_download=a__ , proxies=a__ , resume_download=a__ , local_files_only=a__ , use_auth_token=a__ , )
__a = '''git'''
__a = pretrained_model_name_or_path + '''.py'''
except EnvironmentError:
logger.error(F"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
raise
else:
try:
# Load from URL or cache if already cached
__a = hf_hub_download(
a__ , a__ , cache_dir=a__ , force_download=a__ , proxies=a__ , resume_download=a__ , local_files_only=a__ , use_auth_token=a__ , )
__a = os.path.join('''local''' , '''--'''.join(pretrained_model_name_or_path.split('''/''' ) ) )
except EnvironmentError:
logger.error(F"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
raise
# Check we have all the requirements in our environment
__a = check_imports(a__ )
# Now we move the module inside our cached dynamic modules.
__a = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(a__ )
__a = Path(a__ ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(a__ , submodule_path / module_file )
for module_needed in modules_needed:
__a = F"""{module_needed}.py"""
shutil.copy(os.path.join(a__ , a__ ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(a__ , a__ ):
__a = use_auth_token
elif use_auth_token is True:
__a = HfFolder.get_token()
else:
__a = None
__a = model_info(a__ , revision=a__ , token=a__ ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
__a = submodule_path / commit_hash
__a = full_submodule + os.path.sep + commit_hash
create_dynamic_module(a__ )
if not (submodule_path / module_file).exists():
shutil.copy(a__ , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
a__ , F"""{module_needed}.py""" , cache_dir=a__ , force_download=a__ , resume_download=a__ , proxies=a__ , use_auth_token=a__ , revision=a__ , local_files_only=a__ , )
return os.path.join(a__ , a__ )
def __lowerCAmelCase ( a__ , a__ , a__ = None , a__ = None , a__ = False , a__ = False , a__ = None , a__ = None , a__ = None , a__ = False , **a__ , ) -> Tuple:
__a = get_cached_module_file(
a__ , a__ , cache_dir=a__ , force_download=a__ , resume_download=a__ , proxies=a__ , use_auth_token=a__ , revision=a__ , local_files_only=a__ , )
return get_class_in_module(a__ , final_module.replace('''.py''' , '''''' ) ) | 33 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
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 (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class _snake_case ( lowercase_ ):
def __init__( self , a__ , a__=13 , a__=7 , a__=True , a__=True , a__=True , a__=True , a__=99 , a__=32 , a__=5 , a__=4 , a__=37 , a__="gelu" , a__=0.1 , a__=0.1 , a__=512 , a__=16 , a__=2 , a__=0.0_2 , a__=False , a__=True , a__="None" , a__=3 , a__=4 , a__=None , ) -> List[Any]:
'''simple docstring'''
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_ = num_choices
snake_case_ = relative_attention
snake_case_ = position_biased_input
snake_case_ = pos_att_type
snake_case_ = scope
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
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
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_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
return DebertaConfig(
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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ = self.get_config()
snake_case_ = 300
return config
def lowerCAmelCase__ ( self , a__ ) -> List[str]:
'''simple docstring'''
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def lowerCAmelCase__ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ) -> Any:
'''simple docstring'''
snake_case_ = DebertaModel(config=a__ )
model.to(a__ )
model.eval()
snake_case_ = model(a__ , attention_mask=a__ , token_type_ids=a__ )[0]
snake_case_ = model(a__ , token_type_ids=a__ )[0]
snake_case_ = model(a__ )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def lowerCAmelCase__ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ) -> Any:
'''simple docstring'''
snake_case_ = DebertaForMaskedLM(config=a__ )
model.to(a__ )
model.eval()
snake_case_ = model(a__ , attention_mask=a__ , token_type_ids=a__ , labels=a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase__ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = self.num_labels
snake_case_ = DebertaForSequenceClassification(a__ )
model.to(a__ )
model.eval()
snake_case_ = model(a__ , attention_mask=a__ , token_type_ids=a__ , labels=a__ )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(a__ )
def lowerCAmelCase__ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ) -> Optional[int]:
'''simple docstring'''
snake_case_ = self.num_labels
snake_case_ = DebertaForTokenClassification(config=a__ )
model.to(a__ )
model.eval()
snake_case_ = model(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 lowerCAmelCase__ ( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ ) -> Optional[int]:
'''simple docstring'''
snake_case_ = DebertaForQuestionAnswering(config=a__ )
model.to(a__ )
model.eval()
snake_case_ = model(
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 lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
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, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _snake_case ( lowercase_ , lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : Optional[int] = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ : Dict = (
{
"feature-extraction": DebertaModel,
"fill-mask": DebertaForMaskedLM,
"question-answering": DebertaForQuestionAnswering,
"text-classification": DebertaForSequenceClassification,
"token-classification": DebertaForTokenClassification,
"zero-shot": DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase_ : Optional[int] = True
lowerCAmelCase_ : Union[str, Any] = False
lowerCAmelCase_ : Tuple = False
lowerCAmelCase_ : Optional[int] = False
lowerCAmelCase_ : Tuple = False
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ = DebertaModelTester(self )
snake_case_ = ConfigTester(self , config_class=a__ , hidden_size=37 )
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*a__ )
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*a__ )
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*a__ )
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*a__ )
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*a__ )
@slow
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = DebertaModel.from_pretrained(a__ )
self.assertIsNotNone(a__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class _snake_case ( unittest.TestCase ):
@unittest.skip(reason="Model not available yet" )
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
pass
@slow
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ = DebertaModel.from_pretrained("microsoft/deberta-base" )
snake_case_ = torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
snake_case_ = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
snake_case_ = model(a__ , attention_mask=a__ )[0]
# compare the actual values for a slice.
snake_case_ = torch.tensor(
[[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a__ , atol=1e-4 ) , F'{output[:, 1:4, 1:4]}' )
| 85 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BridgeTowerImageProcessor
class A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , _UpperCAmelCase , _UpperCAmelCase = True , _UpperCAmelCase = None , _UpperCAmelCase = 3_2 , _UpperCAmelCase = True , _UpperCAmelCase = 1 / 2_5_5 , _UpperCAmelCase = True , _UpperCAmelCase = True , _UpperCAmelCase = [0.48_145_466, 0.4_578_275, 0.40_821_073] , _UpperCAmelCase = [0.26_862_954, 0.26_130_258, 0.27_577_711] , _UpperCAmelCase = True , _UpperCAmelCase=7 , _UpperCAmelCase=3_0 , _UpperCAmelCase=4_0_0 , _UpperCAmelCase=3 , ) -> Dict:
__UpperCamelCase : Dict = parent
__UpperCamelCase : Any = do_resize
__UpperCamelCase : Union[str, Any] = size if size is not None else {"shortest_edge": 2_8_8}
__UpperCamelCase : Any = size_divisor
__UpperCamelCase : Optional[int] = do_rescale
__UpperCamelCase : Union[str, Any] = rescale_factor
__UpperCamelCase : int = do_normalize
__UpperCamelCase : List[Any] = do_center_crop
__UpperCamelCase : Optional[int] = image_mean
__UpperCamelCase : Tuple = image_std
__UpperCamelCase : Tuple = do_pad
__UpperCamelCase : Tuple = batch_size
__UpperCamelCase : Dict = num_channels
__UpperCamelCase : Dict = min_resolution
__UpperCamelCase : Optional[Any] = max_resolution
def a_ (self ) -> Optional[int]:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"size_divisor": self.size_divisor,
}
def a_ (self , _UpperCAmelCase , _UpperCAmelCase=False ) -> Optional[Any]:
if not batched:
__UpperCamelCase : List[str] = self.size["shortest_edge"]
__UpperCamelCase : Optional[int] = image_inputs[0]
if isinstance(_UpperCAmelCase , Image.Image ):
__UpperCamelCase , __UpperCamelCase : Optional[Any] = image.size
else:
__UpperCamelCase , __UpperCamelCase : Union[str, Any] = image.shape[1], image.shape[2]
__UpperCamelCase : Dict = size / min(_UpperCAmelCase , _UpperCAmelCase )
if h < w:
__UpperCamelCase , __UpperCamelCase : Tuple = size, scale * w
else:
__UpperCamelCase , __UpperCamelCase : List[Any] = scale * h, size
__UpperCamelCase : List[Any] = int((1_3_3_3 / 8_0_0) * size )
if max(_UpperCAmelCase , _UpperCAmelCase ) > max_size:
__UpperCamelCase : str = max_size / max(_UpperCAmelCase , _UpperCAmelCase )
__UpperCamelCase : Dict = newh * scale
__UpperCamelCase : Union[str, Any] = neww * scale
__UpperCamelCase , __UpperCamelCase : Optional[int] = int(newh + 0.5 ), int(neww + 0.5 )
__UpperCamelCase , __UpperCamelCase : Optional[int] = (
newh // self.size_divisor * self.size_divisor,
neww // self.size_divisor * self.size_divisor,
)
else:
__UpperCamelCase : int = []
for image in image_inputs:
__UpperCamelCase , __UpperCamelCase : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__UpperCamelCase : Tuple = max(_UpperCAmelCase , key=lambda _UpperCAmelCase : item[0] )[0]
__UpperCamelCase : Union[str, Any] = max(_UpperCAmelCase , key=lambda _UpperCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class A ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
A = BridgeTowerImageProcessor if is_vision_available() else None
def a_ (self ) -> Dict:
__UpperCamelCase : Optional[Any] = BridgeTowerImageProcessingTester(self )
@property
def a_ (self ) -> Optional[int]:
return self.image_processor_tester.prepare_image_processor_dict()
def a_ (self ) -> Union[str, Any]:
__UpperCamelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_UpperCAmelCase , "image_mean" ) )
self.assertTrue(hasattr(_UpperCAmelCase , "image_std" ) )
self.assertTrue(hasattr(_UpperCAmelCase , "do_normalize" ) )
self.assertTrue(hasattr(_UpperCAmelCase , "do_resize" ) )
self.assertTrue(hasattr(_UpperCAmelCase , "size" ) )
self.assertTrue(hasattr(_UpperCAmelCase , "size_divisor" ) )
def a_ (self ) -> List[str]:
pass
def a_ (self ) -> List[Any]:
# Initialize image processor
__UpperCamelCase : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__UpperCamelCase : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(_UpperCAmelCase , Image.Image )
# Test not batched input
__UpperCamelCase : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
__UpperCamelCase , __UpperCamelCase : List[str] = 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
__UpperCamelCase : Optional[int] = image_processing(_UpperCAmelCase , return_tensors="pt" ).pixel_values
__UpperCamelCase , __UpperCamelCase : List[str] = 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 a_ (self ) -> Tuple:
# Initialize image processor
__UpperCamelCase : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__UpperCamelCase : int = 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
__UpperCamelCase : Optional[int] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
__UpperCamelCase , __UpperCamelCase : Optional[Any] = 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
__UpperCamelCase : List[Any] = image_processing(_UpperCAmelCase , return_tensors="pt" ).pixel_values
__UpperCamelCase , __UpperCamelCase : int = 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 a_ (self ) -> int:
# Initialize image processor
__UpperCamelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__UpperCamelCase : List[str] = 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
__UpperCamelCase : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
__UpperCamelCase , __UpperCamelCase : int = 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
__UpperCamelCase : Optional[Any] = image_processing(_UpperCAmelCase , return_tensors="pt" ).pixel_values
__UpperCamelCase , __UpperCamelCase : Optional[int] = 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,
) , )
| 298 | 0 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class _UpperCamelCase ( lowerCAmelCase__ ,unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] =RoFormerTokenizer
__UpperCAmelCase : List[Any] =RoFormerTokenizerFast
__UpperCAmelCase : int =True
__UpperCAmelCase : Union[str, Any] =True
def snake_case ( self ):
super().setUp()
def snake_case ( self , **__a ):
return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base" , **__a )
def snake_case ( self , **__a ):
return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base" , **__a )
def snake_case ( self ):
__lowerCAmelCase = "永和服装饰品有限公司,今天天气非常好"
__lowerCAmelCase = "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
return input_text, output_text
def snake_case ( self ):
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase , __lowerCAmelCase = self.get_chinese_input_output_texts()
__lowerCAmelCase = tokenizer.tokenize(__a )
self.assertListEqual(__a , output_text.split() )
__lowerCAmelCase = tokens + [tokenizer.unk_token]
__lowerCAmelCase = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , __a )
def snake_case ( self ):
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase , __lowerCAmelCase = self.get_chinese_input_output_texts()
__lowerCAmelCase = tokenizer.tokenize(__a )
self.assertListEqual(__a , output_text.split() )
__lowerCAmelCase = tokens + [tokenizer.unk_token]
__lowerCAmelCase = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , __a )
def snake_case ( self ):
pass
def snake_case ( self ):
pass
def snake_case ( self ):
pass
| 365 |
"""simple docstring"""
import random
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = False ):
'''simple docstring'''
__lowerCAmelCase = {i: [] for i in range(_UpperCamelCase )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_graph(_UpperCamelCase )
# 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(_UpperCamelCase ):
for j in range(i + 1 , _UpperCamelCase ):
if random.random() < probability:
graph[i].append(_UpperCamelCase )
if not directed:
# if the graph is undirected, add an edge in from j to i, either
graph[j].append(_UpperCamelCase )
return graph
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return {
i: [j for j in range(_UpperCamelCase ) if i != j] for i in range(_UpperCamelCase )
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 259 | 0 |
from string import ascii_uppercase
_UpperCAmelCase : List[Any] = {str(ord(c) - 55): c for c in ascii_uppercase}
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('int() can\'t convert non-string with explicit base' )
if num < 0:
raise ValueError('parameter must be positive int' )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('\'str\' object cannot be interpreted as an integer' )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if base in (0, 1):
raise ValueError('base must be >= 2' )
if base > 36:
raise ValueError('base must be <= 36' )
snake_case_ = ''
snake_case_ = 0
snake_case_ = 0
while div != 1:
snake_case_ = divmod(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if base >= 11 and 9 < mod < 36:
snake_case_ = ALPHABET_VALUES[str(_SCREAMING_SNAKE_CASE )]
else:
snake_case_ = str(_SCREAMING_SNAKE_CASE )
new_value += actual_value
snake_case_ = num // base
snake_case_ = div
if div == 0:
return str(new_value[::-1] )
elif div == 1:
new_value += str(_SCREAMING_SNAKE_CASE )
return str(new_value[::-1] )
return new_value[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for base in range(2, 37):
for num in range(1000):
assert int(decimal_to_any(num, base), base) == num, (
num,
base,
decimal_to_any(num, base),
int(decimal_to_any(num, base), base),
)
| 285 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = ort.SessionOptions()
__a : Dict = False
return options
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
__a : int = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
__a : Dict = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy' )
# using the PNDM scheduler by default
__a : str = OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='onnx' , safety_checker=__a , feature_extractor=__a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__a )
__a : Tuple = 'A red cat sitting on a park bench'
__a : int = np.random.RandomState(0 )
__a : Tuple = pipe(
prompt=__a , image=__a , mask_image=__a , strength=0.75 , guidance_scale=7.5 , num_inference_steps=15 , generator=__a , output_type='np' , )
__a : Tuple = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 1E-2
| 27 | 0 |
"""simple docstring"""
import re
import string
import numpy as np
import datasets
UpperCAmelCase = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
UpperCAmelCase = '''
Args:
predictions: List of predicted texts.
references: List of reference texts.
regexes_to_ignore: List, defaults to None. Regex expressions of characters to
ignore when calculating the exact matches. Note: these regexes are removed
from the input data before the changes based on the options below (e.g. ignore_case,
ignore_punctuation, ignore_numbers) are applied.
ignore_case: Boolean, defaults to False. If true, turns everything
to lowercase so that capitalization differences are ignored.
ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
comparing predictions and references.
ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
comparing predictions and references.
Returns:
exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["the cat", "theater", "YELLING", "agent007"]
>>> preds = ["cat?", "theater", "yelling", "agent"]
>>> results = exact_match.compute(references=refs, predictions=preds)
>>> print(round(results["exact_match"], 1))
25.0
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["the cat", "theater", "YELLING", "agent007"]
>>> preds = ["cat?", "theater", "yelling", "agent"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
>>> print(round(results["exact_match"], 1))
50.0
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["the cat", "theater", "YELLING", "agent007"]
>>> preds = ["cat?", "theater", "yelling", "agent"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
>>> print(round(results["exact_match"], 1))
75.0
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["the cat", "theater", "YELLING", "agent007"]
>>> preds = ["cat?", "theater", "yelling", "agent"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
>>> print(round(results["exact_match"], 1))
100.0
>>> exact_match = datasets.load_metric("exact_match")
>>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]
>>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]
>>> results = exact_match.compute(references=refs, predictions=preds)
>>> print(round(results["exact_match"], 1))
33.3
'''
UpperCAmelCase = '''
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def __snake_case ( self : Any ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , reference_urls=[] , )
def __snake_case ( self : Optional[Any] , snake_case__ : Union[str, Any] , snake_case__ : Optional[Any] , snake_case__ : Tuple=None , snake_case__ : Dict=False , snake_case__ : Optional[int]=False , snake_case__ : List[Any]=False , ):
'''simple docstring'''
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
lowercase :Union[str, Any] = np.array([re.sub(snake_case__ , '''''' , snake_case__ ) for x in predictions] )
lowercase :str = np.array([re.sub(snake_case__ , '''''' , snake_case__ ) for x in references] )
else:
lowercase :List[Any] = np.asarray(snake_case__ )
lowercase :int = np.asarray(snake_case__ )
if ignore_case:
lowercase :Any = np.char.lower(snake_case__ )
lowercase :int = np.char.lower(snake_case__ )
if ignore_punctuation:
lowercase :Optional[int] = string.punctuation.maketrans('''''' , '''''' , string.punctuation )
lowercase :List[str] = np.char.translate(snake_case__ , table=snake_case__ )
lowercase :str = np.char.translate(snake_case__ , table=snake_case__ )
if ignore_numbers:
lowercase :str = string.digits.maketrans('''''' , '''''' , string.digits )
lowercase :str = np.char.translate(snake_case__ , table=snake_case__ )
lowercase :Tuple = np.char.translate(snake_case__ , table=snake_case__ )
lowercase :Dict = predictions == references
return {"exact_match": np.mean(snake_case__ ) * 1_0_0}
| 172 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}
class __magic_name__ ( __UpperCAmelCase ):
__A : Tuple = "layoutlmv3"
def __init__( self : int , snake_case__ : Any=5_0_2_6_5 , snake_case__ : int=7_6_8 , snake_case__ : Dict=1_2 , snake_case__ : Optional[Any]=1_2 , snake_case__ : Union[str, Any]=3_0_7_2 , snake_case__ : Tuple="gelu" , snake_case__ : List[str]=0.1 , snake_case__ : List[str]=0.1 , snake_case__ : int=5_1_2 , snake_case__ : int=2 , snake_case__ : Optional[int]=0.02 , snake_case__ : Union[str, Any]=1e-5 , snake_case__ : Optional[int]=1 , snake_case__ : Any=0 , snake_case__ : Optional[int]=2 , snake_case__ : int=1_0_2_4 , snake_case__ : str=1_2_8 , snake_case__ : Tuple=1_2_8 , snake_case__ : Optional[Any]=True , snake_case__ : Union[str, Any]=3_2 , snake_case__ : Any=1_2_8 , snake_case__ : List[Any]=6_4 , snake_case__ : List[Any]=2_5_6 , snake_case__ : Any=True , snake_case__ : Optional[Any]=True , snake_case__ : Tuple=True , snake_case__ : List[Any]=2_2_4 , snake_case__ : Optional[int]=3 , snake_case__ : Union[str, Any]=1_6 , snake_case__ : str=None , **snake_case__ : List[str] , ):
'''simple docstring'''
super().__init__(
vocab_size=snake_case__ , hidden_size=snake_case__ , num_hidden_layers=snake_case__ , num_attention_heads=snake_case__ , intermediate_size=snake_case__ , hidden_act=snake_case__ , hidden_dropout_prob=snake_case__ , attention_probs_dropout_prob=snake_case__ , max_position_embeddings=snake_case__ , type_vocab_size=snake_case__ , initializer_range=snake_case__ , layer_norm_eps=snake_case__ , pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ , )
lowercase :Optional[int] = max_ad_position_embeddings
lowercase :Tuple = coordinate_size
lowercase :Any = shape_size
lowercase :Union[str, Any] = has_relative_attention_bias
lowercase :Optional[Any] = rel_pos_bins
lowercase :Tuple = max_rel_pos
lowercase :Any = has_spatial_attention_bias
lowercase :Any = rel_ad_pos_bins
lowercase :str = max_rel_ad_pos
lowercase :int = text_embed
lowercase :Optional[int] = visual_embed
lowercase :str = input_size
lowercase :List[str] = num_channels
lowercase :str = patch_size
lowercase :Any = classifier_dropout
class __magic_name__ ( __UpperCAmelCase ):
__A : Tuple = version.parse("1.12" )
@property
def __snake_case ( self : Any ):
'''simple docstring'''
if self.task in ["question-answering", "sequence-classification"]:
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
('''bbox''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
else:
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''bbox''', {0: '''batch''', 1: '''sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
] )
@property
def __snake_case ( self : int ):
'''simple docstring'''
return 1e-5
@property
def __snake_case ( self : Union[str, Any] ):
'''simple docstring'''
return 1_2
def __snake_case ( self : str , snake_case__ : "ProcessorMixin" , snake_case__ : int = -1 , snake_case__ : int = -1 , snake_case__ : bool = False , snake_case__ : Optional["TensorType"] = None , snake_case__ : int = 3 , snake_case__ : int = 4_0 , snake_case__ : int = 4_0 , ):
'''simple docstring'''
setattr(processor.image_processor , '''apply_ocr''' , snake_case__ )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase :Dict = compute_effective_axis_dimension(
snake_case__ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase :Union[str, Any] = processor.tokenizer.num_special_tokens_to_add(snake_case__ )
lowercase :List[str] = compute_effective_axis_dimension(
snake_case__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case__ )
# Generate dummy inputs according to compute batch and sequence
lowercase :Tuple = [[''' '''.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
lowercase :List[str] = [[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
lowercase :List[Any] = self._generate_dummy_images(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
lowercase :Dict = dict(
processor(
snake_case__ , text=snake_case__ , boxes=snake_case__ , return_tensors=snake_case__ , ) )
return inputs
| 172 | 1 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> "list[int]":
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
snake_case_ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
snake_case_ = 1
if upper_limit > 0:
snake_case_ = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(_SCREAMING_SNAKE_CASE ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
print('\n*** Enter -1 at any time to quit ***')
print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
try:
while True:
__SCREAMING_SNAKE_CASE : List[str] = int(input().strip())
if N < 0:
print('\n********* Goodbye!! ************')
break
else:
print(f"""The Catalan numbers from 0 through {N} are:""")
print(catalan_numbers(N))
print('Try another upper limit for the sequence: ', end='')
except (NameError, ValueError):
print('\n********* Invalid input, goodbye! ************\n')
import doctest
doctest.testmod()
| 347 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 1 |
'''simple docstring'''
import math
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowerCamelCase=0 ) -> int: # a graph with Node 0,1,...,N-1
A_ : List[str] = n
A_ : Any = [
[math.inf for j in range(0 , _a )] for i in range(0 , _a )
] # adjacency matrix for weight
A_ : Tuple = [
[math.inf for j in range(0 , _a )] for i in range(0 , _a )
] # dp[i][j] stores minimum distance from i to j
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> Optional[Any]:
A_ : int = w
def UpperCAmelCase_ ( self ) -> str:
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
A_ : Optional[int] = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase ) -> Union[str, Any]:
return self.dp[u][v]
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = 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)
| 365 |
'''simple docstring'''
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
UpperCamelCase__ : Optional[Any] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(42)
UpperCamelCase__ : Tuple = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}
UpperCamelCase__ : Optional[Any] = 'zero2'
UpperCamelCase__ : Optional[int] = 'zero3'
UpperCamelCase__ : Dict = [ZEROa, ZEROa]
def UpperCAmelCase ( a_ , a_ , a_ ) -> int:
"""simple docstring"""
A_ : int = parameterized.to_safe_name("""_""".join(str(a_ ) for x in param.args ) )
return F"{func.__name__}_{param_based_name}"
# Cartesian-product of zero stages with models to test
UpperCamelCase__ : Tuple = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class _lowerCAmelCase ( __A ):
"""simple docstring"""
@parameterized.expand(_lowerCamelCase , name_func=_lowerCamelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase ) -> Tuple:
self.run_and_check(
stage=_lowerCamelCase , model=_lowerCamelCase , distributed=_lowerCamelCase , fpaa=_lowerCamelCase , )
@require_torch_multi_gpu
@parameterized.expand(_lowerCamelCase , name_func=_lowerCamelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase ) -> Optional[int]:
self.run_and_check(
stage=_lowerCamelCase , model=_lowerCamelCase , distributed=_lowerCamelCase , fpaa=_lowerCamelCase , )
@parameterized.expand(_lowerCamelCase , name_func=_lowerCamelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase ) -> Dict:
self.run_and_check(
stage=_lowerCamelCase , model=_lowerCamelCase , distributed=_lowerCamelCase , fpaa=_lowerCamelCase , )
@require_torch_multi_gpu
@parameterized.expand(_lowerCamelCase , name_func=_lowerCamelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase ) -> int:
self.run_and_check(
stage=_lowerCamelCase , model=_lowerCamelCase , distributed=_lowerCamelCase , fpaa=_lowerCamelCase , )
def UpperCAmelCase_ ( self , _lowerCamelCase ) -> Optional[Any]:
# XXX: run_asr is premature and doesn't save any results
# so all we check for now is that the process didn't fail
pass
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 10 , _lowerCamelCase = True , _lowerCamelCase = True , _lowerCamelCase = True , ) -> List[str]:
A_ : Union[str, Any] = models[model]
A_ : Tuple = self.run_trainer(
stage=_lowerCamelCase , model_name=_lowerCamelCase , eval_steps=_lowerCamelCase , num_train_epochs=1 , distributed=_lowerCamelCase , fpaa=_lowerCamelCase , )
self.do_checks(_lowerCamelCase )
return output_dir
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 10 , _lowerCamelCase = 1 , _lowerCamelCase = True , _lowerCamelCase = True , ) -> Any:
A_ : Dict = self.get_auto_remove_tmp_dir("""./xxx""" , after=_lowerCamelCase )
A_ : str = F"\n --model_name_or_path {model_name}\n --dataset_name hf-internal-testing/librispeech_asr_dummy\n --dataset_config_name clean\n --train_split_name validation\n --validation_split_name validation\n --output_dir {output_dir}\n --num_train_epochs {str(_lowerCamelCase )}\n --per_device_train_batch_size 2\n --per_device_eval_batch_size 2\n --evaluation_strategy steps\n --learning_rate 5e-4\n --warmup_steps 8\n --orthography timit\n --preprocessing_num_workers 1\n --group_by_length\n --freeze_feature_extractor\n --report_to none\n --save_steps 0\n --eval_steps {eval_steps}\n --report_to none\n ".split()
if fpaa:
args.extend(["""--fp16"""] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
A_ : List[str] = F"--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json".split()
A_ : Union[str, Any] = [F"{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"]
A_ : Tuple = self.get_launcher(_lowerCamelCase )
A_ : Optional[int] = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(_lowerCamelCase , env=self.get_env() )
return output_dir
def UpperCAmelCase_ ( self , _lowerCamelCase=False ) -> Any:
# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
# - it won't be able to handle that
# 2. for now testing with just 2 gpus max (since some quality tests may give different
# results with mode gpus because we use very little data)
A_ : int = min(2 , get_gpu_count() ) if distributed else 1
return F"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()
| 164 | 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 : List[str] = {
"""microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}
class __lowercase ( UpperCAmelCase_ , UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : Optional[int] = '''resnet'''
_UpperCAmelCase : Optional[Any] = ['''basic''', '''bottleneck''']
def __init__( self : str , lowerCAmelCase__ : Any=3 , lowerCAmelCase__ : Optional[int]=64 , lowerCAmelCase__ : Dict=[256, 512, 1024, 2048] , lowerCAmelCase__ : List[str]=[3, 4, 6, 3] , lowerCAmelCase__ : Optional[Any]="bottleneck" , lowerCAmelCase__ : int="relu" , lowerCAmelCase__ : Optional[Any]=False , lowerCAmelCase__ : Dict=None , lowerCAmelCase__ : Optional[int]=None , **lowerCAmelCase__ : List[str] , ):
super().__init__(**lowerCAmelCase__)
if layer_type not in self.layer_types:
raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types)}")
SCREAMING_SNAKE_CASE_: Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE_: Optional[int] = embedding_size
SCREAMING_SNAKE_CASE_: Dict = hidden_sizes
SCREAMING_SNAKE_CASE_: List[Any] = depths
SCREAMING_SNAKE_CASE_: List[Any] = layer_type
SCREAMING_SNAKE_CASE_: Any = hidden_act
SCREAMING_SNAKE_CASE_: Any = downsample_in_first_stage
SCREAMING_SNAKE_CASE_: Tuple = ["stem"] + [F"stage{idx}" for idx in range(1 , len(lowerCAmelCase__) + 1)]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Dict = get_aligned_output_features_output_indices(
out_features=lowerCAmelCase__ , out_indices=lowerCAmelCase__ , stage_names=self.stage_names)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : Dict = version.parse('''1.11''' )
@property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
return 1E-3
| 13 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
SCREAMING_SNAKE_CASE_: Optional[int] = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("All input parameters must be positive" )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError("Relative densities cannot be greater than one" )
else:
SCREAMING_SNAKE_CASE_: int = 1 - (matter_density + radiation_density + dark_energy)
SCREAMING_SNAKE_CASE_: Dict = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
SCREAMING_SNAKE_CASE_: Any = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
lowerCAmelCase : List[Any] = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1E-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 13 | 1 |
# Algorithm for the pigeonhole sorting
def _lowerCAmelCase ( __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : str = min(__lowerCAmelCase ) # min() finds the minimum value
snake_case__ : Optional[int] = max(__lowerCAmelCase ) # max() finds the maximum value
snake_case__ : Tuple = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
snake_case__ : Dict = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(__lowerCAmelCase , __lowerCAmelCase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
snake_case__ : Dict = 0
for count in range(__lowerCAmelCase ):
while holes[count] > 0:
holes[count] -= 1
snake_case__ : List[str] = count + min_val
i += 1
def _lowerCAmelCase ( ) -> List[Any]:
"""simple docstring"""
snake_case__ : Any = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(__lowerCAmelCase )
print('''Sorted order is:''' , ''' '''.join(__lowerCAmelCase ) )
if __name__ == "__main__":
main()
| 44 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase ) + 1
snake_case__ : Tuple = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
snake_case__ : str = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )]
# since string of zero length match pattern of zero length
snake_case__ : int = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __lowerCAmelCase ):
snake_case__ : Dict = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __lowerCAmelCase ):
snake_case__ : str = dp[0][j - 2] if pattern[j - 1] == '''*''' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __lowerCAmelCase ):
for j in range(1 , __lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
snake_case__ : Dict = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
snake_case__ : Union[str, Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
snake_case__ : List[str] = dp[i - 1][j]
else:
snake_case__ : Union[str, Any] = 0
else:
snake_case__ : Tuple = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
A__ = '''aab'''
A__ = '''c*a*b'''
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f"""{input_string} matches the given pattern {pattern}""")
else:
print(f"""{input_string} does not match with the given pattern {pattern}""")
| 44 | 1 |
def __UpperCamelCase ( _A ):
if num < 0:
return False
lowerCAmelCase_ = num
lowerCAmelCase_ = 0
while num > 0:
lowerCAmelCase_ = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod()
| 278 |
def lowerCAmelCase__ ( lowerCamelCase_ : str):
'''simple docstring'''
lowerCAmelCase__ : str = [int(lowerCamelCase_) for i in ip_va_address.split('''.''') if i.isdigit()]
return len(lowerCamelCase_) == 4 and all(0 <= int(lowerCamelCase_) <= 254 for octet in octets)
if __name__ == "__main__":
__snake_case : List[Any] =input().strip()
__snake_case : Optional[Any] ='valid' if is_ip_va_address_valid(ip) else 'invalid'
print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")
| 129 | 0 |
'''simple docstring'''
import argparse
UpperCAmelCase : Optional[Any] = 'docs/source/_static/js/custom.js'
def a__ ( a__ ):
"""simple docstring"""
with open(__lowerCAmelCase , encoding="""utf-8""" , newline="""\n""" ) as f:
__SCREAMING_SNAKE_CASE = f.readlines()
__SCREAMING_SNAKE_CASE = 0
# First let's put the right version
while not lines[index].startswith("""const stableVersion =""" ):
index += 1
__SCREAMING_SNAKE_CASE = F'const stableVersion = "v{version}"\n'
# Then update the dictionary
while not lines[index].startswith("""const versionMapping = {""" ):
index += 1
# We go until the end
while not lines[index].startswith("""}""" ):
index += 1
# We add the new version at the end
lines[index - 1] += F' "v{version}": "v{version}",\n'
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(__lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument('--version', help='Release version.')
UpperCAmelCase : Tuple = parser.parse_args()
update_custom_js(args.version)
| 353 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
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 (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase__ ( a ):
"""simple docstring"""
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : List[Any]=13 , __SCREAMING_SNAKE_CASE : Optional[Any]=7 , __SCREAMING_SNAKE_CASE : Tuple=True , __SCREAMING_SNAKE_CASE : List[str]=True , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : Optional[int]=99 , __SCREAMING_SNAKE_CASE : int=32 , __SCREAMING_SNAKE_CASE : Any=5 , __SCREAMING_SNAKE_CASE : Dict=4 , __SCREAMING_SNAKE_CASE : Optional[int]=37 , __SCREAMING_SNAKE_CASE : str="gelu" , __SCREAMING_SNAKE_CASE : Dict=0.1 , __SCREAMING_SNAKE_CASE : Optional[int]=0.1 , __SCREAMING_SNAKE_CASE : Tuple=512 , __SCREAMING_SNAKE_CASE : Tuple=16 , __SCREAMING_SNAKE_CASE : Union[str, Any]=2 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.02 , __SCREAMING_SNAKE_CASE : Optional[Any]=False , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : List[str]="None" , __SCREAMING_SNAKE_CASE : List[str]=3 , __SCREAMING_SNAKE_CASE : int=4 , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = seq_length
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_input_mask
__SCREAMING_SNAKE_CASE = use_token_type_ids
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = type_vocab_size
__SCREAMING_SNAKE_CASE = type_sequence_label_size
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = num_labels
__SCREAMING_SNAKE_CASE = num_choices
__SCREAMING_SNAKE_CASE = relative_attention
__SCREAMING_SNAKE_CASE = position_biased_input
__SCREAMING_SNAKE_CASE = pos_att_type
__SCREAMING_SNAKE_CASE = scope
def UpperCAmelCase__ ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
if self.use_labels:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
__SCREAMING_SNAKE_CASE = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase__ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return DebertaConfig(
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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def UpperCAmelCase__ ( self : List[str] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_config()
__SCREAMING_SNAKE_CASE = 300
return config
def UpperCAmelCase__ ( self : List[Any] , __SCREAMING_SNAKE_CASE : Any ) -> Union[str, Any]:
"""simple docstring"""
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def UpperCAmelCase__ ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = DebertaModel(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE )[0]
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def UpperCAmelCase__ ( self : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[str] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = DebertaForMaskedLM(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase__ ( self : Any , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.num_labels
__SCREAMING_SNAKE_CASE = DebertaForSequenceClassification(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : List[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.num_labels
__SCREAMING_SNAKE_CASE = DebertaForTokenClassification(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase__ ( self : List[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = DebertaForQuestionAnswering(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(
__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , start_positions=__SCREAMING_SNAKE_CASE , end_positions=__SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase__ ( self : List[str] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
__SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCAmelCase__ ( a , a , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = (
{
"feature-extraction": DebertaModel,
"fill-mask": DebertaForMaskedLM,
"question-answering": DebertaForQuestionAnswering,
"text-classification": DebertaForSequenceClassification,
"token-classification": DebertaForTokenClassification,
"zero-shot": DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def UpperCAmelCase__ ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = DebertaModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , hidden_size=37 )
def UpperCAmelCase__ ( self : Tuple ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self : str ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : List[Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : List[str] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : List[str] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : Any ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__SCREAMING_SNAKE_CASE )
@slow
def UpperCAmelCase__ ( self : str ) -> str:
"""simple docstring"""
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE = DebertaModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
@unittest.skip(reason="""Model not available yet""" )
def UpperCAmelCase__ ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
pass
@slow
def UpperCAmelCase__ ( self : Optional[Any] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = DebertaModel.from_pretrained("""microsoft/deberta-base""" )
__SCREAMING_SNAKE_CASE = torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
__SCREAMING_SNAKE_CASE = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE )[0]
# compare the actual values for a slice.
__SCREAMING_SNAKE_CASE = torch.tensor(
[[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __SCREAMING_SNAKE_CASE , atol=1E-4 ) , f'{output[:, 1:4, 1:4]}' )
| 331 | 0 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__A = """ def __init__(self, config):
super().__init__()
self.transform = BertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
"""
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = tempfile.mkdtemp()
os.makedirs(os.path.join(self.transformer_dir , 'models/bert/' ) )
lowerCAmelCase__ :Tuple = self.transformer_dir
shutil.copy(
os.path.join(__UpperCAmelCase , 'src/transformers/models/bert/modeling_bert.py' ) , os.path.join(self.transformer_dir , 'models/bert/modeling_bert.py' ) , )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :str = 'src/transformers'
shutil.rmtree(self.transformer_dir )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = comment + F"\nclass {class_name}(nn.Module):\n" + class_code
if overwrite_result is not None:
lowerCAmelCase__ :int = comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
lowerCAmelCase__ :int = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_1_9 )
lowerCAmelCase__ :Union[str, Any] = black.format_str(__UpperCAmelCase , mode=__UpperCAmelCase )
lowerCAmelCase__ :Any = os.path.join(self.transformer_dir , 'new_code.py' )
with open(__UpperCAmelCase , 'w' , newline='\n' ) as f:
f.write(__UpperCAmelCase )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(__UpperCAmelCase ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=__UpperCAmelCase )
with open(__UpperCAmelCase , 'r' ) as f:
self.assertTrue(f.read() , __UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :str = check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead' )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead' , 'BertLMPredictionHead' , REFERENCE_CODE + '\n' , )
# With no empty line at the end
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead' , 'BertLMPredictionHead' , __UpperCAmelCase , )
# Copy consistency with rename
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel' , 'TestModelLMPredictionHead' , re.sub('Bert' , 'TestModel' , __UpperCAmelCase ) , )
# Copy consistency with a really long name
lowerCAmelCase__ :Dict = 'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
self.check_copy_consistency(
F"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}" , F"{long_class_name}LMPredictionHead" , re.sub('Bert' , __UpperCAmelCase , __UpperCAmelCase ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel' , 'TestModelLMPredictionHead' , __UpperCAmelCase , overwrite_result=re.sub('Bert' , 'TestModel' , __UpperCAmelCase ) , )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = check_copies.LOCALIZED_READMES['README_zh-hans.md']
lowerCAmelCase__ :Dict = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
' method has been applied to compress GPT2 into'
' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
' Multilingual BERT into'
' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
' Luong, Quoc V. Le, Christopher D. Manning.'
)
lowerCAmelCase__ :Any = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
lowerCAmelCase__ :Optional[Any] = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
' method has been applied to compress GPT2 into'
' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
' Multilingual BERT into'
' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
' Christopher D. Manning 发布。\n'
)
lowerCAmelCase__ , lowerCAmelCase__ :List[Any] = check_copies.convert_to_localized_md(
__UpperCAmelCase , __UpperCAmelCase , localized_readme['format_model_list'] )
self.assertFalse(__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ , lowerCAmelCase__ :Dict = check_copies.convert_to_localized_md(
__UpperCAmelCase , __UpperCAmelCase , localized_readme['format_model_list'] )
# Check whether the number of models is equal to README.md after conversion.
self.assertTrue(__UpperCAmelCase )
lowerCAmelCase__ :List[str] = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
)
lowerCAmelCase__ :str = (
'1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
lowerCAmelCase__ :Dict = (
'1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
)
lowerCAmelCase__ , lowerCAmelCase__ :Optional[Any] = check_copies.convert_to_localized_md(
__UpperCAmelCase , __UpperCAmelCase , localized_readme['format_model_list'] )
# Check if the model link is synchronized.
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
| 293 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->bool:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 293 | 1 |
from __future__ import annotations
def lowercase_ ( A__ , A__ , A__ ) -> int | float:
"""simple docstring"""
if len(A__ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(A__ )
or left < -len(A__ )
or right >= len(A__ )
or right < -len(A__ )
):
raise IndexError("list index out of range" )
if left == right:
return nums[left]
snake_case = (left + right) >> 1 # the middle
snake_case = find_max(A__ , A__ , A__ ) # find max in range[left, mid]
snake_case = find_max(A__ , mid + 1 , A__ ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 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"""
import argparse
import os
import re
import packaging.version
lowerCAmelCase = """examples/"""
lowerCAmelCase = {
"""examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(r"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
"""setup""": (re.compile(r"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), r"""\1version=\"VERSION\","""),
"""doc""": (re.compile(r"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
lowerCAmelCase = {
"""init""": """src/diffusers/__init__.py""",
"""setup""": """setup.py""",
}
lowerCAmelCase = """README.md"""
def lowerCAmelCase_ ( snake_case_ : str , snake_case_ : int , snake_case_ : Dict ) ->List[Any]:
with open(snake_case__ , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCamelCase__ : int =f.read()
lowerCamelCase__ , lowerCamelCase__ : List[Any] =REPLACE_PATTERNS[pattern]
lowerCamelCase__ : Union[str, Any] =replace.replace('VERSION' , snake_case__ )
lowerCamelCase__ : Optional[int] =re_pattern.sub(snake_case__ , snake_case__ )
with open(snake_case__ , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(snake_case__ )
def lowerCAmelCase_ ( snake_case_ : Tuple ) ->Any:
for folder, directories, fnames in os.walk(snake_case__ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('research_projects' )
if "legacy" in directories:
directories.remove('legacy' )
for fname in fnames:
if fname.endswith('.py' ):
update_version_in_file(os.path.join(snake_case__ , snake_case__ ) , snake_case__ , pattern='examples' )
def lowerCAmelCase_ ( snake_case_ : List[str] , snake_case_ : List[Any]=False ) ->List[str]:
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(snake_case__ , snake_case__ , snake_case__ )
if not patch:
update_version_in_examples(snake_case__ )
def lowerCAmelCase_ ( ) ->Tuple:
lowerCamelCase__ : List[str] ='🤗 Transformers currently provides the following architectures'
lowerCamelCase__ : Optional[Any] ='1. Want to contribute a new model?'
with open(snake_case__ , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCamelCase__ : Any =f.readlines()
# Find the start of the list.
lowerCamelCase__ : List[str] =0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowerCamelCase__ : List[str] =start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
lowerCamelCase__ : Tuple =lines[index].replace(
'https://huggingface.co/docs/diffusers/main/model_doc' , 'https://huggingface.co/docs/diffusers/model_doc' , )
index += 1
with open(snake_case__ , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(snake_case__ )
def lowerCAmelCase_ ( ) ->Optional[int]:
with open(REPLACE_FILES['init'] , 'r' ) as f:
lowerCamelCase__ : int =f.read()
lowerCamelCase__ : Optional[Any] =REPLACE_PATTERNS['init'][0].search(snake_case__ ).groups()[0]
return packaging.version.parse(snake_case__ )
def lowerCAmelCase_ ( snake_case_ : Dict=False ) ->Optional[int]:
lowerCamelCase__ : List[str] =get_version()
if patch and default_version.is_devrelease:
raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!' )
if default_version.is_devrelease:
lowerCamelCase__ : List[str] =default_version.base_version
elif patch:
lowerCamelCase__ : str =f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
lowerCamelCase__ : Any =f"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
lowerCamelCase__ : str =input(f"""Which version are you releasing? [{default_version}]""" )
if len(snake_case__ ) == 0:
lowerCamelCase__ : List[Any] =default_version
print(f"""Updating version to {version}.""" )
global_version_update(snake_case__ , patch=snake_case__ )
def lowerCAmelCase_ ( ) ->str:
lowerCamelCase__ : int =get_version()
lowerCamelCase__ : str =f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
lowerCamelCase__ : Dict =current_version.base_version
# Check with the user we got that right.
lowerCamelCase__ : int =input(f"""Which version are we developing now? [{dev_version}]""" )
if len(snake_case__ ) == 0:
lowerCamelCase__ : Tuple =dev_version
print(f"""Updating version to {version}.""" )
global_version_update(snake_case__ )
# print("Cleaning main README, don't forget to run `make fix-copies`.")
# clean_main_ref_in_model_list()
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
lowerCAmelCase = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("""Nothing to do after a patch :-)""")
else:
post_release_work() | 126 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def _snake_case ( snake_case__ : List[Any] , snake_case__ : Optional[int]=0.999 , snake_case__ : Union[str, Any]="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(snake_case__ : Union[str, Any] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(snake_case__ : Dict ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}' )
A = []
for i in range(snake_case__ ):
A = i / num_diffusion_timesteps
A = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(snake_case__ ) / alpha_bar_fn(snake_case__ ) , snake_case__ ) )
return torch.tensor(snake_case__ , dtype=torch.floataa )
class lowerCAmelCase_ ( _lowercase , _lowercase ):
'''simple docstring'''
_lowerCamelCase: Optional[int] = [e.name for e in KarrasDiffusionSchedulers]
_lowerCamelCase: Optional[Any] = 2
@register_to_config
def __init__( self : str ,A_ : int = 1000 ,A_ : float = 0.0_00_85 ,A_ : float = 0.0_12 ,A_ : str = "linear" ,A_ : Optional[Union[np.ndarray, List[float]]] = None ,A_ : str = "epsilon" ,A_ : Optional[bool] = False ,A_ : Optional[bool] = False ,A_ : float = 1.0 ,A_ : str = "linspace" ,A_ : int = 0 ,) -> List[str]:
if trained_betas is not None:
A = torch.tensor(A_ ,dtype=torch.floataa )
elif beta_schedule == "linear":
A = torch.linspace(A_ ,A_ ,A_ ,dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
A = (
torch.linspace(beta_start**0.5 ,beta_end**0.5 ,A_ ,dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
A = betas_for_alpha_bar(A_ ,alpha_transform_type='cosine' )
elif beta_schedule == "exp":
A = betas_for_alpha_bar(A_ ,alpha_transform_type='exp' )
else:
raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )
A = 1.0 - self.betas
A = torch.cumprod(self.alphas ,dim=0 )
# set all values
self.set_timesteps(A_ ,A_ ,A_ )
A = use_karras_sigmas
def _SCREAMING_SNAKE_CASE ( self : int ,A_ : Tuple ,A_ : Tuple=None ) -> Tuple:
if schedule_timesteps is None:
A = self.timesteps
A = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
A = 1 if len(A_ ) > 1 else 0
else:
A = timestep.cpu().item() if torch.is_tensor(A_ ) else timestep
A = self._index_counter[timestep_int]
return indices[pos].item()
@property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> List[Any]:
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def _SCREAMING_SNAKE_CASE ( self : List[Any] ,A_ : torch.FloatTensor ,A_ : Union[float, torch.FloatTensor] ,) -> torch.FloatTensor:
A = self.index_for_timestep(A_ )
A = self.sigmas[step_index]
A = sample / ((sigma**2 + 1) ** 0.5)
return sample
def _SCREAMING_SNAKE_CASE ( self : str ,A_ : int ,A_ : Union[str, torch.device] = None ,A_ : Optional[int] = None ,) -> Optional[Any]:
A = num_inference_steps
A = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
A = np.linspace(0 ,num_train_timesteps - 1 ,A_ ,dtype=A_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
A = 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 = (np.arange(0 ,A_ ) * step_ratio).round()[::-1].copy().astype(A_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
A = 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 = (np.arange(A_ ,0 ,-step_ratio )).round().copy().astype(A_ )
timesteps -= 1
else:
raise ValueError(
F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.' )
A = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
A = np.log(A_ )
A = np.interp(A_ ,np.arange(0 ,len(A_ ) ) ,A_ )
if self.config.use_karras_sigmas:
A = self._convert_to_karras(in_sigmas=A_ ,num_inference_steps=self.num_inference_steps )
A = np.array([self._sigma_to_t(A_ ,A_ ) for sigma in sigmas] )
A = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
A = torch.from_numpy(A_ ).to(device=A_ )
A = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )
A = torch.from_numpy(A_ )
A = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )
if str(A_ ).startswith('mps' ):
# mps does not support float64
A = timesteps.to(A_ ,dtype=torch.floataa )
else:
A = timesteps.to(device=A_ )
# empty dt and derivative
A = None
A = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
A = defaultdict(A_ )
def _SCREAMING_SNAKE_CASE ( self : Any ,A_ : Optional[Any] ,A_ : List[str] ) -> Dict:
# get log sigma
A = np.log(A_ )
# get distribution
A = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
A = np.cumsum((dists >= 0) ,axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
A = low_idx + 1
A = log_sigmas[low_idx]
A = log_sigmas[high_idx]
# interpolate sigmas
A = (low - log_sigma) / (low - high)
A = np.clip(A_ ,0 ,1 )
# transform interpolation to time range
A = (1 - w) * low_idx + w * high_idx
A = t.reshape(sigma.shape )
return t
def _SCREAMING_SNAKE_CASE ( self : List[str] ,A_ : torch.FloatTensor ,A_ : int ) -> torch.FloatTensor:
A = in_sigmas[-1].item()
A = in_sigmas[0].item()
A = 7.0 # 7.0 is the value used in the paper
A = np.linspace(0 ,1 ,A_ )
A = sigma_min ** (1 / rho)
A = sigma_max ** (1 / rho)
A = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
@property
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Dict:
return self.dt is None
def _SCREAMING_SNAKE_CASE ( self : Tuple ,A_ : Union[torch.FloatTensor, np.ndarray] ,A_ : Union[float, torch.FloatTensor] ,A_ : Union[torch.FloatTensor, np.ndarray] ,A_ : bool = True ,) -> Union[SchedulerOutput, Tuple]:
A = self.index_for_timestep(A_ )
# advance index counter by 1
A = timestep.cpu().item() if torch.is_tensor(A_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
A = self.sigmas[step_index]
A = self.sigmas[step_index + 1]
else:
# 2nd order / Heun's method
A = self.sigmas[step_index - 1]
A = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
A = 0
A = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
A = sigma_hat if self.state_in_first_order else sigma_next
A = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
A = sigma_hat if self.state_in_first_order else sigma_next
A = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
A = model_output
else:
raise ValueError(
F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`' )
if self.config.clip_sample:
A = pred_original_sample.clamp(
-self.config.clip_sample_range ,self.config.clip_sample_range )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
A = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
A = sigma_next - sigma_hat
# store for 2nd order step
A = derivative
A = dt
A = sample
else:
# 2. 2nd order / Heun's method
A = (sample - pred_original_sample) / sigma_next
A = (self.prev_derivative + derivative) / 2
# 3. take prev timestep & sample
A = self.dt
A = self.sample
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
A = None
A = None
A = None
A = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=A_ )
def _SCREAMING_SNAKE_CASE ( self : int ,A_ : torch.FloatTensor ,A_ : torch.FloatTensor ,A_ : torch.FloatTensor ,) -> torch.FloatTensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
A = self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(A_ ):
# mps does not support float64
A = self.timesteps.to(original_samples.device ,dtype=torch.floataa )
A = timesteps.to(original_samples.device ,dtype=torch.floataa )
else:
A = self.timesteps.to(original_samples.device )
A = timesteps.to(original_samples.device )
A = [self.index_for_timestep(A_ ,A_ ) for t in timesteps]
A = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
A = sigma.unsqueeze(-1 )
A = original_samples + noise * sigma
return noisy_samples
def __len__( self : Dict ) -> int:
return self.config.num_train_timesteps | 74 | 0 |
import math
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> float:
"""simple docstring"""
if (
not isinstance(__lowerCAmelCase , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('''power_factor must be a valid float value between -1 and 1.''' )
return apparent_power * power_factor
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> float:
"""simple docstring"""
if (
not isinstance(__lowerCAmelCase , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('''power_factor must be a valid float value between -1 and 1.''' )
return apparent_power * math.sqrt(1 - power_factor**2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 |
def _lowerCAmelCase ( __lowerCAmelCase = 50 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 44 | 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
| 127 |
"""simple docstring"""
lowerCAmelCase__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = set()
# keep track of all the paths to be checked
UpperCamelCase = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
UpperCamelCase = queue.pop(0 )
# get the last node from the path
UpperCamelCase = path[-1]
if node not in explored:
UpperCamelCase = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
UpperCamelCase = list(_SCREAMING_SNAKE_CASE )
new_path.append(_SCREAMING_SNAKE_CASE )
queue.append(_SCREAMING_SNAKE_CASE )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(_SCREAMING_SNAKE_CASE )
# in case there's no path between the 2 nodes
return []
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
UpperCamelCase = [start]
UpperCamelCase = set(_SCREAMING_SNAKE_CASE )
# Keep tab on distances from `start` node.
UpperCamelCase = {start: 0, target: -1}
while queue:
UpperCamelCase = queue.pop(0 )
if node == target:
UpperCamelCase = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(_SCREAMING_SNAKE_CASE )
queue.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 153 | 0 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load
try:
import torch
_a : int= True
except ImportError:
_a : List[Any]= False
try:
from torch.hub import _get_torch_home
_a : Tuple= _get_torch_home()
except ImportError:
_a : Optional[int]= os.path.expanduser(
os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
)
_a : Optional[Any]= os.path.join(torch_cache_home, "transformers")
_a : int= "https://cdn.huggingface.co"
_a : Dict= "https://s3.amazonaws.com/models.huggingface.co/bert"
_a : Dict= "/".join(str(Path(__file__).resolve()).split("/")[:-1])
_a : Dict= os.path.join(PATH, "config.yaml")
_a : str= os.path.join(PATH, "attributes.txt")
_a : str= os.path.join(PATH, "objects.txt")
_a : Optional[int]= os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
_a : List[str]= os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
_a : str= os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
_a : Tuple= "pytorch_model.bin"
_a : Dict= "config.yaml"
def __UpperCAmelCase ( UpperCAmelCase_ : Union[str, Any]=OBJECTS , UpperCAmelCase_ : Tuple=ATTRIBUTES ) -> List[str]:
'''simple docstring'''
__snake_case : str = []
with open(UpperCAmelCase_ ) as f:
for object in f.readlines():
vg_classes.append(object.split(',' )[0].lower().strip() )
__snake_case : List[Any] = []
with open(UpperCAmelCase_ ) as f:
for object in f.readlines():
vg_attrs.append(object.split(',' )[0].lower().strip() )
return vg_classes, vg_attrs
def __UpperCAmelCase ( UpperCAmelCase_ : List[Any] ) -> Any:
'''simple docstring'''
__snake_case : Union[str, Any] = OrderedDict()
with open(UpperCAmelCase_ , 'rb' ) as f:
__snake_case : int = pkl.load(UpperCAmelCase_ )['model']
for k in copy.deepcopy(list(ckp.keys() ) ):
__snake_case : Dict = ckp.pop(UpperCAmelCase_ )
if isinstance(UpperCAmelCase_ , np.ndarray ):
__snake_case : List[str] = torch.tensor(UpperCAmelCase_ )
else:
assert isinstance(UpperCAmelCase_ , torch.tensor ), type(UpperCAmelCase_ )
__snake_case : int = v
return r
class UpperCamelCase :
UpperCAmelCase : List[str] = {}
def __init__(self : List[str] , _A : dict , _A : str = "root" , _A : int=0) -> Dict:
__snake_case : Union[str, Any] = name
__snake_case : int = level
__snake_case : Optional[int] = {}
for k, v in dictionary.items():
if v is None:
raise ValueError()
__snake_case : Optional[Any] = copy.deepcopy(_A)
__snake_case : Any = copy.deepcopy(_A)
if isinstance(_A , _A):
__snake_case : Tuple = Config(_A , name=_A , level=level + 1)
__snake_case : Tuple = v
setattr(self , _A , _A)
__snake_case : Any = d
def __repr__(self : Dict) -> Union[str, Any]:
return str(list((self._pointer.keys())))
def __setattr__(self : int , _A : str , _A : str) -> Optional[Any]:
__snake_case : List[str] = val
__snake_case : Optional[Any] = val
__snake_case : Optional[Any] = key.split('.')
__snake_case : Dict = len(_A) - 1
__snake_case : Union[str, Any] = self._pointer
if len(_A) > 1:
for i, l in enumerate(_A):
if hasattr(self , _A) and isinstance(getattr(self , _A) , _A):
setattr(getattr(self , _A) , '.'.join(levels[i:]) , _A)
if l == last_level:
__snake_case : Optional[Any] = val
else:
__snake_case : Tuple = pointer[l]
def _lowercase (self : Dict) -> Optional[Any]:
return self._pointer
def _lowercase (self : Dict , _A : str , _A : Optional[int]) -> List[Any]:
with open(f"{file_name}" , 'w') as stream:
dump(_A , _A)
def _lowercase (self : Union[str, Any] , _A : Optional[int] , _A : Dict) -> Tuple:
with open(f"{file_name}" , 'w') as stream:
json.dump(_A , _A)
@staticmethod
def _lowercase (_A : int) -> str:
with open(_A) as stream:
__snake_case : Any = load(_A , Loader=_A)
return data
def __str__(self : List[str]) -> Union[str, Any]:
__snake_case : str = ' '
if self._name != "root":
__snake_case : Any = f"{t * (self._level-1)}{self._name}:\n"
else:
__snake_case : Optional[Any] = ''
__snake_case : Tuple = self._level
for i, (k, v) in enumerate(self._pointer.items()):
if isinstance(_A , _A):
r += f"{t * (self._level)}{v}\n"
self._level += 1
else:
r += f"{t * (self._level)}{k}: {v} ({type(_A).__name__})\n"
__snake_case : Tuple = level
return r[:-1]
@classmethod
def _lowercase (cls : Optional[int] , _A : str , **_A : Dict) -> List[str]:
__snake_case , __snake_case : List[str] = cls.get_config_dict(_A , **_A)
return cls(_A)
@classmethod
def _lowercase (cls : List[Any] , _A : str , **_A : List[Any]) -> Dict:
__snake_case : str = kwargs.pop('cache_dir' , _A)
__snake_case : List[Any] = kwargs.pop('force_download' , _A)
__snake_case : List[str] = kwargs.pop('resume_download' , _A)
__snake_case : int = kwargs.pop('proxies' , _A)
__snake_case : Dict = kwargs.pop('local_files_only' , _A)
if os.path.isdir(_A):
__snake_case : Dict = os.path.join(_A , _A)
elif os.path.isfile(_A) or is_remote_url(_A):
__snake_case : Union[str, Any] = pretrained_model_name_or_path
else:
__snake_case : int = hf_bucket_url(_A , filename=_A , use_cdn=_A)
try:
# Load from URL or cache if already cached
__snake_case : Optional[Any] = cached_path(
_A , cache_dir=_A , force_download=_A , proxies=_A , resume_download=_A , local_files_only=_A , )
# Load config dict
if resolved_config_file is None:
raise EnvironmentError
__snake_case : Union[str, Any] = Config.load_yaml(_A)
except EnvironmentError:
__snake_case : List[Any] = 'Can\'t load config for'
raise EnvironmentError(_A)
if resolved_config_file == config_file:
print('loading configuration file from path')
else:
print('loading configuration file cache')
return Config.load_yaml(_A), kwargs
def __UpperCAmelCase ( UpperCAmelCase_ : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[Any] = torch.load('dump.pt' , map_location=in_tensor.device )
__snake_case : List[str] = in_tensor.numpy()
__snake_case : List[str] = out_tensor.numpy()[0]
print(na.shape , na[0, 0, :5] )
print(na.shape , na[0, 0, :5] )
assert np.allclose(UpperCAmelCase_ , UpperCAmelCase_ , rtol=0.01 , atol=0.1 ), (
F"{sum([1 for x in np.isclose(UpperCAmelCase_ , UpperCAmelCase_ , rtol=0.01 , atol=0.1 ).flatten() if x is False] )/len(na.flatten() )*1_00:.4f} %"
" element-wise mismatch"
)
raise Exception('tensors are all good' )
# Hugging face functions below
def __UpperCAmelCase ( UpperCAmelCase_ : Union[str, Any] ) -> Any:
'''simple docstring'''
__snake_case : str = urlparse(UpperCAmelCase_ )
return parsed.scheme in ("http", "https")
def __UpperCAmelCase ( UpperCAmelCase_ : str , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[int]=True ) -> str:
'''simple docstring'''
__snake_case : List[Any] = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
__snake_case : Optional[Any] = '/' not in model_id
if legacy_format:
return F"{endpoint}/{model_id}-{filename}"
else:
return F"{endpoint}/{model_id}/{filename}"
def __UpperCAmelCase ( UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : Optional[Any]=0 , UpperCAmelCase_ : Any=None , ) -> Optional[Any]:
'''simple docstring'''
__snake_case : str = 'python/{}'.format(sys.version.split()[0] )
if _torch_available:
ua += "; torch/{}".format(torch.__version__ )
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
ua += "; " + "; ".join('{}/{}'.format(UpperCAmelCase_ , UpperCAmelCase_ ) for k, v in user_agent.items() )
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
ua += "; " + user_agent
__snake_case : Dict = {'user-agent': ua}
if resume_size > 0:
__snake_case : Optional[int] = 'bytes=%d-' % (resume_size,)
__snake_case : Optional[Any] = requests.get(UpperCAmelCase_ , stream=UpperCAmelCase_ , proxies=UpperCAmelCase_ , headers=UpperCAmelCase_ )
if response.status_code == 4_16: # Range not satisfiable
return
__snake_case : Optional[Any] = response.headers.get('Content-Length' )
__snake_case : Dict = resume_size + int(UpperCAmelCase_ ) if content_length is not None else None
__snake_case : str = tqdm(
unit='B' , unit_scale=UpperCAmelCase_ , total=UpperCAmelCase_ , initial=UpperCAmelCase_ , desc='Downloading' , )
for chunk in response.iter_content(chunk_size=10_24 ):
if chunk: # filter out keep-alive new chunks
progress.update(len(UpperCAmelCase_ ) )
temp_file.write(UpperCAmelCase_ )
progress.close()
def __UpperCAmelCase ( UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : List[Any]=False , UpperCAmelCase_ : List[Any]=None , UpperCAmelCase_ : List[Any]=10 , UpperCAmelCase_ : Union[str, Any]=False , UpperCAmelCase_ : Union[str, Any]=None , UpperCAmelCase_ : List[Any]=False , ) -> List[str]:
'''simple docstring'''
if cache_dir is None:
__snake_case : str = TRANSFORMERS_CACHE
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
__snake_case : Dict = str(UpperCAmelCase_ )
os.makedirs(UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
__snake_case : Optional[Any] = None
if not local_files_only:
try:
__snake_case : int = requests.head(UpperCAmelCase_ , allow_redirects=UpperCAmelCase_ , proxies=UpperCAmelCase_ , timeout=UpperCAmelCase_ )
if response.status_code == 2_00:
__snake_case : Union[str, Any] = response.headers.get('ETag' )
except (EnvironmentError, requests.exceptions.Timeout):
# etag is already None
pass
__snake_case : Optional[Any] = url_to_filename(UpperCAmelCase_ , UpperCAmelCase_ )
# get cache path to put the file
__snake_case : Any = os.path.join(UpperCAmelCase_ , UpperCAmelCase_ )
# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
# try to get the last downloaded one
if etag is None:
if os.path.exists(UpperCAmelCase_ ):
return cache_path
else:
__snake_case : Union[str, Any] = [
file
for file in fnmatch.filter(os.listdir(UpperCAmelCase_ ) , filename + '.*' )
if not file.endswith('.json' ) and not file.endswith('.lock' )
]
if len(UpperCAmelCase_ ) > 0:
return os.path.join(UpperCAmelCase_ , matching_files[-1] )
else:
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise ValueError(
'Cannot find the requested files in the cached path and outgoing traffic has been'
' disabled. To enable model look-ups and downloads online, set \'local_files_only\''
' to False.' )
return None
# From now on, etag is not None.
if os.path.exists(UpperCAmelCase_ ) and not force_download:
return cache_path
# Prevent parallel downloads of the same file with a lock.
__snake_case : str = cache_path + '.lock'
with FileLock(UpperCAmelCase_ ):
# If the download just completed while the lock was activated.
if os.path.exists(UpperCAmelCase_ ) and not force_download:
# Even if returning early like here, the lock will be released.
return cache_path
if resume_download:
__snake_case : int = cache_path + '.incomplete'
@contextmanager
def _resumable_file_manager():
with open(UpperCAmelCase_ , 'a+b' ) as f:
yield f
__snake_case : List[Any] = _resumable_file_manager
if os.path.exists(UpperCAmelCase_ ):
__snake_case : Tuple = os.stat(UpperCAmelCase_ ).st_size
else:
__snake_case : Optional[Any] = 0
else:
__snake_case : str = partial(tempfile.NamedTemporaryFile , dir=UpperCAmelCase_ , delete=UpperCAmelCase_ )
__snake_case : int = 0
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
print(
'%s not found in cache or force_download set to True, downloading to %s' , UpperCAmelCase_ , temp_file.name , )
http_get(
UpperCAmelCase_ , UpperCAmelCase_ , proxies=UpperCAmelCase_ , resume_size=UpperCAmelCase_ , user_agent=UpperCAmelCase_ , )
os.replace(temp_file.name , UpperCAmelCase_ )
__snake_case : List[str] = {'url': url, 'etag': etag}
__snake_case : Any = cache_path + '.json'
with open(UpperCAmelCase_ , 'w' ) as meta_file:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ )
return cache_path
def __UpperCAmelCase ( UpperCAmelCase_ : Any , UpperCAmelCase_ : List[str]=None ) -> List[str]:
'''simple docstring'''
__snake_case : Dict = url.encode('utf-8' )
__snake_case : int = shaaaa(UpperCAmelCase_ )
__snake_case : Dict = url_hash.hexdigest()
if etag:
__snake_case : Tuple = etag.encode('utf-8' )
__snake_case : Optional[Any] = shaaaa(UpperCAmelCase_ )
filename += "." + etag_hash.hexdigest()
if url.endswith('.h5' ):
filename += ".h5"
return filename
def __UpperCAmelCase ( UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : Tuple=False , UpperCAmelCase_ : Tuple=None , UpperCAmelCase_ : str=False , UpperCAmelCase_ : str=None , UpperCAmelCase_ : Tuple=False , UpperCAmelCase_ : Optional[int]=False , UpperCAmelCase_ : List[str]=False , ) -> Optional[int]:
'''simple docstring'''
if cache_dir is None:
__snake_case : Dict = TRANSFORMERS_CACHE
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
__snake_case : List[Any] = str(UpperCAmelCase_ )
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
__snake_case : Optional[Any] = str(UpperCAmelCase_ )
if is_remote_url(UpperCAmelCase_ ):
# URL, so get it from the cache (downloading if necessary)
__snake_case : int = get_from_cache(
UpperCAmelCase_ , cache_dir=UpperCAmelCase_ , force_download=UpperCAmelCase_ , proxies=UpperCAmelCase_ , resume_download=UpperCAmelCase_ , user_agent=UpperCAmelCase_ , local_files_only=UpperCAmelCase_ , )
elif os.path.exists(UpperCAmelCase_ ):
# File, and it exists.
__snake_case : Optional[Any] = url_or_filename
elif urlparse(UpperCAmelCase_ ).scheme == "":
# File, but it doesn't exist.
raise EnvironmentError('file {} not found'.format(UpperCAmelCase_ ) )
else:
# Something unknown
raise ValueError('unable to parse {} as a URL or as a local path'.format(UpperCAmelCase_ ) )
if extract_compressed_file:
if not is_zipfile(UpperCAmelCase_ ) and not tarfile.is_tarfile(UpperCAmelCase_ ):
return output_path
# Path where we extract compressed archives
# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
__snake_case , __snake_case : Optional[Any] = os.path.split(UpperCAmelCase_ )
__snake_case : Union[str, Any] = output_file.replace('.' , '-' ) + '-extracted'
__snake_case : Optional[Any] = os.path.join(UpperCAmelCase_ , UpperCAmelCase_ )
if os.path.isdir(UpperCAmelCase_ ) and os.listdir(UpperCAmelCase_ ) and not force_extract:
return output_path_extracted
# Prevent parallel extractions
__snake_case : Optional[Any] = output_path + '.lock'
with FileLock(UpperCAmelCase_ ):
shutil.rmtree(UpperCAmelCase_ , ignore_errors=UpperCAmelCase_ )
os.makedirs(UpperCAmelCase_ )
if is_zipfile(UpperCAmelCase_ ):
with ZipFile(UpperCAmelCase_ , 'r' ) as zip_file:
zip_file.extractall(UpperCAmelCase_ )
zip_file.close()
elif tarfile.is_tarfile(UpperCAmelCase_ ):
__snake_case : Dict = tarfile.open(UpperCAmelCase_ )
tar_file.extractall(UpperCAmelCase_ )
tar_file.close()
else:
raise EnvironmentError('Archive format of {} could not be identified'.format(UpperCAmelCase_ ) )
return output_path_extracted
return output_path
def __UpperCAmelCase ( UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Any="," ) -> Optional[Any]:
'''simple docstring'''
assert isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
if os.path.isfile(UpperCAmelCase_ ):
with open(UpperCAmelCase_ ) as f:
__snake_case : Dict = eval(f.read() )
else:
__snake_case : str = requests.get(UpperCAmelCase_ )
try:
__snake_case : Dict = requests.json()
except Exception:
__snake_case : int = req.content.decode()
assert data is not None, "could not connect"
try:
__snake_case : str = eval(UpperCAmelCase_ )
except Exception:
__snake_case : Union[str, Any] = data.split('\n' )
req.close()
return data
def __UpperCAmelCase ( UpperCAmelCase_ : Optional[int] ) -> List[str]:
'''simple docstring'''
__snake_case : Any = requests.get(UpperCAmelCase_ )
__snake_case : int = np.array(Image.open(BytesIO(response.content ) ) )
return img
def __UpperCAmelCase ( UpperCAmelCase_ : str ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Dict = url.split('/' )[-1]
if fn not in os.listdir(os.getcwd() ):
wget.download(UpperCAmelCase_ )
with open(UpperCAmelCase_ , 'rb' ) as stream:
__snake_case : str = pkl.load(UpperCAmelCase_ )
__snake_case : Optional[int] = weights.pop('model' )
__snake_case : List[Any] = {}
for k, v in model.items():
__snake_case : int = torch.from_numpy(UpperCAmelCase_ )
if "running_var" in k:
__snake_case : int = torch.tensor([0] )
__snake_case : int = k.replace('running_var' , 'num_batches_tracked' )
__snake_case : Optional[int] = zero
return new
def __UpperCAmelCase ( ) -> Optional[int]:
'''simple docstring'''
print(F"{os.path.abspath(os.path.join(UpperCAmelCase_ , os.pardir ) )}/demo.ipynb" )
def __UpperCAmelCase ( UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : int="RGB" ) -> List[Any]:
'''simple docstring'''
assert isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
if os.path.isfile(UpperCAmelCase_ ):
__snake_case : int = cva.imread(UpperCAmelCase_ )
else:
__snake_case : Union[str, Any] = get_image_from_url(UpperCAmelCase_ )
assert img is not None, F"could not connect to: {im}"
__snake_case : str = cva.cvtColor(UpperCAmelCase_ , cva.COLOR_BGR2RGB )
if input_format == "RGB":
__snake_case : List[str] = img[:, :, ::-1]
return img
def __UpperCAmelCase ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[Any]=1 ) -> List[Any]:
'''simple docstring'''
return (images[i : i + batch] for i in range(0 , len(UpperCAmelCase_ ) , UpperCAmelCase_ ))
| 95 | """simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCamelCase ( lowercase ):
UpperCAmelCase : Optional[int] = """new-model"""
if is_tf_available():
class UpperCamelCase ( lowercase ):
UpperCAmelCase : List[str] = NewModelConfig
@require_tf
class UpperCamelCase ( unittest.TestCase ):
@slow
def _lowercase (self : List[str]) -> Dict:
__snake_case : Any = 'bert-base-cased'
__snake_case : Optional[Any] = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : Union[str, Any] = TFAutoModel.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : List[Any]) -> str:
__snake_case : Optional[int] = 'bert-base-cased'
__snake_case : List[Any] = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : Dict = TFAutoModelForPreTraining.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : Any) -> List[str]:
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : str = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : List[str] = TFAutoModelForCausalLM.from_pretrained(_A)
__snake_case , __snake_case : List[str] = TFAutoModelForCausalLM.from_pretrained(_A , output_loading_info=_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : Tuple) -> Dict:
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : Union[str, Any] = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : Union[str, Any] = TFAutoModelWithLMHead.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : Union[str, Any]) -> Optional[int]:
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : Union[str, Any] = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : Optional[Any] = TFAutoModelForMaskedLM.from_pretrained(_A)
__snake_case , __snake_case : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(_A , output_loading_info=_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : str) -> Union[str, Any]:
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : Dict = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : str = TFAutoModelForSeqaSeqLM.from_pretrained(_A)
__snake_case , __snake_case : Union[str, Any] = TFAutoModelForSeqaSeqLM.from_pretrained(_A , output_loading_info=_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : str) -> str:
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__snake_case : Tuple = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : Tuple = TFAutoModelForSequenceClassification.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : Optional[Any]) -> Optional[int]:
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__snake_case : List[str] = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : Any = TFAutoModelForQuestionAnswering.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
@require_tensorflow_probability
def _lowercase (self : List[Any]) -> List[str]:
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
__snake_case : Optional[Any] = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : int = TFAutoModelForTableQuestionAnswering.from_pretrained(_A)
__snake_case , __snake_case : Dict = TFAutoModelForTableQuestionAnswering.from_pretrained(
_A , output_loading_info=_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
def _lowercase (self : Optional[Any]) -> Optional[Any]:
__snake_case : Optional[int] = TFAutoModelWithLMHead.from_pretrained(_A)
self.assertIsInstance(_A , _A)
self.assertEqual(model.num_parameters() , 1_44_10)
self.assertEqual(model.num_parameters(only_trainable=_A) , 1_44_10)
def _lowercase (self : Any) -> List[str]:
__snake_case : List[Any] = TFAutoModelWithLMHead.from_pretrained(_A)
self.assertIsInstance(_A , _A)
self.assertEqual(model.num_parameters() , 1_44_10)
self.assertEqual(model.num_parameters(only_trainable=_A) , 1_44_10)
def _lowercase (self : Optional[Any]) -> str:
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
__snake_case : Optional[Any] = TFAutoModel.from_pretrained('sgugger/funnel-random-tiny')
self.assertIsInstance(_A , _A)
__snake_case : int = copy.deepcopy(model.config)
__snake_case : int = ['FunnelBaseModel']
__snake_case : int = TFAutoModel.from_config(_A)
self.assertIsInstance(_A , _A)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(_A)
__snake_case : List[Any] = TFAutoModel.from_pretrained(_A)
self.assertIsInstance(_A , _A)
def _lowercase (self : List[Any]) -> int:
try:
AutoConfig.register('new-model' , _A)
__snake_case : int = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__):
# Wrong config class will raise an error
with self.assertRaises(_A):
auto_class.register(_A , _A)
auto_class.register(_A , _A)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_A):
auto_class.register(_A , _A)
# Now that the config is registered, it can be used as any other config with the auto-API
__snake_case : Union[str, Any] = BertModelTester(self).get_config()
__snake_case : Optional[int] = NewModelConfig(**tiny_config.to_dict())
__snake_case : List[str] = auto_class.from_config(_A)
self.assertIsInstance(_A , _A)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(_A)
__snake_case : Tuple = auto_class.from_pretrained(_A)
self.assertIsInstance(_A , _A)
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def _lowercase (self : Optional[int]) -> Union[str, Any]:
with self.assertRaisesRegex(
_A , 'bert-base is not a local folder and is not a valid model identifier'):
__snake_case : Any = TFAutoModel.from_pretrained('bert-base')
def _lowercase (self : str) -> str:
with self.assertRaisesRegex(
_A , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
__snake_case : Optional[Any] = TFAutoModel.from_pretrained(_A , revision='aaaaaa')
def _lowercase (self : int) -> Any:
with self.assertRaisesRegex(
_A , 'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin' , ):
__snake_case : List[str] = TFAutoModel.from_pretrained('hf-internal-testing/config-no-model')
def _lowercase (self : Optional[Any]) -> Any:
with self.assertRaisesRegex(_A , 'Use `from_pt=True` to load this model'):
__snake_case : List[str] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only')
def _lowercase (self : str) -> Any:
# Make sure we have cached the model.
__snake_case : str = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert')
with RequestCounter() as counter:
__snake_case : List[str] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert')
self.assertEqual(counter.get_request_count , 0)
self.assertEqual(counter.head_request_count , 1)
self.assertEqual(counter.other_request_count , 0)
# With a sharded checkpoint
__snake_case : Optional[int] = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded')
with RequestCounter() as counter:
__snake_case : Any = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded')
self.assertEqual(counter.get_request_count , 0)
self.assertEqual(counter.head_request_count , 1)
self.assertEqual(counter.other_request_count , 0)
| 95 | 1 |
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