code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
_a = 100
_a = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_a = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def __a ( __lowerCamelCase ):
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
UpperCAmelCase_ : Dict = set()
UpperCAmelCase_ : Optional[Any] = 42
UpperCAmelCase_ : Optional[int] = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def __a ( __lowerCamelCase = 5000 ):
for number_to_partition in range(1, lowerCAmelCase__ ):
if len(partition(lowerCAmelCase__ ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(f"""{solution() = }""")
| 61 | """simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class _A :
def A__ ( self , __lowerCAmelCase ):
"""simple docstring"""
raise NotImplementedError()
def A__ ( self ):
"""simple docstring"""
raise NotImplementedError()
class _A ( lowerCAmelCase ):
def __init__( self , __lowerCAmelCase , __lowerCAmelCase = False , **__lowerCAmelCase ):
"""simple docstring"""
lowercase = tokenizer
lowercase = skip_prompt
lowercase = decode_kwargs
# variables used in the streaming process
lowercase = []
lowercase = 0
lowercase = True
def A__ ( self , __lowerCAmelCase ):
"""simple docstring"""
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError("""TextStreamer only supports batch size 1""" )
elif len(value.shape ) > 1:
lowercase = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
lowercase = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
lowercase = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith("""\n""" ):
lowercase = text[self.print_len :]
lowercase = []
lowercase = 0
# If the last token is a CJK character, we print the characters.
elif len(__lowerCAmelCase ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
lowercase = text[self.print_len :]
self.print_len += len(__lowerCAmelCase )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
lowercase = text[self.print_len : text.rfind(""" """ ) + 1]
self.print_len += len(__lowerCAmelCase )
self.on_finalized_text(__lowerCAmelCase )
def A__ ( self ):
"""simple docstring"""
if len(self.token_cache ) > 0:
lowercase = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
lowercase = text[self.print_len :]
lowercase = []
lowercase = 0
else:
lowercase = """"""
lowercase = True
self.on_finalized_text(__lowerCAmelCase , stream_end=__lowerCAmelCase )
def A__ ( self , __lowerCAmelCase , __lowerCAmelCase = False ):
"""simple docstring"""
print(__lowerCAmelCase , flush=__lowerCAmelCase , end="""""" if not stream_end else None )
def A__ ( self , __lowerCAmelCase ):
"""simple docstring"""
if (
(cp >= 0X4_e00 and cp <= 0X9_fff)
or (cp >= 0X3_400 and cp <= 0X4_dbf) #
or (cp >= 0X20_000 and cp <= 0X2a_6df) #
or (cp >= 0X2a_700 and cp <= 0X2b_73f) #
or (cp >= 0X2b_740 and cp <= 0X2b_81f) #
or (cp >= 0X2b_820 and cp <= 0X2c_eaf) #
or (cp >= 0Xf_900 and cp <= 0Xf_aff)
or (cp >= 0X2f_800 and cp <= 0X2f_a1f) #
): #
return True
return False
class _A ( lowerCAmelCase ):
def __init__( self , __lowerCAmelCase , __lowerCAmelCase = False , __lowerCAmelCase = None , **__lowerCAmelCase ):
"""simple docstring"""
super().__init__(__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
lowercase = Queue()
lowercase = None
lowercase = timeout
def A__ ( self , __lowerCAmelCase , __lowerCAmelCase = False ):
"""simple docstring"""
self.text_queue.put(__lowerCAmelCase , timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal , timeout=self.timeout )
def __iter__( self ):
"""simple docstring"""
return self
def A__ ( self ):
"""simple docstring"""
lowercase = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 197 | 0 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class __snake_case ( lowerCAmelCase ):
def __init__( self ,snake_case ,snake_case ,snake_case ,snake_case ,):
'''simple docstring'''
super().__init__()
lowercase : int = value_function
lowercase : Union[str, Any] = unet
lowercase : Tuple = scheduler
lowercase : int = env
lowercase : str = env.get_dataset()
lowercase : int = {}
for key in self.data.keys():
try:
lowercase : Dict = self.data[key].mean()
except: # noqa: E722
pass
lowercase : Optional[int] = {}
for key in self.data.keys():
try:
lowercase : Tuple = self.data[key].std()
except: # noqa: E722
pass
lowercase : int = env.observation_space.shape[0]
lowercase : Optional[int] = env.action_space.shape[0]
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case ):
'''simple docstring'''
return (x_in - self.means[key]) / self.stds[key]
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case ):
'''simple docstring'''
return x_in * self.stds[key] + self.means[key]
def _SCREAMING_SNAKE_CASE ( self ,snake_case ):
'''simple docstring'''
if type(snake_case ) is dict:
return {k: self.to_torch(snake_case ) for k, v in x_in.items()}
elif torch.is_tensor(snake_case ):
return x_in.to(self.unet.device )
return torch.tensor(snake_case ,device=self.unet.device )
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case ,snake_case ):
'''simple docstring'''
for key, val in cond.items():
lowercase : str = val.clone()
return x_in
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case ,snake_case ,snake_case ):
'''simple docstring'''
lowercase : str = x.shape[0]
lowercase : Optional[int] = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
lowercase : Tuple = torch.full((batch_size,) ,snake_case ,device=self.unet.device ,dtype=torch.long )
for _ in range(snake_case ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
lowercase : int = self.value_function(x.permute(0 ,2 ,1 ) ,snake_case ).sample
lowercase : List[Any] = torch.autograd.grad([y.sum()] ,[x] )[0]
lowercase : int = self.scheduler._get_variance(snake_case )
lowercase : Any = torch.exp(0.5 * posterior_variance )
lowercase : List[Any] = model_std * grad
lowercase : Optional[Any] = 0
lowercase : List[str] = x.detach()
lowercase : Optional[Any] = x + scale * grad
lowercase : Optional[int] = self.reset_xa(snake_case ,snake_case ,self.action_dim )
lowercase : Optional[Any] = self.unet(x.permute(0 ,2 ,1 ) ,snake_case ).sample.permute(0 ,2 ,1 )
# TODO: verify deprecation of this kwarg
lowercase : Optional[Any] = self.scheduler.step(snake_case ,snake_case ,snake_case ,predict_epsilon=snake_case )["""prev_sample"""]
# apply conditions to the trajectory (set the initial state)
lowercase : int = self.reset_xa(snake_case ,snake_case ,self.action_dim )
lowercase : Dict = self.to_torch(snake_case )
return x, y
def __call__( self ,snake_case ,snake_case=64 ,snake_case=32 ,snake_case=2 ,snake_case=0.1 ):
'''simple docstring'''
lowercase : List[Any] = self.normalize(snake_case ,"""observations""" )
lowercase : List[str] = obs[None].repeat(snake_case ,axis=0 )
lowercase : List[Any] = {0: self.to_torch(snake_case )}
lowercase : Tuple = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
lowercase : Dict = randn_tensor(snake_case ,device=self.unet.device )
lowercase : Optional[Any] = self.reset_xa(snake_case ,snake_case ,self.action_dim )
lowercase : str = self.to_torch(snake_case )
# run the diffusion process
lowercase , lowercase : Any = self.run_diffusion(snake_case ,snake_case ,snake_case ,snake_case )
# sort output trajectories by value
lowercase : Dict = y.argsort(0 ,descending=snake_case ).squeeze()
lowercase : Any = x[sorted_idx]
lowercase : int = sorted_values[:, :, : self.action_dim]
lowercase : List[Any] = actions.detach().cpu().numpy()
lowercase : List[str] = self.de_normalize(snake_case ,key="""actions""" )
# select the action with the highest value
if y is not None:
lowercase : Tuple = 0
else:
# if we didn't run value guiding, select a random action
lowercase : int = np.random.randint(0 ,snake_case )
lowercase : Optional[Any] = denorm_actions[selected_index, 0]
return denorm_actions
| 285 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
lowercase : Tuple = get_logger(__name__)
lowercase : Optional[int] = Path(__file__).parent / """model_card_template.md"""
lowercase : Dict = uuida().hex
lowercase : Tuple = os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
lowercase : str = os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
lowercase : Tuple = HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""
def _snake_case( SCREAMING_SNAKE_CASE__ = None ) -> str:
lowercase : str = f"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += f"; torch/{_torch_version}"
if is_flax_available():
ua += f"; jax/{_jax_version}"
ua += f"; flax/{_flax_version}"
if is_onnx_available():
ua += f"; onnxruntime/{_onnxruntime_version}"
# CI will set this value to True
if os.environ.get("""DIFFUSERS_IS_CI""" , """""" ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items() )
elif isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
ua += "; " + user_agent
return ua
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None ) -> Dict:
if token is None:
lowercase : Optional[int] = HfFolder.get_token()
if organization is None:
lowercase : int = whoami(SCREAMING_SNAKE_CASE__ )["""name"""]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Tuple:
if not is_jinja_available():
raise ValueError(
"""Modelcard rendering is based on Jinja templates."""
""" Please make sure to have `jinja` installed before using `create_model_card`."""
""" To install it, please run `pip install Jinja2`.""" )
if hasattr(SCREAMING_SNAKE_CASE__ , """local_rank""" ) and args.local_rank not in [-1, 0]:
return
lowercase : str = args.hub_token if hasattr(SCREAMING_SNAKE_CASE__ , """hub_token""" ) else None
lowercase : int = get_full_repo_name(SCREAMING_SNAKE_CASE__ , token=SCREAMING_SNAKE_CASE__ )
lowercase : Dict = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language="""en""" , license="""apache-2.0""" , library_name="""diffusers""" , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=SCREAMING_SNAKE_CASE__ , model_name=SCREAMING_SNAKE_CASE__ , repo_name=SCREAMING_SNAKE_CASE__ , dataset_name=args.dataset_name if hasattr(SCREAMING_SNAKE_CASE__ , """dataset_name""" ) else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(SCREAMING_SNAKE_CASE__ , """gradient_accumulation_steps""" ) else None
) , adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__ , """adam_beta1""" ) else None , adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__ , """adam_beta2""" ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(SCREAMING_SNAKE_CASE__ , """adam_weight_decay""" ) else None , adam_epsilon=args.adam_epsilon if hasattr(SCREAMING_SNAKE_CASE__ , """adam_epsilon""" ) else None , lr_scheduler=args.lr_scheduler if hasattr(SCREAMING_SNAKE_CASE__ , """lr_scheduler""" ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(SCREAMING_SNAKE_CASE__ , """lr_warmup_steps""" ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(SCREAMING_SNAKE_CASE__ , """ema_inv_gamma""" ) else None , ema_power=args.ema_power if hasattr(SCREAMING_SNAKE_CASE__ , """ema_power""" ) else None , ema_max_decay=args.ema_max_decay if hasattr(SCREAMING_SNAKE_CASE__ , """ema_max_decay""" ) else None , mixed_precision=args.mixed_precision , )
lowercase : str = os.path.join(args.output_dir , """README.md""" )
model_card.save(SCREAMING_SNAKE_CASE__ )
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ) -> Optional[Any]:
if resolved_file is None or commit_hash is not None:
return commit_hash
lowercase : List[Any] = str(Path(SCREAMING_SNAKE_CASE__ ).as_posix() )
lowercase : Any = re.search(R"""snapshots/([^/]+)/""" , SCREAMING_SNAKE_CASE__ )
if search is None:
return None
lowercase : List[Any] = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_SNAKE_CASE__ ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
lowercase : Optional[Any] = os.path.expanduser(
os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
lowercase : Optional[int] = os.path.join(hf_cache_home, """diffusers""")
def _snake_case( SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None ) -> None:
if new_cache_dir is None:
lowercase : Union[str, Any] = DIFFUSERS_CACHE
if old_cache_dir is None:
lowercase : List[str] = old_diffusers_cache
lowercase : Dict = Path(SCREAMING_SNAKE_CASE__ ).expanduser()
lowercase : int = Path(SCREAMING_SNAKE_CASE__ ).expanduser()
for old_blob_path in old_cache_dir.glob("""**/blobs/*""" ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
lowercase : Any = new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE__ )
new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
os.replace(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
try:
os.symlink(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
except OSError:
logger.warning(
"""Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.""" )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
lowercase : Dict = os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
lowercase : Any = 0
else:
with open(cache_version_file) as f:
try:
lowercase : List[Any] = int(f.read())
except ValueError:
lowercase : int = 0
if cache_version < 1:
lowercase : Union[str, Any] = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
"""The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your """
"""existing cached models. This is a one-time operation, you can interrupt it or run it """
"""later by calling `diffusers.utils.hub_utils.move_cache()`."""
)
try:
move_cache()
except Exception as e:
lowercase : int = """\n""".join(traceback.format_tb(e.__traceback__))
logger.error(
F'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
"""file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole """
"""message and we will do our best to help."""
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, """w""") as f:
f.write("""1""")
except Exception:
logger.warning(
F'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
"""the directory exists and can be written to."""
)
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ) -> str:
if variant is not None:
lowercase : List[str] = weights_name.split(""".""" )
lowercase : Optional[Any] = splits[:-1] + [variant] + splits[-1:]
lowercase : int = """.""".join(SCREAMING_SNAKE_CASE__ )
return weights_name
def _snake_case( SCREAMING_SNAKE_CASE__ , *,
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None , ) -> Optional[Any]:
lowercase : Optional[int] = str(SCREAMING_SNAKE_CASE__ )
if os.path.isfile(SCREAMING_SNAKE_CASE__ ):
return pretrained_model_name_or_path
elif os.path.isdir(SCREAMING_SNAKE_CASE__ ):
if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) ):
# Load from a PyTorch checkpoint
lowercase : List[Any] = os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) ):
lowercase : Any = os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return model_file
else:
raise EnvironmentError(
f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}." )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(SCREAMING_SNAKE_CASE__ ).base_version ) >= version.parse("""0.20.0""" )
):
try:
lowercase : Any = hf_hub_download(
SCREAMING_SNAKE_CASE__ , filename=_add_variant(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , cache_dir=SCREAMING_SNAKE_CASE__ , force_download=SCREAMING_SNAKE_CASE__ , proxies=SCREAMING_SNAKE_CASE__ , resume_download=SCREAMING_SNAKE_CASE__ , local_files_only=SCREAMING_SNAKE_CASE__ , use_auth_token=SCREAMING_SNAKE_CASE__ , user_agent=SCREAMING_SNAKE_CASE__ , subfolder=SCREAMING_SNAKE_CASE__ , revision=revision or commit_hash , )
warnings.warn(
f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead." , SCREAMING_SNAKE_CASE__ , )
return model_file
except: # noqa: E722
warnings.warn(
f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )}' so that the correct variant file can be added." , SCREAMING_SNAKE_CASE__ , )
try:
# 2. Load model file as usual
lowercase : int = hf_hub_download(
SCREAMING_SNAKE_CASE__ , filename=SCREAMING_SNAKE_CASE__ , cache_dir=SCREAMING_SNAKE_CASE__ , force_download=SCREAMING_SNAKE_CASE__ , proxies=SCREAMING_SNAKE_CASE__ , resume_download=SCREAMING_SNAKE_CASE__ , local_files_only=SCREAMING_SNAKE_CASE__ , use_auth_token=SCREAMING_SNAKE_CASE__ , user_agent=SCREAMING_SNAKE_CASE__ , subfolder=SCREAMING_SNAKE_CASE__ , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
"""listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a """
"""token having permission to this repo with `use_auth_token` or log in with `huggingface-cli """
"""login`.""" )
except RevisionNotFoundError:
raise EnvironmentError(
f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
"""this model name. Check the model page at """
f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions." )
except EntryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}." )
except HTTPError as err:
raise EnvironmentError(
f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}" )
except ValueError:
raise EnvironmentError(
f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
f" directory containing a file named {weights_name} or"
""" \nCheckout your internet connection or see how to run the library in"""
""" offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'.""" )
except EnvironmentError:
raise EnvironmentError(
f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
"""'https://huggingface.co/models', make sure you don't have a local directory with the same name. """
f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
f"containing a file named {weights_name}" )
| 285 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
'configuration_nllb_moe': [
'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
'NllbMoeConfig',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
'NllbMoeForConditionalGeneration',
'NllbMoeModel',
'NllbMoePreTrainedModel',
'NllbMoeTop2Router',
'NllbMoeSparseMLP',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 205 |
def a ( A__ : int = 1000000 ) -> int:
"""simple docstring"""
_lowercase =1
_lowercase =1
_lowercase ={1: 1}
for inputa in range(2 , A__ ):
_lowercase =0
_lowercase =inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
_lowercase =(3 * number) + 1
counter += 1
if inputa not in counters:
_lowercase =counter
if counter > pre_counter:
_lowercase =inputa
_lowercase =counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 205 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''openai/whisper-base''': '''https://huggingface.co/openai/whisper-base/resolve/main/config.json''',
}
# fmt: off
lowerCAmelCase__ = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
lowerCAmelCase__ = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
lowercase_ = """whisper"""
lowercase_ = ["""past_key_values"""]
lowercase_ = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : List[str]=51_865 , SCREAMING_SNAKE_CASE : Tuple=80 , SCREAMING_SNAKE_CASE : Tuple=6 , SCREAMING_SNAKE_CASE : Tuple=4 , SCREAMING_SNAKE_CASE : Any=6 , SCREAMING_SNAKE_CASE : Union[str, Any]=4 , SCREAMING_SNAKE_CASE : int=1_536 , SCREAMING_SNAKE_CASE : Optional[int]=1_536 , SCREAMING_SNAKE_CASE : List[Any]=0.0 , SCREAMING_SNAKE_CASE : Any=0.0 , SCREAMING_SNAKE_CASE : Any=50_257 , SCREAMING_SNAKE_CASE : Tuple=True , SCREAMING_SNAKE_CASE : List[Any]=True , SCREAMING_SNAKE_CASE : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE : Optional[Any]=256 , SCREAMING_SNAKE_CASE : Union[str, Any]=0.0 , SCREAMING_SNAKE_CASE : int=0.0 , SCREAMING_SNAKE_CASE : Tuple=0.0 , SCREAMING_SNAKE_CASE : Tuple=0.02 , SCREAMING_SNAKE_CASE : int=False , SCREAMING_SNAKE_CASE : Any=1_500 , SCREAMING_SNAKE_CASE : Tuple=448 , SCREAMING_SNAKE_CASE : Any=50_256 , SCREAMING_SNAKE_CASE : List[Any]=50_256 , SCREAMING_SNAKE_CASE : Optional[Any]=50_256 , SCREAMING_SNAKE_CASE : str=None , SCREAMING_SNAKE_CASE : List[str]=[220, 50_256] , SCREAMING_SNAKE_CASE : Union[str, Any]=False , SCREAMING_SNAKE_CASE : Dict=256 , SCREAMING_SNAKE_CASE : str=False , SCREAMING_SNAKE_CASE : Tuple=0.05 , SCREAMING_SNAKE_CASE : int=10 , SCREAMING_SNAKE_CASE : str=2 , SCREAMING_SNAKE_CASE : Tuple=0.0 , SCREAMING_SNAKE_CASE : Optional[int]=10 , SCREAMING_SNAKE_CASE : Tuple=0 , SCREAMING_SNAKE_CASE : Tuple=7 , **SCREAMING_SNAKE_CASE : List[str] , ):
lowercase__ : Tuple = vocab_size
lowercase__ : Optional[Any] = num_mel_bins
lowercase__ : List[str] = d_model
lowercase__ : int = encoder_layers
lowercase__ : Union[str, Any] = encoder_attention_heads
lowercase__ : Any = decoder_layers
lowercase__ : Tuple = decoder_attention_heads
lowercase__ : Any = decoder_ffn_dim
lowercase__ : Dict = encoder_ffn_dim
lowercase__ : Optional[int] = dropout
lowercase__ : Union[str, Any] = attention_dropout
lowercase__ : Tuple = activation_dropout
lowercase__ : Any = activation_function
lowercase__ : Any = init_std
lowercase__ : int = encoder_layerdrop
lowercase__ : int = decoder_layerdrop
lowercase__ : Any = use_cache
lowercase__ : Optional[Any] = encoder_layers
lowercase__ : List[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
lowercase__ : List[str] = max_source_positions
lowercase__ : str = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
lowercase__ : Any = classifier_proj_size
lowercase__ : Any = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase__ : Tuple = apply_spec_augment
lowercase__ : Union[str, Any] = mask_time_prob
lowercase__ : Optional[Any] = mask_time_length
lowercase__ : str = mask_time_min_masks
lowercase__ : Optional[Any] = mask_feature_prob
lowercase__ : Optional[int] = mask_feature_length
lowercase__ : str = mask_feature_min_masks
lowercase__ : Tuple = median_filter_width
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE , bos_token_id=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE , is_encoder_decoder=SCREAMING_SNAKE_CASE , decoder_start_token_id=SCREAMING_SNAKE_CASE , suppress_tokens=SCREAMING_SNAKE_CASE , begin_suppress_tokens=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
@property
def snake_case ( self : Union[str, Any] ):
lowercase__ : str = OrderedDict(
[
("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
] )
if self.use_past:
lowercase__ : List[Any] = {0: "batch"}
else:
lowercase__ : Union[str, Any] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE , direction="inputs" )
return common_inputs
def snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , SCREAMING_SNAKE_CASE : int = -1 , SCREAMING_SNAKE_CASE : int = -1 , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : Optional["TensorType"] = None , SCREAMING_SNAKE_CASE : int = 22_050 , SCREAMING_SNAKE_CASE : float = 5.0 , SCREAMING_SNAKE_CASE : int = 220 , ):
lowercase__ : Optional[Any] = OrderedDict()
lowercase__ : Optional[int] = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=SCREAMING_SNAKE_CASE , framework=SCREAMING_SNAKE_CASE , sampling_rate=SCREAMING_SNAKE_CASE , time_duration=SCREAMING_SNAKE_CASE , frequency=SCREAMING_SNAKE_CASE , )
lowercase__ : str = encoder_inputs["input_features"].shape[2]
lowercase__ : Tuple = encoder_sequence_length // 2 if self.use_past else seq_length
lowercase__ : Optional[Any] = super().generate_dummy_inputs(
preprocessor.tokenizer , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = encoder_inputs.pop("input_features" )
lowercase__ : Dict = decoder_inputs.pop("decoder_input_ids" )
if "past_key_values" in decoder_inputs:
lowercase__ : Union[str, Any] = decoder_inputs.pop("past_key_values" )
return dummy_inputs
@property
def snake_case ( self : int ):
return 1E-3
| 369 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class snake_case__(unittest.TestCase ):
"""simple docstring"""
@property
def snake_case ( self : Any ):
torch.manual_seed(0 )
lowercase__ : Tuple = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
@property
def snake_case ( self : List[str] ):
torch.manual_seed(0 )
lowercase__ : Optional[int] = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=3 , )
return model
@property
def snake_case ( self : Dict ):
torch.manual_seed(0 )
lowercase__ : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModel(SCREAMING_SNAKE_CASE )
def snake_case ( self : str ):
lowercase__ : Any = self.dummy_uncond_unet
lowercase__ : Dict = DDIMScheduler()
lowercase__ : Optional[Any] = self.dummy_vq_model
lowercase__ : Union[str, Any] = LDMPipeline(unet=SCREAMING_SNAKE_CASE , vqvae=SCREAMING_SNAKE_CASE , scheduler=SCREAMING_SNAKE_CASE )
ldm.to(SCREAMING_SNAKE_CASE )
ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
lowercase__ : int = torch.manual_seed(0 )
lowercase__ : Optional[int] = ldm(generator=SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="numpy" ).images
lowercase__ : str = torch.manual_seed(0 )
lowercase__ : List[Any] = ldm(generator=SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="numpy" , return_dict=SCREAMING_SNAKE_CASE )[0]
lowercase__ : Any = image[0, -3:, -3:, -1]
lowercase__ : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase__ : List[Any] = np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172] )
lowercase__ : Optional[Any] = 1E-2 if torch_device != "mps" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class snake_case__(unittest.TestCase ):
"""simple docstring"""
def snake_case ( self : Optional[Any] ):
lowercase__ : int = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256" )
ldm.to(SCREAMING_SNAKE_CASE )
ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
lowercase__ : Dict = torch.manual_seed(0 )
lowercase__ : Tuple = ldm(generator=SCREAMING_SNAKE_CASE , num_inference_steps=5 , output_type="numpy" ).images
lowercase__ : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowercase__ : Optional[Any] = np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447] )
lowercase__ : int = 1E-2 if torch_device != "mps" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 121 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class snake_case__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = StableDiffusionInstructPixaPixPipeline
_SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
_SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
_SCREAMING_SNAKE_CASE = IMAGE_TO_IMAGE_IMAGE_PARAMS
_SCREAMING_SNAKE_CASE = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowercase_ ( self : List[Any] ) ->Dict:
torch.manual_seed(0 )
snake_case__ : str = UNetaDConditionModel(
block_out_channels=(3_2, 6_4), layers_per_block=2, sample_size=3_2, in_channels=8, out_channels=4, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'), up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'), cross_attention_dim=3_2, )
snake_case__ : List[Any] = PNDMScheduler(skip_prk_steps=_snake_case )
torch.manual_seed(0 )
snake_case__ : List[Any] = AutoencoderKL(
block_out_channels=[3_2, 6_4], in_channels=3, out_channels=3, down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'], up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'], latent_channels=4, )
torch.manual_seed(0 )
snake_case__ : List[str] = CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=3_2, intermediate_size=3_7, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_0_0_0, )
snake_case__ : Union[str, Any] = CLIPTextModel(_snake_case )
snake_case__ : List[Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
snake_case__ : Dict = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def lowercase_ ( self : Tuple, _snake_case : Union[str, Any], _snake_case : Union[str, Any]=0 ) ->Tuple:
snake_case__ : Dict = floats_tensor((1, 3, 3_2, 3_2), rng=random.Random(_snake_case ) ).to(_snake_case )
snake_case__ : Union[str, Any] = image.cpu().permute(0, 2, 3, 1 )[0]
snake_case__ : str = Image.fromarray(np.uinta(_snake_case ) ).convert('RGB' )
if str(_snake_case ).startswith('mps' ):
snake_case__ : Optional[int] = torch.manual_seed(_snake_case )
else:
snake_case__ : Any = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
snake_case__ : Union[str, Any] = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'image_guidance_scale': 1,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self : Optional[Any] ) ->int:
snake_case__ : Optional[int] = 'cpu' # ensure determinism for the device-dependent torch.Generator
snake_case__ : Any = self.get_dummy_components()
snake_case__ : Any = StableDiffusionInstructPixaPixPipeline(**_snake_case )
snake_case__ : Tuple = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
snake_case__ : Union[str, Any] = self.get_dummy_inputs(_snake_case )
snake_case__ : Tuple = sd_pipe(**_snake_case ).images
snake_case__ : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
snake_case__ : List[str] = np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase_ ( self : List[Any] ) ->List[str]:
snake_case__ : List[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
snake_case__ : List[str] = self.get_dummy_components()
snake_case__ : List[str] = StableDiffusionInstructPixaPixPipeline(**_snake_case )
snake_case__ : List[str] = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
snake_case__ : Any = self.get_dummy_inputs(_snake_case )
snake_case__ : Any = 'french fries'
snake_case__ : List[str] = sd_pipe(**_snake_case, negative_prompt=_snake_case )
snake_case__ : int = output.images
snake_case__ : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
snake_case__ : Optional[int] = np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase_ ( self : Tuple ) ->List[str]:
snake_case__ : List[str] = 'cpu' # ensure determinism for the device-dependent torch.Generator
snake_case__ : Optional[Any] = self.get_dummy_components()
snake_case__ : List[str] = StableDiffusionInstructPixaPixPipeline(**_snake_case )
snake_case__ : str = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
snake_case__ : Any = self.get_dummy_inputs(_snake_case )
snake_case__ : Optional[Any] = [inputs['prompt']] * 2
snake_case__ : Optional[Any] = np.array(inputs['image'] ).astype(np.floataa ) / 2_5_5.0
snake_case__ : Optional[Any] = torch.from_numpy(_snake_case ).unsqueeze(0 ).to(_snake_case )
snake_case__ : str = image / 2 + 0.5
snake_case__ : str = image.permute(0, 3, 1, 2 )
snake_case__ : List[str] = image.repeat(2, 1, 1, 1 )
snake_case__ : Dict = sd_pipe(**_snake_case ).images
snake_case__ : Optional[Any] = image[-1, -3:, -3:, -1]
assert image.shape == (2, 3_2, 3_2, 3)
snake_case__ : Tuple = np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase_ ( self : Union[str, Any] ) ->List[Any]:
snake_case__ : Optional[int] = 'cpu' # ensure determinism for the device-dependent torch.Generator
snake_case__ : str = self.get_dummy_components()
snake_case__ : Dict = EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, beta_schedule='scaled_linear' )
snake_case__ : List[str] = StableDiffusionInstructPixaPixPipeline(**_snake_case )
snake_case__ : Optional[int] = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
snake_case__ : List[Any] = self.get_dummy_inputs(_snake_case )
snake_case__ : Tuple = sd_pipe(**_snake_case ).images
snake_case__ : Optional[Any] = image[0, -3:, -3:, -1]
snake_case__ : Optional[Any] = [round(_snake_case, 4 ) for x in image_slice.flatten().tolist()]
print(','.join([str(_snake_case ) for x in slice] ) )
assert image.shape == (1, 3_2, 3_2, 3)
snake_case__ : Optional[Any] = np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase_ ( self : Optional[Any] ) ->Optional[Any]:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def lowercase_ ( self : Any ) ->str:
snake_case__ : str = self.get_dummy_components()
snake_case__ : Dict = StableDiffusionInstructPixaPixPipeline(**_snake_case )
snake_case__ : Optional[int] = VaeImageProcessor(do_resize=_snake_case, do_normalize=_snake_case )
snake_case__ : Union[str, Any] = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
snake_case__ : Optional[Any] = pipe(**self.get_dummy_inputs_by_type(_snake_case, input_image_type='pt' ) )[0]
snake_case__ : Any = components['vae']
snake_case__ : Optional[int] = self.get_dummy_inputs_by_type(_snake_case, input_image_type='pt' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
snake_case__ : List[Any] = vae.encode(inputs[image_param] ).latent_dist.mode()
snake_case__ : int = pipe(**_snake_case )[0]
snake_case__ : Optional[Any] = np.abs(out - out_latents_inputs ).max()
self.assertLess(_snake_case, 1e-4, 'passing latents as image input generate different result from passing image' )
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self : str ) ->List[str]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self : Tuple, _snake_case : Tuple=0 ) ->List[Any]:
snake_case__ : Any = torch.manual_seed(_snake_case )
snake_case__ : List[str] = load_image(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg' )
snake_case__ : Tuple = {
'prompt': 'turn him into a cyborg',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'image_guidance_scale': 1.0,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self : Optional[Any] ) ->List[Any]:
snake_case__ : Tuple = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix', safety_checker=_snake_case )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
snake_case__ : List[Any] = self.get_inputs()
snake_case__ : Tuple = pipe(**_snake_case ).images
snake_case__ : List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
snake_case__ : Any = np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase_ ( self : Tuple ) ->Tuple:
snake_case__ : Union[str, Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix', safety_checker=_snake_case )
snake_case__ : Dict = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
snake_case__ : Dict = self.get_inputs()
snake_case__ : Dict = pipe(**_snake_case ).images
snake_case__ : List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
snake_case__ : Any = np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase_ ( self : str ) ->Optional[int]:
snake_case__ : Any = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix', safety_checker=_snake_case )
snake_case__ : Union[str, Any] = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
snake_case__ : str = self.get_inputs()
snake_case__ : int = pipe(**_snake_case ).images
snake_case__ : str = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
snake_case__ : int = np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase_ ( self : Any ) ->Optional[Any]:
snake_case__ : Union[str, Any] = 0
def callback_fn(_snake_case : int, _snake_case : int, _snake_case : torch.FloatTensor ) -> None:
snake_case__ : Tuple = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
snake_case__ : int = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
snake_case__ : Union[str, Any] = latents[0, -3:, -3:, -1]
snake_case__ : int = np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
snake_case__ : int = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
snake_case__ : List[Any] = latents[0, -3:, -3:, -1]
snake_case__ : List[str] = np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
snake_case__ : Optional[int] = False
snake_case__ : Tuple = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix', safety_checker=_snake_case, torch_dtype=torch.floataa )
snake_case__ : Dict = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
snake_case__ : Tuple = self.get_inputs()
pipe(**_snake_case, callback=_snake_case, callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def lowercase_ ( self : Optional[int] ) ->int:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
snake_case__ : Optional[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix', safety_checker=_snake_case, torch_dtype=torch.floataa )
snake_case__ : List[Any] = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
snake_case__ : str = self.get_inputs()
snake_case__ : Union[str, Any] = pipe(**_snake_case )
snake_case__ : str = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 1_0**9
def lowercase_ ( self : Optional[Any] ) ->Optional[Any]:
snake_case__ : Optional[Any] = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
snake_case__ : Dict = inputs['image'].resize((5_0_4, 5_0_4) )
snake_case__ : List[Any] = 'timbrooks/instruct-pix2pix'
snake_case__ : Optional[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
_snake_case, safety_checker=_snake_case, )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
snake_case__ : List[Any] = pipe(**_snake_case )
snake_case__ : List[str] = output.images[0]
snake_case__ : Union[str, Any] = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert image.shape == (5_0_4, 5_0_4, 3)
snake_case__ : List[str] = np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 277 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class snake_case__ :
"""simple docstring"""
def __init__( self : Tuple, _snake_case : Any, _snake_case : int=1_3, _snake_case : Optional[int]=3_2, _snake_case : Tuple=2, _snake_case : Any=3, _snake_case : Tuple=1_6, _snake_case : Tuple=[1, 2, 1], _snake_case : Dict=[2, 2, 4], _snake_case : str=2, _snake_case : Union[str, Any]=2.0, _snake_case : Dict=True, _snake_case : Dict=0.0, _snake_case : str=0.0, _snake_case : str=0.1, _snake_case : List[str]="gelu", _snake_case : int=False, _snake_case : Optional[Any]=True, _snake_case : List[Any]=0.0_2, _snake_case : Union[str, Any]=1e-5, _snake_case : Union[str, Any]=True, _snake_case : List[Any]=None, _snake_case : Any=True, _snake_case : List[Any]=1_0, _snake_case : str=8, ) ->Union[str, Any]:
snake_case__ : Any = parent
snake_case__ : Tuple = batch_size
snake_case__ : Tuple = image_size
snake_case__ : Any = patch_size
snake_case__ : Optional[int] = num_channels
snake_case__ : Tuple = embed_dim
snake_case__ : Any = depths
snake_case__ : Any = num_heads
snake_case__ : List[str] = window_size
snake_case__ : Dict = mlp_ratio
snake_case__ : Optional[int] = qkv_bias
snake_case__ : Optional[Any] = hidden_dropout_prob
snake_case__ : List[str] = attention_probs_dropout_prob
snake_case__ : Union[str, Any] = drop_path_rate
snake_case__ : str = hidden_act
snake_case__ : Union[str, Any] = use_absolute_embeddings
snake_case__ : Union[str, Any] = patch_norm
snake_case__ : Any = layer_norm_eps
snake_case__ : Tuple = initializer_range
snake_case__ : Dict = is_training
snake_case__ : Any = scope
snake_case__ : Optional[Any] = use_labels
snake_case__ : str = type_sequence_label_size
snake_case__ : List[Any] = encoder_stride
def lowercase_ ( self : Tuple ) ->str:
snake_case__ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case__ : List[Any] = None
if self.use_labels:
snake_case__ : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size )
snake_case__ : Any = self.get_config()
return config, pixel_values, labels
def lowercase_ ( self : Optional[int] ) ->Optional[int]:
return SwinvaConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, embed_dim=self.embed_dim, depths=self.depths, num_heads=self.num_heads, window_size=self.window_size, mlp_ratio=self.mlp_ratio, qkv_bias=self.qkv_bias, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, drop_path_rate=self.drop_path_rate, hidden_act=self.hidden_act, use_absolute_embeddings=self.use_absolute_embeddings, path_norm=self.patch_norm, layer_norm_eps=self.layer_norm_eps, initializer_range=self.initializer_range, encoder_stride=self.encoder_stride, )
def lowercase_ ( self : Optional[int], _snake_case : str, _snake_case : List[str], _snake_case : int ) ->Dict:
snake_case__ : List[Any] = SwinvaModel(config=_snake_case )
model.to(_snake_case )
model.eval()
snake_case__ : Optional[int] = model(_snake_case )
snake_case__ : List[Any] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case__ : List[Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, expected_dim) )
def lowercase_ ( self : Optional[Any], _snake_case : Any, _snake_case : List[str], _snake_case : Dict ) ->List[Any]:
snake_case__ : List[str] = SwinvaForMaskedImageModeling(config=_snake_case )
model.to(_snake_case )
model.eval()
snake_case__ : Union[str, Any] = model(_snake_case )
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case__ : Optional[Any] = 1
snake_case__ : Optional[int] = SwinvaForMaskedImageModeling(_snake_case )
model.to(_snake_case )
model.eval()
snake_case__ : Tuple = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case__ : Any = model(_snake_case )
self.parent.assertEqual(result.logits.shape, (self.batch_size, 1, self.image_size, self.image_size) )
def lowercase_ ( self : List[str], _snake_case : int, _snake_case : List[Any], _snake_case : Optional[int] ) ->Any:
snake_case__ : Tuple = self.type_sequence_label_size
snake_case__ : int = SwinvaForImageClassification(_snake_case )
model.to(_snake_case )
model.eval()
snake_case__ : Tuple = model(_snake_case, labels=_snake_case )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def lowercase_ ( self : Any ) ->Dict:
snake_case__ : str = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ : List[str] = config_and_inputs
snake_case__ : Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class snake_case__ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE = (
{"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
def lowercase_ ( self : Union[str, Any] ) ->Dict:
snake_case__ : Optional[int] = SwinvaModelTester(self )
snake_case__ : int = ConfigTester(self, config_class=_snake_case, embed_dim=3_7 )
def lowercase_ ( self : Tuple ) ->int:
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 lowercase_ ( self : Any ) ->str:
snake_case__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.' )
def lowercase_ ( self : Any ) ->Union[str, Any]:
pass
@unittest.skip(reason='Swinv2 does not use inputs_embeds' )
def lowercase_ ( self : str ) ->Union[str, Any]:
pass
def lowercase_ ( self : Optional[Any] ) ->Union[str, Any]:
snake_case__ , snake_case__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : Union[str, Any] = model_class(_snake_case )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
snake_case__ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_snake_case, nn.Linear ) )
def lowercase_ ( self : List[str] ) ->Optional[int]:
snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : Any = model_class(_snake_case )
snake_case__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case__ : Optional[Any] = [*signature.parameters.keys()]
snake_case__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1], _snake_case )
def lowercase_ ( self : str ) ->Union[str, Any]:
snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : int = True
for model_class in self.all_model_classes:
snake_case__ : str = True
snake_case__ : Union[str, Any] = False
snake_case__ : Tuple = True
snake_case__ : int = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
snake_case__ : Optional[int] = model(**self._prepare_for_class(_snake_case, _snake_case ) )
snake_case__ : List[str] = outputs.attentions
snake_case__ : List[Any] = len(self.model_tester.depths )
self.assertEqual(len(_snake_case ), _snake_case )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case__ : str = True
snake_case__ : Tuple = config.window_size**2
snake_case__ : Optional[int] = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
snake_case__ : str = model(**self._prepare_for_class(_snake_case, _snake_case ) )
snake_case__ : Tuple = outputs.attentions
self.assertEqual(len(_snake_case ), _snake_case )
self.assertListEqual(
list(attentions[0].shape[-3:] ), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], )
snake_case__ : Optional[Any] = len(_snake_case )
# Check attention is always last and order is fine
snake_case__ : Optional[int] = True
snake_case__ : Dict = True
snake_case__ : List[Any] = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
snake_case__ : Optional[int] = model(**self._prepare_for_class(_snake_case, _snake_case ) )
if hasattr(self.model_tester, 'num_hidden_states_types' ):
snake_case__ : str = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
snake_case__ : Dict = 2
self.assertEqual(out_len + added_hidden_states, len(_snake_case ) )
snake_case__ : Any = outputs.attentions
self.assertEqual(len(_snake_case ), _snake_case )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], )
def lowercase_ ( self : Dict, _snake_case : Tuple, _snake_case : Any, _snake_case : int, _snake_case : Optional[int] ) ->str:
snake_case__ : Dict = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
snake_case__ : List[Any] = model(**self._prepare_for_class(_snake_case, _snake_case ) )
snake_case__ : Dict = outputs.hidden_states
snake_case__ : int = getattr(
self.model_tester, 'expected_num_hidden_layers', len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_snake_case ), _snake_case )
# Swinv2 has a different seq_length
snake_case__ : int = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case__ : Optional[Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ), [num_patches, self.model_tester.embed_dim], )
snake_case__ : Union[str, Any] = outputs.reshaped_hidden_states
self.assertEqual(len(_snake_case ), _snake_case )
snake_case__ , snake_case__ , snake_case__ , snake_case__ : str = reshaped_hidden_states[0].shape
snake_case__ : Any = (
reshaped_hidden_states[0].view(_snake_case, _snake_case, height * width ).permute(0, 2, 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ), [num_patches, self.model_tester.embed_dim], )
def lowercase_ ( self : str ) ->List[Any]:
snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : List[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
snake_case__ : Optional[int] = True
self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, _snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case__ : Dict = True
self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, _snake_case )
def lowercase_ ( self : List[str] ) ->str:
snake_case__ , snake_case__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : List[str] = 3
snake_case__ : Union[str, Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
snake_case__ : str = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case__ : Tuple = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case__ : Optional[Any] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
snake_case__ : int = True
self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case__ : List[str] = True
self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, (padded_height, padded_width) )
def lowercase_ ( self : List[str] ) ->Optional[int]:
snake_case__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_snake_case )
def lowercase_ ( self : List[Any] ) ->str:
snake_case__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_snake_case )
@slow
def lowercase_ ( self : str ) ->Union[str, Any]:
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case__ : Dict = SwinvaModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def lowercase_ ( self : Optional[int] ) ->List[str]:
snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : List[Any] = _config_zero_init(_snake_case )
for model_class in self.all_model_classes:
snake_case__ : List[str] = model_class(config=_snake_case )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=F'''Parameter {name} of model {model_class} seems not properly initialized''', )
@require_vision
@require_torch
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self : Union[str, Any] ) ->List[str]:
return (
AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' )
if is_vision_available()
else None
)
@slow
def lowercase_ ( self : int ) ->List[Any]:
snake_case__ : Any = SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' ).to(
_snake_case )
snake_case__ : int = self.default_image_processor
snake_case__ : Union[str, Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
snake_case__ : Optional[Any] = image_processor(images=_snake_case, return_tensors='pt' ).to(_snake_case )
# forward pass
with torch.no_grad():
snake_case__ : List[str] = model(**_snake_case )
# verify the logits
snake_case__ : int = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape, _snake_case )
snake_case__ : Optional[int] = torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6] ).to(_snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3], _snake_case, atol=1e-4 ) )
| 277 | 1 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
@register_to_config
def __init__( self : str, lowerCamelCase : int = 768, )-> Union[str, Any]:
super().__init__()
lowerCamelCase__ : Any =nn.Parameter(torch.zeros(1, lowerCamelCase ) )
lowerCamelCase__ : Optional[Any] =nn.Parameter(torch.ones(1, lowerCamelCase ) )
def snake_case ( self : Union[str, Any], lowerCamelCase : Optional[Union[str, torch.device]] = None, lowerCamelCase : Optional[torch.dtype] = None, )-> Any:
lowerCamelCase__ : int =nn.Parameter(self.mean.to(lowerCamelCase ).to(lowerCamelCase ) )
lowerCamelCase__ : Tuple =nn.Parameter(self.std.to(lowerCamelCase ).to(lowerCamelCase ) )
return self
def snake_case ( self : Union[str, Any], lowerCamelCase : Tuple )-> Optional[Any]:
lowerCamelCase__ : Tuple =(embeds - self.mean) * 1.0 / self.std
return embeds
def snake_case ( self : List[Any], lowerCamelCase : List[str] )-> Optional[Any]:
lowerCamelCase__ : Optional[Any] =(embeds * self.std) + self.mean
return embeds
| 272 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
_lowercase : Dict = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
_lowercase : str = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"wmt19-en-de": {"length_penalty": 1.0},
"wmt19-de-en": {"length_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
"wmt16-en-de-12-1": {"length_penalty": 0.8},
"wmt19-de-en-6-6-base": {"length_penalty": 0.6},
"wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}
# this remaps the different models to their organization names
_lowercase : str = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
_lowercase : Any = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
_lowercase : List[Any] = "allenai"
def snake_case__ ( __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
lowerCamelCase__ : Optional[Any] =dict((re.sub(R'''@@$''' , '''''' , __lowerCamelCase ), v) if k.endswith('''@@''' ) else (re.sub(R'''$''' , '''</w>''' , __lowerCamelCase ), v) for k, v in d.items() )
lowerCamelCase__ : Tuple ='''<s> <pad> </s> <unk>'''.split()
# restore the special tokens
for k in keep_keys:
del da[f'''{k}</w>''']
lowerCamelCase__ : str =d[k] # restore
return da
def snake_case__ ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict ):
"""simple docstring"""
# prep
assert os.path.exists(__lowerCamelCase )
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
print(f'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
lowerCamelCase__ : Union[str, Any] =basename(__lowerCamelCase )
lowerCamelCase__ : str =dirname(__lowerCamelCase )
lowerCamelCase__ : Dict =fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
lowerCamelCase__ : Union[str, Any] =cls.hub_models()
lowerCamelCase__ : Optional[Any] ={'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
lowerCamelCase__ : Any ='''.'''
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(f'''using checkpoint {checkpoint_file}''' )
lowerCamelCase__ : Optional[int] =hub_utils.from_pretrained(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , archive_map=__lowerCamelCase , **__lowerCamelCase )
lowerCamelCase__ : Any =vars(chkpt['''args''']['''model'''] )
lowerCamelCase__ : int =args['''source_lang''']
lowerCamelCase__ : Optional[Any] =args['''target_lang''']
lowerCamelCase__ : Dict =dirname(__lowerCamelCase )
lowerCamelCase__ : str =basename(__lowerCamelCase )
# dicts
lowerCamelCase__ : Optional[Any] =os.path.join(__lowerCamelCase , f'''dict.{src_lang}.txt''' )
lowerCamelCase__ : int =os.path.join(__lowerCamelCase , f'''dict.{tgt_lang}.txt''' )
lowerCamelCase__ : Dict =Dictionary.load(__lowerCamelCase )
lowerCamelCase__ : List[str] =rewrite_dict_keys(src_dict.indices )
lowerCamelCase__ : Optional[int] =len(__lowerCamelCase )
lowerCamelCase__ : Dict =os.path.join(__lowerCamelCase , '''vocab-src.json''' )
print(f'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(__lowerCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(__lowerCamelCase , ensure_ascii=__lowerCamelCase , indent=__lowerCamelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
lowerCamelCase__ : Optional[int] =True
for k in src_vocab.keys():
if not k.islower():
lowerCamelCase__ : int =False
break
lowerCamelCase__ : Any =Dictionary.load(__lowerCamelCase )
lowerCamelCase__ : Tuple =rewrite_dict_keys(tgt_dict.indices )
lowerCamelCase__ : str =len(__lowerCamelCase )
lowerCamelCase__ : Dict =os.path.join(__lowerCamelCase , '''vocab-tgt.json''' )
print(f'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(__lowerCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(__lowerCamelCase , ensure_ascii=__lowerCamelCase , indent=__lowerCamelCase ) )
# merges_file (bpecodes)
lowerCamelCase__ : Union[str, Any] =os.path.join(__lowerCamelCase , VOCAB_FILES_NAMES['''merges_file'''] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
lowerCamelCase__ : Tuple =os.path.join(__lowerCamelCase , __lowerCamelCase )
if os.path.exists(__lowerCamelCase ):
break
with open(__lowerCamelCase , encoding='''utf-8''' ) as fin:
lowerCamelCase__ : Optional[Any] =fin.read()
lowerCamelCase__ : List[Any] =re.sub(R''' \d+$''' , '''''' , __lowerCamelCase , 0 , re.M ) # remove frequency number
print(f'''Generating {merges_file}''' )
with open(__lowerCamelCase , '''w''' , encoding='''utf-8''' ) as fout:
fout.write(__lowerCamelCase )
# model config
lowerCamelCase__ : List[Any] =os.path.join(__lowerCamelCase , '''config.json''' )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", f'''need to extend tokenizer to support bpe={args["bpe"]}'''
assert args["tokenizer"] == "moses", f'''need to extend tokenizer to support bpe={args["tokenizer"]}'''
lowerCamelCase__ : str ={
'''architectures''': ['''FSMTForConditionalGeneration'''],
'''model_type''': '''fsmt''',
'''activation_dropout''': args['''activation_dropout'''],
'''activation_function''': '''relu''',
'''attention_dropout''': args['''attention_dropout'''],
'''d_model''': args['''decoder_embed_dim'''],
'''dropout''': args['''dropout'''],
'''init_std''': 0.02,
'''max_position_embeddings''': args['''max_source_positions'''],
'''num_hidden_layers''': args['''encoder_layers'''],
'''src_vocab_size''': src_vocab_size,
'''tgt_vocab_size''': tgt_vocab_size,
'''langs''': [src_lang, tgt_lang],
'''encoder_attention_heads''': args['''encoder_attention_heads'''],
'''encoder_ffn_dim''': args['''encoder_ffn_embed_dim'''],
'''encoder_layerdrop''': args['''encoder_layerdrop'''],
'''encoder_layers''': args['''encoder_layers'''],
'''decoder_attention_heads''': args['''decoder_attention_heads'''],
'''decoder_ffn_dim''': args['''decoder_ffn_embed_dim'''],
'''decoder_layerdrop''': args['''decoder_layerdrop'''],
'''decoder_layers''': args['''decoder_layers'''],
'''bos_token_id''': 0,
'''pad_token_id''': 1,
'''eos_token_id''': 2,
'''is_encoder_decoder''': True,
'''scale_embedding''': not args['''no_scale_embedding'''],
'''tie_word_embeddings''': args['''share_all_embeddings'''],
}
# good hparam defaults to start with
lowerCamelCase__ : Optional[int] =5
lowerCamelCase__ : List[str] =False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
lowerCamelCase__ : Optional[int] =best_score_hparams[model_dir]['''length_penalty''']
else:
lowerCamelCase__ : Union[str, Any] =1.0
print(f'''Generating {fsmt_model_config_file}''' )
with open(__lowerCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(__lowerCamelCase , ensure_ascii=__lowerCamelCase , indent=__lowerCamelCase ) )
# tokenizer config
lowerCamelCase__ : Any =os.path.join(__lowerCamelCase , __lowerCamelCase )
lowerCamelCase__ : int ={
'''langs''': [src_lang, tgt_lang],
'''model_max_length''': 1024,
'''do_lower_case''': do_lower_case,
}
print(f'''Generating {fsmt_tokenizer_config_file}''' )
with open(__lowerCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(__lowerCamelCase , ensure_ascii=__lowerCamelCase , indent=__lowerCamelCase ) )
# model
lowerCamelCase__ : int =chkpt['''models'''][0]
lowerCamelCase__ : Union[str, Any] =model.state_dict()
# rename keys to start with 'model.'
lowerCamelCase__ : Union[str, Any] =OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
lowerCamelCase__ : str =[
'''model.model''',
'''model.encoder.version''',
'''model.decoder.version''',
'''model.encoder_embed_tokens.weight''',
'''model.decoder_embed_tokens.weight''',
'''model.encoder.embed_positions._float_tensor''',
'''model.decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
model_state_dict.pop(__lowerCamelCase , __lowerCamelCase )
lowerCamelCase__ : Dict =FSMTConfig.from_pretrained(__lowerCamelCase )
lowerCamelCase__ : int =FSMTForConditionalGeneration(__lowerCamelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCamelCase , strict=__lowerCamelCase )
# save
lowerCamelCase__ : Optional[int] =os.path.join(__lowerCamelCase , __lowerCamelCase )
print(f'''Generating {pytorch_weights_dump_path}''' )
torch.save(__lowerCamelCase , __lowerCamelCase )
print('''Conversion is done!''' )
print('''\nLast step is to upload the files to s3''' )
print(f'''cd {data_root}''' )
print(f'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
_lowercase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fsmt_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
" bpecodes, etc."
),
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_lowercase : str = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 272 | 1 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
_UpperCamelCase : Any = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase_ :
lowerCamelCase__ : Optional[str] = field(
default="tab_fact" , metadata={"help": "The name of the dataset to use (via the datasets library)."})
lowerCamelCase__ : Optional[str] = field(
default="tab_fact" , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} , )
lowerCamelCase__ : int = field(
default=1_0_2_4 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
lowerCamelCase__ : bool = field(
default=_a , metadata={"help": "Overwrite the cached preprocessed datasets or not."})
lowerCamelCase__ : bool = field(
default=_a , metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
} , )
lowerCamelCase__ : Optional[int] = field(
default=_a , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
lowerCamelCase__ : Optional[int] = field(
default=_a , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
lowerCamelCase__ : Optional[int] = field(
default=_a , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
} , )
lowerCamelCase__ : Optional[str] = field(
default=_a , metadata={"help": "A csv or a json file containing the training data."})
lowerCamelCase__ : Optional[str] = field(
default=_a , metadata={"help": "A csv or a json file containing the validation data."})
lowerCamelCase__ : Optional[str] = field(default=_a , metadata={"help": "A csv or a json file containing the test data."})
def _UpperCAmelCase ( self ) -> Optional[Any]:
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.' )
else:
lowercase__ : Optional[int] = self.train_file.split('.' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
lowercase__ : Optional[Any] = self.validation_file.split('.' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class UpperCAmelCase_ :
lowerCamelCase__ : str = field(
default=_a , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
lowerCamelCase__ : Optional[str] = field(
default=_a , metadata={"help": "Pretrained config name or path if not the same as model_name"})
lowerCamelCase__ : Optional[str] = field(
default=_a , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
lowerCamelCase__ : Optional[str] = field(
default=_a , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
lowerCamelCase__ : bool = field(
default=_a , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
lowerCamelCase__ : str = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
lowerCamelCase__ : bool = field(
default=_a , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase__ , lowercase__ , lowercase__ : str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase__ , lowercase__ , lowercase__ : List[Any] = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
lowercase__ : int = training_args.get_process_log_level()
logger.setLevel(_lowerCAmelCase )
datasets.utils.logging.set_verbosity(_lowerCAmelCase )
transformers.utils.logging.set_verbosity(_lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
lowercase__ : Optional[int] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase__ : Any = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
lowercase__ : Optional[int] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
lowercase__ : Tuple = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
lowercase__ : List[Any] = data_args.train_file.split('.' )[-1]
lowercase__ : Optional[int] = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
lowercase__ : List[str] = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(f"""load a local file for {key}: {data_files[key]}""" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
lowercase__ : List[Any] = load_dataset('csv' , data_files=_lowerCAmelCase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
lowercase__ : List[Any] = load_dataset('json' , data_files=_lowerCAmelCase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
lowercase__ : int = raw_datasets['train'].features['label'].names
lowercase__ : Union[str, Any] = len(_lowerCAmelCase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase__ : Union[str, Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
lowercase__ : List[str] = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=_lowerCAmelCase , )
lowercase__ : Optional[int] = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
lowercase__ : Tuple = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
lowercase__ : Optional[int] = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
lowercase__ : str = {'Refused': 0, 'Entailed': 1}
lowercase__ : Dict = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
lowercase__ : Optional[Any] = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(_lowerCAmelCase : Optional[int] ):
# Tokenize the texts
def _convert_table_text_to_pandas(_lowerCAmelCase : Union[str, Any] ):
lowercase__ : Optional[int] = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
lowercase__ : str = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
lowercase__ : Tuple = examples['statement']
lowercase__ : int = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
lowercase__ : Optional[int] = tokenizer(_lowerCAmelCase , _lowerCAmelCase , padding=_lowerCAmelCase , max_length=_lowerCAmelCase , truncation=_lowerCAmelCase )
lowercase__ : str = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
lowercase__ : str = raw_datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
lowercase__ : int = raw_datasets['train']
if data_args.max_train_samples is not None:
lowercase__ : int = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
lowercase__ : str = raw_datasets['validation']
if data_args.max_eval_samples is not None:
lowercase__ : Any = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
lowercase__ : List[Any] = raw_datasets['test']
if data_args.max_predict_samples is not None:
lowercase__ : Optional[int] = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_lowerCAmelCase ) ) , 3 ):
logger.info(f"""Sample {index} of the training set: {train_dataset[index]}.""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_lowerCAmelCase : EvalPrediction ):
lowercase__ : Optional[int] = p.predictions[0] if isinstance(p.predictions , _lowerCAmelCase ) else p.predictions
lowercase__ : List[str] = np.argmax(_lowerCAmelCase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
lowercase__ : List[str] = default_data_collator
elif training_args.fpaa:
lowercase__ : Optional[Any] = DataCollatorWithPadding(_lowerCAmelCase , pad_to_multiple_of=8 )
else:
lowercase__ : str = None
# Initialize our Trainer
lowercase__ : int = Trainer(
model=_lowerCAmelCase , args=_lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_lowerCAmelCase , tokenizer=_lowerCAmelCase , data_collator=_lowerCAmelCase , )
# Training
if training_args.do_train:
lowercase__ : Union[str, Any] = None
if training_args.resume_from_checkpoint is not None:
lowercase__ : str = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase__ : Optional[int] = last_checkpoint
lowercase__ : Tuple = trainer.train(resume_from_checkpoint=_lowerCAmelCase )
lowercase__ : Tuple = train_result.metrics
lowercase__ : str = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_lowerCAmelCase )
)
lowercase__ : Any = min(_lowerCAmelCase , len(_lowerCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _lowerCAmelCase )
trainer.save_metrics('train' , _lowerCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
lowercase__ : Union[str, Any] = trainer.evaluate(eval_dataset=_lowerCAmelCase )
lowercase__ : str = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_lowerCAmelCase )
lowercase__ : List[str] = min(_lowerCAmelCase , len(_lowerCAmelCase ) )
trainer.log_metrics('eval' , _lowerCAmelCase )
trainer.save_metrics('eval' , _lowerCAmelCase )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
lowercase__ : int = predict_dataset.remove_columns('label' )
lowercase__ : Any = trainer.predict(_lowerCAmelCase , metric_key_prefix='predict' ).predictions
lowercase__ : str = np.argmax(_lowerCAmelCase , axis=1 )
lowercase__ : Optional[Any] = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_lowerCAmelCase , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_lowerCAmelCase ):
lowercase__ : Optional[int] = label_list[item]
writer.write(f"""{index}\t{item}\n""" )
lowercase__ : Union[str, Any] = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_lowerCAmelCase )
else:
trainer.create_model_card(**_lowerCAmelCase )
def a_ ( _lowerCAmelCase : List[Any] ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 77 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
),
}
class lowerCamelCase (_snake_case ):
'''simple docstring'''
_snake_case : Optional[int] = '''efficientformer'''
def __init__( self , _UpperCamelCase = [3, 2, 6, 4] , _UpperCamelCase = [4_8, 9_6, 2_2_4, 4_4_8] , _UpperCamelCase = [True, True, True, True] , _UpperCamelCase = 4_4_8 , _UpperCamelCase = 3_2 , _UpperCamelCase = 4 , _UpperCamelCase = 7 , _UpperCamelCase = 5 , _UpperCamelCase = 8 , _UpperCamelCase = 4 , _UpperCamelCase = 0.0 , _UpperCamelCase = 1_6 , _UpperCamelCase = 3 , _UpperCamelCase = 3 , _UpperCamelCase = 3 , _UpperCamelCase = 2 , _UpperCamelCase = 1 , _UpperCamelCase = 0.0 , _UpperCamelCase = 1 , _UpperCamelCase = True , _UpperCamelCase = True , _UpperCamelCase = 1E-5 , _UpperCamelCase = "gelu" , _UpperCamelCase = 0.02 , _UpperCamelCase = 1E-12 , _UpperCamelCase = 2_2_4 , _UpperCamelCase = 1E-05 , **_UpperCamelCase , ) -> None:
super().__init__(**_UpperCamelCase )
UpperCAmelCase_ : int = hidden_act
UpperCAmelCase_ : Union[str, Any] = hidden_dropout_prob
UpperCAmelCase_ : Tuple = hidden_sizes
UpperCAmelCase_ : Union[str, Any] = num_hidden_layers
UpperCAmelCase_ : List[str] = num_attention_heads
UpperCAmelCase_ : List[Any] = initializer_range
UpperCAmelCase_ : int = layer_norm_eps
UpperCAmelCase_ : List[str] = patch_size
UpperCAmelCase_ : Union[str, Any] = num_channels
UpperCAmelCase_ : Optional[Any] = depths
UpperCAmelCase_ : List[Any] = mlp_expansion_ratio
UpperCAmelCase_ : List[str] = downsamples
UpperCAmelCase_ : List[Any] = dim
UpperCAmelCase_ : Tuple = key_dim
UpperCAmelCase_ : Optional[int] = attention_ratio
UpperCAmelCase_ : str = resolution
UpperCAmelCase_ : Dict = pool_size
UpperCAmelCase_ : Union[str, Any] = downsample_patch_size
UpperCAmelCase_ : List[str] = downsample_stride
UpperCAmelCase_ : List[str] = downsample_pad
UpperCAmelCase_ : Any = drop_path_rate
UpperCAmelCase_ : Dict = num_metaad_blocks
UpperCAmelCase_ : Dict = distillation
UpperCAmelCase_ : int = use_layer_scale
UpperCAmelCase_ : Any = layer_scale_init_value
UpperCAmelCase_ : Any = image_size
UpperCAmelCase_ : Dict = batch_norm_eps
| 29 | 0 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('0.12.2'):
raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
raise Exception('requires fairseq < v2')
logging.set_verbosity_info()
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : Any = 'Hello, World!'
lowercase : Optional[Any] = 'en_XX'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str , _lowerCamelCase : str , _lowerCamelCase : bool) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase : str = Path("data_bin")
__UpperCamelCase : Dict = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(_lowerCamelCase).parent) , checkpoint_file=Path(_lowerCamelCase).name , _name="xmod_base" , arch="xmod_base" , task="multilingual_masked_lm" , data_name_or_path=str(_lowerCamelCase) , bpe="sentencepiece" , sentencepiece_model=str(Path(_lowerCamelCase).parent / "sentencepiece.bpe.model") , src_dict=str(data_dir / "dict.txt") , )
xmod.eval() # disable dropout
print(_lowerCamelCase)
__UpperCamelCase : Any = xmod.model.encoder.sentence_encoder
__UpperCamelCase : Optional[int] = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , "bottleneck" , 2) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
__UpperCamelCase : List[str] = xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our X-MOD config:" , _lowerCamelCase)
__UpperCamelCase : Optional[Any] = XmodForSequenceClassification(_lowerCamelCase) if classification_head else XmodForMaskedLM(_lowerCamelCase)
model.eval()
# Now let's copy all the weights.
# Embeddings
__UpperCamelCase : str = xmod_sent_encoder.embed_tokens.weight
__UpperCamelCase : List[Any] = xmod_sent_encoder.embed_positions.weight
__UpperCamelCase : Any = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight) # just zero them out b/c xmod doesn't use them.
__UpperCamelCase : int = xmod_sent_encoder.layernorm_embedding.weight
__UpperCamelCase : List[str] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers):
# Encoder: start of layer
__UpperCamelCase : Tuple = model.roberta.encoder.layer[i]
__UpperCamelCase : Union[str, Any] = xmod_sent_encoder.layers[i]
# self attention
__UpperCamelCase : List[str] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size))
):
raise AssertionError("Dimensions of self-attention weights do not match.")
__UpperCamelCase : int = xmod_layer.self_attn.q_proj.weight
__UpperCamelCase : Union[str, Any] = xmod_layer.self_attn.q_proj.bias
__UpperCamelCase : int = xmod_layer.self_attn.k_proj.weight
__UpperCamelCase : Optional[int] = xmod_layer.self_attn.k_proj.bias
__UpperCamelCase : str = xmod_layer.self_attn.v_proj.weight
__UpperCamelCase : Any = xmod_layer.self_attn.v_proj.bias
# self-attention output
__UpperCamelCase : Any = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("Dimensions of self-attention output weights do not match.")
__UpperCamelCase : List[Any] = xmod_layer.self_attn.out_proj.weight
__UpperCamelCase : List[Any] = xmod_layer.self_attn.out_proj.bias
__UpperCamelCase : Union[str, Any] = xmod_layer.self_attn_layer_norm.weight
__UpperCamelCase : Any = xmod_layer.self_attn_layer_norm.bias
# intermediate
__UpperCamelCase : int = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of intermediate weights do not match.")
__UpperCamelCase : Optional[int] = xmod_layer.fca.weight
__UpperCamelCase : int = xmod_layer.fca.bias
# output
__UpperCamelCase : Union[str, Any] = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of feed-forward weights do not match.")
__UpperCamelCase : Any = xmod_layer.fca.weight
__UpperCamelCase : Union[str, Any] = xmod_layer.fca.bias
__UpperCamelCase : Tuple = xmod_layer.final_layer_norm.weight
__UpperCamelCase : Dict = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
__UpperCamelCase : Union[str, Any] = xmod_layer.adapter_layer_norm.weight
__UpperCamelCase : Union[str, Any] = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys()) != sorted(xmod_layer.adapter_modules.keys()):
raise AssertionError("Lists of language adapters do not match.")
for lang_code, adapter in xmod_layer.adapter_modules.items():
__UpperCamelCase : int = bert_output.adapter_modules[lang_code]
__UpperCamelCase : List[Any] = xmod_layer.adapter_modules[lang_code]
__UpperCamelCase : Optional[Any] = from_adapter.fca.weight
__UpperCamelCase : int = from_adapter.fca.bias
__UpperCamelCase : Union[str, Any] = from_adapter.fca.weight
__UpperCamelCase : List[Any] = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
__UpperCamelCase : Tuple = xmod_sent_encoder.layer_norm.weight
__UpperCamelCase : Tuple = xmod_sent_encoder.layer_norm.bias
if classification_head:
__UpperCamelCase : int = xmod.model.classification_heads["mnli"].dense.weight
__UpperCamelCase : Union[str, Any] = xmod.model.classification_heads["mnli"].dense.bias
__UpperCamelCase : str = xmod.model.classification_heads["mnli"].out_proj.weight
__UpperCamelCase : List[Any] = xmod.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
__UpperCamelCase : Any = xmod.model.encoder.lm_head.dense.weight
__UpperCamelCase : Union[str, Any] = xmod.model.encoder.lm_head.dense.bias
__UpperCamelCase : Optional[Any] = xmod.model.encoder.lm_head.layer_norm.weight
__UpperCamelCase : Any = xmod.model.encoder.lm_head.layer_norm.bias
__UpperCamelCase : Tuple = xmod.model.encoder.lm_head.weight
__UpperCamelCase : Union[str, Any] = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
__UpperCamelCase : Tuple = xmod.encode(_lowerCamelCase).unsqueeze(0) # batch of size 1
model.roberta.set_default_language(_lowerCamelCase)
__UpperCamelCase : Tuple = model(_lowerCamelCase)[0]
if classification_head:
__UpperCamelCase : str = xmod.model.classification_heads["mnli"](xmod.extract_features(_lowerCamelCase))
else:
__UpperCamelCase : List[str] = xmod.model(_lowerCamelCase , lang_id=[SAMPLE_LANGUAGE])[0]
print(our_output.shape , their_output.shape)
__UpperCamelCase : List[str] = torch.max(torch.abs(our_output - their_output)).item()
print(F'max_absolute_diff = {max_absolute_diff}') # ~ 1e-7
__UpperCamelCase : Dict = torch.allclose(_lowerCamelCase , _lowerCamelCase , atol=1e-3)
print("Do both models output the same tensors?" , "🔥" if success else "💩")
if not success:
raise Exception("Something went wRoNg")
Path(_lowerCamelCase).mkdir(parents=_lowerCamelCase , exist_ok=_lowerCamelCase)
print(F'Saving model to {pytorch_dump_folder_path}')
model.save_pretrained(_lowerCamelCase)
if __name__ == "__main__":
lowercase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--classification_head', action='store_true', help='Whether to convert a final classification head.'
)
lowercase : List[Any] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
) | 151 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
lowercase : Any = '\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n'
lowercase : str = '\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper "Evaluating Large Language Models Trained on Code"\n(https://arxiv.org/abs/2107.03374).\n'
lowercase : str = '\nCalculates how good are predictions given some references, using certain scores\nArgs:\n predictions: list of candidates to evaluate. Each candidates should be a list\n of strings with several code candidates to solve the problem.\n references: a list with a test for each prediction. Each test should evaluate the\n correctness of a code candidate.\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n timeout:\nReturns:\n pass_at_k: dict with pass rates for each k\n results: dict with granular results of each unittest\nExamples:\n >>> code_eval = datasets.load_metric("code_eval")\n >>> test_cases = ["assert add(2,3)==5"]\n >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n >>> print(pass_at_k)\n {\'pass@1\': 0.5, \'pass@2\': 1.0}\n'
lowercase : List[str] = '\n################################################################################\n !!!WARNING!!!\n################################################################################\nThe "code_eval" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper "Evaluating Large\nLanguage Models Trained on Code" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"\n\n################################################################################\\n'
lowercase : List[Any] = 'The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the "Software"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class lowerCamelCase__ ( datasets.Metric):
'''simple docstring'''
def _lowerCamelCase ( self :List[Any] ) -> List[Any]:
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" ) ),
"references": datasets.Value("string" ),
} ) , homepage="https://github.com/openai/human-eval" , codebase_urls=["https://github.com/openai/human-eval"] , reference_urls=["https://github.com/openai/human-eval"] , license=_LICENSE , )
def _lowerCamelCase ( self :str , a :Tuple , a :str , a :Tuple=[1, 1_0, 1_0_0] , a :Optional[Any]=4 , a :Optional[int]=3.0 ) -> Dict:
if os.getenv("HF_ALLOW_CODE_EVAL" , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError("This metric is currently not supported on Windows." )
with ThreadPoolExecutor(max_workers=a ) as executor:
__UpperCamelCase : List[Any] = []
__UpperCamelCase : str = Counter()
__UpperCamelCase : Tuple = 0
__UpperCamelCase : Dict = defaultdict(a )
for task_id, (candidates, test_case) in enumerate(zip(a , a ) ):
for candidate in candidates:
__UpperCamelCase : List[str] = candidate + "\n" + test_case
__UpperCamelCase : Tuple = (test_program, timeout, task_id, completion_id[task_id])
__UpperCamelCase : str = executor.submit(a , *a )
futures.append(a )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(a ):
__UpperCamelCase : int = future.result()
results[result["task_id"]].append((result["completion_id"], result) )
__UpperCamelCase , __UpperCamelCase : Tuple = [], []
for result in results.values():
result.sort()
__UpperCamelCase : List[Any] = [r[1]["passed"] for r in result]
total.append(len(a ) )
correct.append(sum(a ) )
__UpperCamelCase : Union[str, Any] = np.array(a )
__UpperCamelCase : Dict = np.array(a )
__UpperCamelCase : List[str] = k
__UpperCamelCase : Optional[int] = {f'pass@{k}': estimate_pass_at_k(a , a , a ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : str , _lowerCamelCase : Union[str, Any]) -> Dict:
'''simple docstring'''
def estimator(_lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1))
if isinstance(_lowerCamelCase , _lowerCamelCase):
__UpperCamelCase : List[Any] = itertools.repeat(_lowerCamelCase , len(_lowerCamelCase))
else:
assert len(_lowerCamelCase) == len(_lowerCamelCase)
__UpperCamelCase : Optional[int] = iter(_lowerCamelCase)
return np.array([estimator(int(_lowerCamelCase) , int(_lowerCamelCase) , _lowerCamelCase) for n, c in zip(_lowerCamelCase , _lowerCamelCase)]) | 151 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
__snake_case = {
'google/tapas-base-finetuned-sqa': (
'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
),
'google/tapas-base-finetuned-wtq': (
'https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'
),
'google/tapas-base-finetuned-wikisql-supervised': (
'https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'
),
'google/tapas-base-finetuned-tabfact': (
'https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'
),
}
class __lowerCamelCase ( UpperCAmelCase__ ):
'''simple docstring'''
A_ : Optional[int] = "tapas"
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=1024 , __UpperCAmelCase=[3, 256, 256, 2, 256, 256, 10] , __UpperCAmelCase=0.02 , __UpperCAmelCase=1e-1_2 , __UpperCAmelCase=0 , __UpperCAmelCase=10.0 , __UpperCAmelCase=0 , __UpperCAmelCase=1.0 , __UpperCAmelCase=None , __UpperCAmelCase=1.0 , __UpperCAmelCase=False , __UpperCAmelCase=None , __UpperCAmelCase=1.0 , __UpperCAmelCase=1.0 , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase="ratio" , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=64 , __UpperCAmelCase=32 , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase , ) -> Dict:
super().__init__(pad_token_id=snake_case__ , **snake_case__ )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = hidden_act
_a = intermediate_size
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_sizes
_a = initializer_range
_a = layer_norm_eps
# Fine-tuning task hyperparameters
_a = positive_label_weight
_a = num_aggregation_labels
_a = aggregation_loss_weight
_a = use_answer_as_supervision
_a = answer_loss_importance
_a = use_normalized_answer_loss
_a = huber_loss_delta
_a = temperature
_a = aggregation_temperature
_a = use_gumbel_for_cells
_a = use_gumbel_for_aggregation
_a = average_approximation_function
_a = cell_selection_preference
_a = answer_loss_cutoff
_a = max_num_rows
_a = max_num_columns
_a = average_logits_per_cell
_a = select_one_column
_a = allow_empty_column_selection
_a = init_cell_selection_weights_to_zero
_a = reset_position_index_per_cell
_a = disable_per_token_loss
# Aggregation hyperparameters
_a = aggregation_labels
_a = no_aggregation_label_index
if isinstance(self.aggregation_labels , snake_case__ ):
_a = {int(snake_case__ ): v for k, v in aggregation_labels.items()} | 320 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Dict = logging.get_logger(__name__)
lowercase_ : Union[str, Any] = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class __lowerCAmelCase ( UpperCAmelCase__ ):
snake_case_ : int = "ctrl"
snake_case_ : Optional[int] = ["past_key_values"]
snake_case_ : Tuple = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : List[str] , snake_case__ : List[str]=246_534 , snake_case__ : Optional[Any]=256 , snake_case__ : List[str]=1_280 , snake_case__ : Optional[int]=8_192 , snake_case__ : List[Any]=48 , snake_case__ : Dict=16 , snake_case__ : int=0.1 , snake_case__ : List[str]=0.1 , snake_case__ : Optional[int]=1e-6 , snake_case__ : Dict=0.02 , snake_case__ : List[Any]=True , **snake_case__ : List[str] , ):
"""simple docstring"""
_UpperCAmelCase = vocab_size
_UpperCAmelCase = n_positions
_UpperCAmelCase = n_embd
_UpperCAmelCase = n_layer
_UpperCAmelCase = n_head
_UpperCAmelCase = dff
_UpperCAmelCase = resid_pdrop
_UpperCAmelCase = embd_pdrop
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = use_cache
super().__init__(**snake_case__ )
| 133 | 0 |
from manim import *
class snake_case_ ( __A ):
def __UpperCamelCase ( self : str ) -> Union[str, Any]:
lowercase__ : Optional[Any] = Rectangle(height=0.5 , width=0.5 )
lowercase__ : str = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
lowercase__ : Tuple = [mem.copy() for i in range(6 )]
lowercase__ : List[Any] = [mem.copy() for i in range(6 )]
lowercase__ : str = VGroup(*lowerCamelCase_ ).arrange(lowerCamelCase_ , buff=0 )
lowercase__ : Optional[int] = VGroup(*lowerCamelCase_ ).arrange(lowerCamelCase_ , buff=0 )
lowercase__ : Optional[int] = VGroup(lowerCamelCase_ , lowerCamelCase_ ).arrange(lowerCamelCase_ , buff=0 )
lowercase__ : Dict = Text("CPU" , font_size=24 )
lowercase__ : Any = Group(lowerCamelCase_ , lowerCamelCase_ ).arrange(lowerCamelCase_ , buff=0.5 , aligned_edge=lowerCamelCase_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowerCamelCase_ )
lowercase__ : str = [mem.copy() for i in range(1 )]
lowercase__ : Any = VGroup(*lowerCamelCase_ ).arrange(lowerCamelCase_ , buff=0 )
lowercase__ : Tuple = Text("GPU" , font_size=24 )
lowercase__ : Optional[int] = Group(lowerCamelCase_ , lowerCamelCase_ ).arrange(lowerCamelCase_ , buff=0.5 , aligned_edge=lowerCamelCase_ )
gpu.align_to(lowerCamelCase_ , lowerCamelCase_ )
gpu.set_x(gpu.get_x() - 1 )
self.add(lowerCamelCase_ )
lowercase__ : Tuple = [mem.copy() for i in range(6 )]
lowercase__ : Any = VGroup(*lowerCamelCase_ ).arrange(lowerCamelCase_ , buff=0 )
lowercase__ : Any = Text("Model" , font_size=24 )
lowercase__ : List[Any] = Group(lowerCamelCase_ , lowerCamelCase_ ).arrange(lowerCamelCase_ , buff=0.5 , aligned_edge=lowerCamelCase_ )
model.move_to([3, -1.0, 0] )
self.play(
Create(lowerCamelCase_ , run_time=1 ) , Create(lowerCamelCase_ , run_time=1 ) , Create(lowerCamelCase_ , run_time=1 ) , )
lowercase__ : Optional[int] = MarkupText(
F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''' , font_size=24 , )
lowercase__ : List[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowercase__ : str = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCamelCase_ , run_time=2.5 ) , Write(lowerCamelCase_ ) , Write(lowerCamelCase_ ) )
self.add(lowerCamelCase_ )
lowercase__ : Dict = []
lowercase__ : List[Any] = []
lowercase__ : List[str] = []
for i, rect in enumerate(lowerCamelCase_ ):
lowercase__ : List[str] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(lowerCamelCase_ , opacity=0.7 )
cpu_target.move_to(lowerCamelCase_ )
cpu_target.generate_target()
lowercase__ : Tuple = 0.46 / 4
lowercase__ : Optional[int] = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=lowerCamelCase_ )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=lowerCamelCase_ , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=lowerCamelCase_ , buff=0.0 )
cpu_targs.append(lowerCamelCase_ )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(lowerCamelCase_ ) )
second_animations.append(MoveToTarget(lowerCamelCase_ , run_time=1.5 ) )
self.play(*lowerCamelCase_ )
self.play(*lowerCamelCase_ )
self.wait()
| 355 | # Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def lowercase_ ( _lowerCamelCase : List[str]):
return 1 / (1 + np.exp(-z))
def lowercase_ ( _lowerCamelCase : Dict , _lowerCamelCase : Tuple):
return (-y * np.log(_lowerCamelCase) - (1 - y) * np.log(1 - h)).mean()
def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Tuple):
lowercase__ : Union[str, Any] = np.dot(_lowerCamelCase , _lowerCamelCase)
return np.sum(y * scores - np.log(1 + np.exp(_lowerCamelCase)))
def lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : Tuple , _lowerCamelCase : Optional[Any] , _lowerCamelCase : str=7_0000):
lowercase__ : Optional[int] = np.zeros(x.shape[1])
for iterations in range(_lowerCamelCase):
lowercase__ : Union[str, Any] = np.dot(_lowerCamelCase , _lowerCamelCase)
lowercase__ : Tuple = sigmoid_function(_lowerCamelCase)
lowercase__ : Dict = np.dot(x.T , h - y) / y.size
lowercase__ : int = theta - alpha * gradient # updating the weights
lowercase__ : List[str] = np.dot(_lowerCamelCase , _lowerCamelCase)
lowercase__ : Union[str, Any] = sigmoid_function(_lowerCamelCase)
lowercase__ : Optional[Any] = cost_function(_lowerCamelCase , _lowerCamelCase)
if iterations % 100 == 0:
print(f'''loss: {j} \t''') # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
UpperCamelCase = datasets.load_iris()
UpperCamelCase = iris.data[:, :2]
UpperCamelCase = (iris.target != 0) * 1
UpperCamelCase = 0.1
UpperCamelCase = logistic_reg(alpha, x, y, max_iterations=7_0000)
print('''theta: ''', theta) # printing the theta i.e our weights vector
def lowercase_ ( _lowerCamelCase : List[Any]):
return sigmoid_function(
np.dot(_lowerCamelCase , _lowerCamelCase)) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
((UpperCamelCase) , (UpperCamelCase)) = (x[:, 0].min(), x[:, 0].max())
((UpperCamelCase) , (UpperCamelCase)) = (x[:, 1].min(), x[:, 1].max())
((UpperCamelCase) , (UpperCamelCase)) = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
UpperCamelCase = np.c_[xxa.ravel(), xxa.ravel()]
UpperCamelCase = predict_prob(grid).reshape(xxa.shape)
plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')
plt.legend()
plt.show()
| 333 | 0 |
import os
import sys
import unittest
__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 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,
)
__A = os.path.join("tests", "models", "bert", "test_modeling_bert.py")
__A = os.path.join("tests", "models", "blip", "test_modeling_blip.py")
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = get_test_to_tester_mapping(lowerCamelCase__ )
__lowerCamelCase = get_test_to_tester_mapping(lowerCamelCase__ )
__lowerCamelCase = {'BertModelTest': 'BertModelTester'}
__lowerCamelCase = {
'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 lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = get_model_to_test_mapping(lowerCamelCase__ )
__lowerCamelCase = get_model_to_test_mapping(lowerCamelCase__ )
__lowerCamelCase = {
'BertForMaskedLM': ['BertModelTest'],
'BertForMultipleChoice': ['BertModelTest'],
'BertForNextSentencePrediction': ['BertModelTest'],
'BertForPreTraining': ['BertModelTest'],
'BertForQuestionAnswering': ['BertModelTest'],
'BertForSequenceClassification': ['BertModelTest'],
'BertForTokenClassification': ['BertModelTest'],
'BertLMHeadModel': ['BertModelTest'],
'BertModel': ['BertModelTest'],
}
__lowerCamelCase = {
'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 lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = get_model_to_tester_mapping(lowerCamelCase__ )
__lowerCamelCase = get_model_to_tester_mapping(lowerCamelCase__ )
__lowerCamelCase = {
'BertForMaskedLM': ['BertModelTester'],
'BertForMultipleChoice': ['BertModelTester'],
'BertForNextSentencePrediction': ['BertModelTester'],
'BertForPreTraining': ['BertModelTester'],
'BertForQuestionAnswering': ['BertModelTester'],
'BertForSequenceClassification': ['BertModelTester'],
'BertForTokenClassification': ['BertModelTester'],
'BertLMHeadModel': ['BertModelTester'],
'BertModel': ['BertModelTester'],
}
__lowerCamelCase = {
'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__ )
| 90 |
from math import sqrt
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(UpperCamelCase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCamelCase_ ( UpperCamelCase__ : int = 1_0001 ) -> int:
"""simple docstring"""
__lowerCamelCase = 0
__lowerCamelCase = 1
while count != nth and number < 3:
number += 1
if is_prime(UpperCamelCase__ ):
count += 1
while count != nth:
number += 2
if is_prime(UpperCamelCase__ ):
count += 1
return number
if __name__ == "__main__":
print(f'''{solution() = }''')
| 90 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCamelCase__ ( a , a , a , a , a ) -> str:
# Load configuration defined in the metadata file
with open(_lowerCAmelCase ) as metadata_file:
_A: Dict = json.load(_lowerCAmelCase )
_A: List[Any] = LukeConfig(use_entity_aware_attention=_lowerCAmelCase , **metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
_A: List[Any] = torch.load(_lowerCAmelCase , map_location='''cpu''' )["module"]
# Load the entity vocab file
_A: List[Any] = load_original_entity_vocab(_lowerCAmelCase )
# add an entry for [MASK2]
_A: Union[str, Any] = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
_A: Tuple = XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
_A: str = AddedToken('''<ent>''' , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase )
_A: Any = AddedToken('''<ent2>''' , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f"""Saving tokenizer to {pytorch_dump_folder_path}""" )
tokenizer.save_pretrained(_lowerCAmelCase )
with open(os.path.join(_lowerCAmelCase , '''tokenizer_config.json''' ) , '''r''' ) as f:
_A: Optional[int] = json.load(_lowerCAmelCase )
_A: Dict = "MLukeTokenizer"
with open(os.path.join(_lowerCAmelCase , '''tokenizer_config.json''' ) , '''w''' ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
with open(os.path.join(_lowerCAmelCase , MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ) , '''w''' ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
_A: Dict = MLukeTokenizer.from_pretrained(_lowerCAmelCase )
# Initialize the embeddings of the special tokens
_A: List[str] = tokenizer.convert_tokens_to_ids(['''@'''] )[0]
_A: Optional[Any] = tokenizer.convert_tokens_to_ids(['''#'''] )[0]
_A: Any = state_dict["embeddings.word_embeddings.weight"]
_A: str = word_emb[ent_init_index].unsqueeze(0 )
_A: Union[str, Any] = word_emb[enta_init_index].unsqueeze(0 )
_A: int = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
_A: List[str] = state_dict[bias_name]
_A: Optional[Any] = decoder_bias[ent_init_index].unsqueeze(0 )
_A: List[Any] = decoder_bias[enta_init_index].unsqueeze(0 )
_A: Any = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
_A: Dict = f"""encoder.layer.{layer_index}.attention.self."""
_A: Union[str, Any] = state_dict[prefix + matrix_name]
_A: Optional[int] = state_dict[prefix + matrix_name]
_A: Any = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
_A: List[str] = state_dict["entity_embeddings.entity_embeddings.weight"]
_A: List[Any] = entity_emb[entity_vocab["[MASK]"]].unsqueeze(0 )
_A: int = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
_A: List[str] = state_dict["entity_predictions.bias"]
_A: List[Any] = entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0 )
_A: str = torch.cat([entity_prediction_bias, entity_mask_bias] )
_A: str = LukeForMaskedLM(config=_lowerCAmelCase ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
_A: str = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
_A: Dict = state_dict[key]
else:
_A: Optional[int] = state_dict[key]
_A: Optional[int] = model.load_state_dict(_lowerCAmelCase , strict=_lowerCAmelCase )
if set(_lowerCAmelCase ) != {"luke.embeddings.position_ids"}:
raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}""" )
if set(_lowerCAmelCase ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(f"""Unexpected missing_keys: {missing_keys}""" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
_A: List[Any] = MLukeTokenizer.from_pretrained(_lowerCAmelCase , task='''entity_classification''' )
_A: Optional[Any] = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
_A: int = (0, 9)
_A: Dict = tokenizer(_lowerCAmelCase , entity_spans=[span] , return_tensors='''pt''' )
_A: Any = model(**_lowerCAmelCase )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_A: Dict = torch.Size((1, 33, 7_68) )
_A: List[str] = torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}""" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , _lowerCAmelCase , atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_A: Union[str, Any] = torch.Size((1, 1, 7_68) )
_A: Any = torch.tensor([[-0.1482, 0.0609, 0.0322]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
f"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
f""" {expected_shape}""" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , _lowerCAmelCase , atol=1E-4 ):
raise ValueError
# Verify masked word/entity prediction
_A: Tuple = MLukeTokenizer.from_pretrained(_lowerCAmelCase )
_A: Any = "Tokyo is the capital of <mask>."
_A: List[Any] = (24, 30)
_A: Dict = tokenizer(_lowerCAmelCase , entity_spans=[span] , return_tensors='''pt''' )
_A: Any = model(**_lowerCAmelCase )
_A: Optional[Any] = encoding["input_ids"][0].tolist()
_A: List[Any] = input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
_A: List[str] = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(_lowerCAmelCase )
_A: int = outputs.entity_logits[0][0].argmax().item()
_A: Optional[int] = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(_lowerCAmelCase ) )
model.save_pretrained(_lowerCAmelCase )
def lowerCamelCase__ ( a ) -> Any:
_A: Optional[Any] = ["[MASK]", "[PAD]", "[UNK]"]
_A: int = [json.loads(_lowerCAmelCase ) for line in open(_lowerCAmelCase )]
_A: str = {}
for entry in data:
_A: str = entry["id"]
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
_A: str = entity_id
break
_A: Any = f"""{language}:{entity_name}"""
_A: List[Any] = entity_id
return new_mapping
if __name__ == "__main__":
UpperCAmelCase__ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
parser.add_argument(
'--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
)
parser.add_argument(
'--entity_vocab_path',
default=None,
type=str,
help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
)
parser.add_argument(
'--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
)
UpperCAmelCase__ : List[Any] = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 354 |
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 : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[str]=3 , lowerCAmelCase_ : List[str]=3_2 , lowerCAmelCase_ : Union[str, Any]=3 , lowerCAmelCase_ : int=1_0 , lowerCAmelCase_ : Tuple=[1_0, 2_0, 3_0, 4_0] , lowerCAmelCase_ : Optional[Any]=[1, 1, 2, 1] , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Optional[Any]="relu" , lowerCAmelCase_ : int=3 , lowerCAmelCase_ : List[Any]=None , ):
"""simple docstring"""
_A: str = parent
_A: List[Any] = batch_size
_A: Optional[int] = image_size
_A: Dict = num_channels
_A: str = embeddings_size
_A: Any = hidden_sizes
_A: Dict = depths
_A: Any = is_training
_A: int = use_labels
_A: Tuple = hidden_act
_A: int = num_labels
_A: int = scope
_A: str = len(lowerCAmelCase_ )
def __magic_name__ ( self : Dict ):
"""simple docstring"""
_A: Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_A: Union[str, Any] = self.get_config()
return config, pixel_values
def __magic_name__ ( self : str ):
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def __magic_name__ ( self : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str ):
"""simple docstring"""
_A: str = FlaxRegNetModel(config=lowerCAmelCase_ )
_A: Optional[int] = model(lowerCAmelCase_ )
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def __magic_name__ ( self : str , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple ):
"""simple docstring"""
_A: Union[str, Any] = self.num_labels
_A: Union[str, Any] = FlaxRegNetForImageClassification(config=lowerCAmelCase_ )
_A: str = model(lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__ ( self : List[Any] ):
"""simple docstring"""
_A: str = self.prepare_config_and_inputs()
_A , _A: Optional[int] = config_and_inputs
_A: Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Tuple = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
__UpperCamelCase : Union[str, Any] = False
__UpperCamelCase : List[Any] = False
__UpperCamelCase : int = False
def __magic_name__ ( self : int ):
"""simple docstring"""
_A: int = FlaxRegNetModelTester(self )
_A: Union[str, Any] = ConfigTester(self , config_class=lowerCAmelCase_ , has_text_modality=lowerCAmelCase_ )
def __magic_name__ ( self : str ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __magic_name__ ( self : int ):
"""simple docstring"""
return
def __magic_name__ ( self : Tuple ):
"""simple docstring"""
_A: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_ )
def __magic_name__ ( self : Tuple ):
"""simple docstring"""
_A: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_ )
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def __magic_name__ ( self : str ):
"""simple docstring"""
pass
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def __magic_name__ ( self : Optional[int] ):
"""simple docstring"""
pass
def __magic_name__ ( self : List[Any] ):
"""simple docstring"""
_A , _A: int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A: Union[str, Any] = model_class(lowerCAmelCase_ )
_A: Any = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A: Any = [*signature.parameters.keys()]
_A: Union[str, Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCAmelCase_ )
def __magic_name__ ( self : str ):
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple ):
_A: int = model_class(lowerCAmelCase_ )
_A: List[str] = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
_A: str = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_A: Tuple = self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase_ ) , expected_num_stages + 1 )
_A , _A: List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A: Optional[Any] = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_A: int = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
def __magic_name__ ( self : Dict ):
"""simple docstring"""
_A , _A: str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_A: int = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ )
_A: Union[str, Any] = model_class(lowerCAmelCase_ )
@jax.jit
def model_jitted(lowerCAmelCase_ : Optional[Any] , **lowerCAmelCase_ : Optional[Any] ):
return model(pixel_values=lowerCAmelCase_ , **lowerCAmelCase_ )
with self.subTest('''JIT Enabled''' ):
_A: str = model_jitted(**lowerCAmelCase_ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
_A: List[Any] = model_jitted(**lowerCAmelCase_ ).to_tuple()
self.assertEqual(len(lowerCAmelCase_ ) , len(lowerCAmelCase_ ) )
for jitted_output, output in zip(lowerCAmelCase_ , lowerCAmelCase_ ):
self.assertEqual(jitted_output.shape , output.shape )
def lowerCamelCase__ ( ) -> Tuple:
_A: List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_flax
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __magic_name__ ( self : Union[str, Any] ):
"""simple docstring"""
return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None
@slow
def __magic_name__ ( self : List[str] ):
"""simple docstring"""
_A: List[str] = FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )
_A: str = self.default_image_processor
_A: int = prepare_img()
_A: List[Any] = image_processor(images=lowerCAmelCase_ , return_tensors='''np''' )
_A: str = model(**lowerCAmelCase_ )
# verify the logits
_A: str = (1, 1_0_0_0)
self.assertEqual(outputs.logits.shape , lowerCAmelCase_ )
_A: Tuple = jnp.array([-0.4180, -1.5051, -3.4836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , lowerCAmelCase_ , atol=1e-4 ) )
| 301 | 0 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 146 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class __magic_name__ ( unittest.TestCase):
def UpperCAmelCase__ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = '''ZinengTang/tvlt-base'''
UpperCamelCase__ : int = tempfile.mkdtemp()
def UpperCAmelCase__ ( self : int , **lowerCamelCase__ : List[str] ) -> List[Any]:
'''simple docstring'''
return TvltImageProcessor.from_pretrained(self.checkpoint , **lowerCamelCase__ )
def UpperCAmelCase__ ( self : Optional[Any] , **lowerCamelCase__ : Tuple ) -> List[Any]:
'''simple docstring'''
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **lowerCamelCase__ )
def UpperCAmelCase__ ( self : str ) -> Tuple:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase__ ( self : Any ) -> int:
'''simple docstring'''
UpperCamelCase__ : int = self.get_image_processor()
UpperCamelCase__ : Union[str, Any] = self.get_feature_extractor()
UpperCamelCase__ : List[str] = TvltProcessor(image_processor=lowerCamelCase__ , feature_extractor=lowerCamelCase__ )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase__ : Optional[int] = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , lowerCamelCase__ )
self.assertIsInstance(processor.image_processor , lowerCamelCase__ )
def UpperCAmelCase__ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ : str = self.get_image_processor()
UpperCamelCase__ : List[Any] = self.get_feature_extractor()
UpperCamelCase__ : Dict = TvltProcessor(image_processor=lowerCamelCase__ , feature_extractor=lowerCamelCase__ )
UpperCamelCase__ : Any = np.ones([12000] )
UpperCamelCase__ : Union[str, Any] = feature_extractor(lowerCamelCase__ , return_tensors='''np''' )
UpperCamelCase__ : Any = processor(audio=lowerCamelCase__ , return_tensors='''np''' )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCAmelCase__ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ : List[Any] = self.get_image_processor()
UpperCamelCase__ : Any = self.get_feature_extractor()
UpperCamelCase__ : int = TvltProcessor(image_processor=lowerCamelCase__ , feature_extractor=lowerCamelCase__ )
UpperCamelCase__ : int = np.ones([3, 224, 224] )
UpperCamelCase__ : List[str] = image_processor(lowerCamelCase__ , return_tensors='''np''' )
UpperCamelCase__ : str = processor(images=lowerCamelCase__ , return_tensors='''np''' )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCAmelCase__ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : Any = self.get_image_processor()
UpperCamelCase__ : Dict = self.get_feature_extractor()
UpperCamelCase__ : Union[str, Any] = TvltProcessor(image_processor=lowerCamelCase__ , feature_extractor=lowerCamelCase__ )
UpperCamelCase__ : List[str] = np.ones([12000] )
UpperCamelCase__ : Tuple = np.ones([3, 224, 224] )
UpperCamelCase__ : Optional[Any] = processor(audio=lowerCamelCase__ , images=lowerCamelCase__ )
self.assertListEqual(list(inputs.keys() ) , ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask'''] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase__ ):
processor()
def UpperCAmelCase__ ( self : Dict ) -> int:
'''simple docstring'''
UpperCamelCase__ : List[str] = self.get_image_processor()
UpperCamelCase__ : str = self.get_feature_extractor()
UpperCamelCase__ : Tuple = TvltProcessor(image_processor=lowerCamelCase__ , feature_extractor=lowerCamelCase__ )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match''' , )
| 146 | 1 |
"""simple docstring"""
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class a__ :
def __init__( self : List[Any], lowerCAmelCase : Tuple, lowerCAmelCase : int, lowerCAmelCase : bool = True, lowerCAmelCase : bool = False ) -> Tuple:
lowercase : Dict = scheduler
lowercase : List[Any] = optimizers if isinstance(lowerCAmelCase, (list, tuple) ) else [optimizers]
lowercase : int = split_batches
lowercase : int = step_with_optimizer
lowercase : int = GradientState()
def lowercase ( self : str, *lowerCAmelCase : Optional[int], **lowerCAmelCase : Optional[int] ) -> Optional[int]:
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*lowerCAmelCase, **lowerCAmelCase )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*lowerCAmelCase, **lowerCAmelCase )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
lowercase : Optional[Any] = AcceleratorState().num_processes
for _ in range(lowerCAmelCase ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler, 'total_steps' ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*lowerCAmelCase, **lowerCAmelCase )
else:
self.scheduler.step(*lowerCAmelCase, **lowerCAmelCase )
def lowercase ( self : Dict ) -> Optional[int]:
return self.scheduler.get_last_lr()
def lowercase ( self : Optional[int] ) -> str:
return self.scheduler.state_dict()
def lowercase ( self : Dict, lowerCAmelCase : List[str] ) -> Dict:
self.scheduler.load_state_dict(lowerCAmelCase )
def lowercase ( self : List[Any] ) -> List[Any]:
return self.scheduler.get_lr()
def lowercase ( self : Tuple, *lowerCAmelCase : int, **lowerCAmelCase : Any ) -> int:
return self.scheduler.print_lr(*lowerCAmelCase, **lowerCAmelCase )
| 355 |
"""simple docstring"""
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a__ ( SCREAMING_SNAKE_CASE__, unittest.TestCase ):
_lowerCamelCase = PhobertTokenizer
_lowerCamelCase = False
def lowercase ( self : Optional[Any] ) -> Optional[Any]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase : Optional[Any] = ['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
lowercase : Any = dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowercase : int = ['#version: 0.2', 'l à</w>']
lowercase : Tuple = {'unk_token': '<unk>'}
lowercase : int = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'] )
lowercase : Union[str, Any] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file, 'w', encoding='utf-8' ) as fp:
for token in vocab_tokens:
fp.write(f'''{token} {vocab_tokens[token]}\n''' )
with open(self.merges_file, 'w', encoding='utf-8' ) as fp:
fp.write('\n'.join(lowerCAmelCase ) )
def lowercase ( self : List[str], **lowerCAmelCase : Optional[Any] ) -> Tuple:
kwargs.update(self.special_tokens_map )
return PhobertTokenizer.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase ( self : Union[str, Any], lowerCAmelCase : Dict ) -> Optional[int]:
lowercase : List[Any] = 'Tôi là VinAI Research'
lowercase : Any = 'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
return input_text, output_text
def lowercase ( self : int ) -> Tuple:
lowercase : List[Any] = PhobertTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map )
lowercase : List[str] = 'Tôi là VinAI Research'
lowercase : Dict = 'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
lowercase : int = tokenizer.tokenize(lowerCAmelCase )
print(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
lowercase : str = tokens + [tokenizer.unk_token]
lowercase : Tuple = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ), lowerCAmelCase )
| 53 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class __magic_name__ :
UpperCamelCase__ = PegasusConfig
UpperCamelCase__ = {}
UpperCamelCase__ = '''gelu'''
def __init__( self : int , lowercase_ : Dict , lowercase_ : Optional[int]=13 , lowercase_ : Union[str, Any]=7 , lowercase_ : Optional[int]=True , lowercase_ : Tuple=False , lowercase_ : Any=99 , lowercase_ : List[Any]=32 , lowercase_ : Optional[int]=2 , lowercase_ : Union[str, Any]=4 , lowercase_ : List[str]=37 , lowercase_ : Tuple=0.1 , lowercase_ : int=0.1 , lowercase_ : List[Any]=40 , lowercase_ : Optional[Any]=2 , lowercase_ : Optional[Any]=1 , lowercase_ : str=0 , ):
lowercase_ : int = parent
lowercase_ : int = batch_size
lowercase_ : Union[str, Any] = seq_length
lowercase_ : List[Any] = is_training
lowercase_ : Dict = use_labels
lowercase_ : Optional[Any] = vocab_size
lowercase_ : Union[str, Any] = hidden_size
lowercase_ : Any = num_hidden_layers
lowercase_ : List[str] = num_attention_heads
lowercase_ : int = intermediate_size
lowercase_ : Tuple = hidden_dropout_prob
lowercase_ : Optional[Any] = attention_probs_dropout_prob
lowercase_ : List[Any] = max_position_embeddings
lowercase_ : List[Any] = eos_token_id
lowercase_ : Any = pad_token_id
lowercase_ : Dict = bos_token_id
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : List[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
lowercase_ : Optional[int] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
lowercase_ : Union[str, Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
lowercase_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase_ : int = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
lowercase_ : Optional[int] = prepare_pegasus_inputs_dict(lowercase_ , lowercase_ , lowercase_ )
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : Any , lowercase_ : int ):
lowercase_ : List[str] = TFPegasusModel(config=lowercase_ ).get_decoder()
lowercase_ : List[str] = inputs_dict["""input_ids"""]
lowercase_ : Union[str, Any] = input_ids[:1, :]
lowercase_ : Optional[int] = inputs_dict["""attention_mask"""][:1, :]
lowercase_ : int = inputs_dict["""head_mask"""]
lowercase_ : str = 1
# first forward pass
lowercase_ : Tuple = model(lowercase_ , attention_mask=lowercase_ , head_mask=lowercase_ , use_cache=lowercase_ )
lowercase_ , lowercase_ : List[str] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowercase_ : Optional[int] = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowercase_ : Any = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
lowercase_ : Dict = tf.concat([input_ids, next_tokens] , axis=-1 )
lowercase_ : Optional[int] = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
lowercase_ : Tuple = model(lowercase_ , attention_mask=lowercase_ )[0]
lowercase_ : int = model(lowercase_ , attention_mask=lowercase_ , past_key_values=lowercase_ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
lowercase_ : Optional[Any] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
lowercase_ : List[str] = output_from_no_past[:, -3:, random_slice_idx]
lowercase_ : Optional[int] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowercase_ , lowercase_ , rtol=1E-3 )
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Dict=None , ) -> List[Any]:
if attention_mask is None:
lowercase_ : Optional[int] = tf.cast(tf.math.not_equal(UpperCAmelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
lowercase_ : Optional[int] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
lowercase_ : Optional[int] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowercase_ : Optional[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowercase_ : Tuple = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
UpperCamelCase__ = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
UpperCamelCase__ = (
{
'''conversational''': TFPegasusForConditionalGeneration,
'''feature-extraction''': TFPegasusModel,
'''summarization''': TFPegasusForConditionalGeneration,
'''text2text-generation''': TFPegasusForConditionalGeneration,
'''translation''': TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : str = TFPegasusModelTester(self )
lowercase_ : Union[str, Any] = ConfigTester(self , config_class=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : int ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowercase_ )
@require_sentencepiece
@require_tokenizers
@require_tf
class __magic_name__ ( unittest.TestCase):
UpperCamelCase__ = [
''' 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!" ''',
]
UpperCamelCase__ = [
'''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
''' reduce the risk of wildfires.''',
'''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
] # differs slightly from pytorch, likely due to numerical differences in linear layers
UpperCamelCase__ = '''google/pegasus-xsum'''
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : str ):
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def SCREAMING_SNAKE_CASE_ ( self : Any , **lowercase_ : Dict ):
lowercase_ : Optional[int] = self.translate_src_text(**lowercase_ )
assert self.expected_text == generated_words
def SCREAMING_SNAKE_CASE_ ( self : Any , **lowercase_ : str ):
lowercase_ : Optional[Any] = self.tokenizer(self.src_text , **lowercase_ , padding=lowercase_ , return_tensors="""tf""" )
lowercase_ : Optional[Any] = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=lowercase_ , )
lowercase_ : Tuple = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=lowercase_ )
return generated_words
@slow
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
self._assert_generated_batch_equal_expected()
| 239 | '''simple docstring'''
import operator as op
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> int:
lowercase_ : Optional[Any] = []
lowercase_ : str = lambda UpperCAmelCase__ , UpperCAmelCase__ : int(x / y ) # noqa: E731 integer division operation
lowercase_ : Optional[Any] = {
"""^""": op.pow,
"""*""": op.mul,
"""/""": div,
"""+""": op.add,
"""-""": op.sub,
} # operators & their respective operation
# print table header
print("""Symbol""".center(8 ) , """Action""".center(12 ) , """Stack""" , sep=""" | """ )
print("""-""" * (30 + len(UpperCAmelCase__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(UpperCAmelCase__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ("""push(""" + x + """)""").ljust(12 ) , """,""".join(UpperCAmelCase__ ) , sep=""" | """ )
else:
lowercase_ : str = stack.pop() # pop stack
# output in tabular format
print("""""".rjust(8 ) , ("""pop(""" + b + """)""").ljust(12 ) , """,""".join(UpperCAmelCase__ ) , sep=""" | """ )
lowercase_ : Optional[int] = stack.pop() # pop stack
# output in tabular format
print("""""".rjust(8 ) , ("""pop(""" + a + """)""").ljust(12 ) , """,""".join(UpperCAmelCase__ ) , sep=""" | """ )
stack.append(
str(opr[x](int(UpperCAmelCase__ ) , int(UpperCAmelCase__ ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ("""push(""" + a + x + b + """)""").ljust(12 ) , """,""".join(UpperCAmelCase__ ) , sep=""" | """ , )
return int(stack[0] )
if __name__ == "__main__":
_lowercase : Tuple = input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
print("\n\tResult = ", solve(Postfix))
| 239 | 1 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
@parameterized.expand([(None,), ("""foo.json""",)] )
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Any ):
"""simple docstring"""
UpperCamelCase = GenerationConfig(
do_sample=_lowerCamelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_lowerCamelCase , config_name=_lowerCamelCase )
UpperCamelCase = GenerationConfig.from_pretrained(_lowerCamelCase , config_name=_lowerCamelCase )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , _lowerCamelCase )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , _lowerCamelCase )
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = AutoConfig.from_pretrained("""gpt2""" )
UpperCamelCase = GenerationConfig.from_model_config(_lowerCamelCase )
UpperCamelCase = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(_lowerCamelCase , _lowerCamelCase )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
UpperCamelCase = GenerationConfig()
UpperCamelCase = {
"""max_new_tokens""": 1024,
"""foo""": """bar""",
}
UpperCamelCase = copy.deepcopy(_lowerCamelCase )
UpperCamelCase = generation_config.update(**_lowerCamelCase )
# update_kwargs was not modified (no side effects)
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(_lowerCamelCase , {"""foo""": """bar"""} )
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
UpperCamelCase = GenerationConfig()
UpperCamelCase = """bar"""
with tempfile.TemporaryDirectory("""test-generation-config""" ) as tmp_dir:
generation_config.save_pretrained(_lowerCamelCase )
UpperCamelCase = GenerationConfig.from_pretrained(_lowerCamelCase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , """bar""" )
UpperCamelCase = GenerationConfig.from_model_config(_lowerCamelCase )
assert not hasattr(_lowerCamelCase , """foo""" ) # no new kwargs should be initialized if from config
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
UpperCamelCase = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , _lowerCamelCase )
self.assertEqual(default_config.num_beams , 1 )
UpperCamelCase = GenerationConfig(
do_sample=_lowerCamelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , _lowerCamelCase )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_lowerCamelCase )
UpperCamelCase = GenerationConfig.from_pretrained(_lowerCamelCase , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , _lowerCamelCase )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
@classmethod
def lowerCamelCase_ ( cls : List[Any] ):
"""simple docstring"""
UpperCamelCase = TOKEN
HfFolder.save_token(_lowerCamelCase )
@classmethod
def lowerCamelCase_ ( cls : Optional[Any] ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id="""test-generation-config""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-generation-config-org""" )
except HTTPError:
pass
def lowerCamelCase_ ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = GenerationConfig(
do_sample=_lowerCamelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""test-generation-config""" , use_auth_token=self._token )
UpperCamelCase = GenerationConfig.from_pretrained(f"""{USER}/test-generation-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-generation-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_lowerCamelCase , repo_id="""test-generation-config""" , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
UpperCamelCase = GenerationConfig.from_pretrained(f"""{USER}/test-generation-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def lowerCamelCase_ ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = GenerationConfig(
do_sample=_lowerCamelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""valid_org/test-generation-config-org""" , use_auth_token=self._token )
UpperCamelCase = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-generation-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_lowerCamelCase , repo_id="""valid_org/test-generation-config-org""" , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
UpperCamelCase = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
| 368 | from ....configuration_utils import PretrainedConfig
from ....utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
# TODO: upload to AWS
_SCREAMING_SNAKE_CASE = {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"""
),
}
class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase ):
__lowerCAmelCase = """retribert"""
def __init__( self : Optional[Any] , lowerCamelCase_ : Any=3_0522 , lowerCamelCase_ : List[Any]=768 , lowerCamelCase_ : List[str]=8 , lowerCamelCase_ : Optional[int]=12 , lowerCamelCase_ : str=3072 , lowerCamelCase_ : List[str]="gelu" , lowerCamelCase_ : List[str]=0.1 , lowerCamelCase_ : List[str]=0.1 , lowerCamelCase_ : Dict=512 , lowerCamelCase_ : str=2 , lowerCamelCase_ : int=0.0_2 , lowerCamelCase_ : Any=1E-12 , lowerCamelCase_ : int=True , lowerCamelCase_ : Optional[Any]=128 , lowerCamelCase_ : Optional[Any]=0 , **lowerCamelCase_ : List[str] , ):
"""simple docstring"""
super().__init__(pad_token_id=lowerCamelCase_ , **lowerCamelCase_ )
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = hidden_act
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = share_encoders
UpperCamelCase = projection_dim
| 165 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , __a , __a ):
if len(UpperCAmelCase__ ) != degree + 1:
raise ValueError(
"The number of coefficients should be equal to the degree + 1." )
__lowerCAmelCase = list(UpperCAmelCase__ )
__lowerCAmelCase = degree
def __add__( self , __a ):
if self.degree > polynomial_a.degree:
__lowerCAmelCase = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , UpperCAmelCase__ )
else:
__lowerCAmelCase = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , UpperCAmelCase__ )
def __sub__( self , __a ):
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self ):
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self , __a ):
__lowerCAmelCase = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , UpperCAmelCase__ )
def snake_case ( self , __a ):
__lowerCAmelCase = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self ):
__lowerCAmelCase = ""
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(UpperCAmelCase__ )
return polynomial
def __repr__( self ):
return self.__str__()
def snake_case ( self ):
__lowerCAmelCase = [0] * self.degree
for i in range(self.degree ):
__lowerCAmelCase = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , UpperCAmelCase__ )
def snake_case ( self , __a = 0 ):
__lowerCAmelCase = [0] * (self.degree + 2)
__lowerCAmelCase = constant
for i in range(self.degree + 1 ):
__lowerCAmelCase = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , UpperCAmelCase__ )
def __eq__( self , __a ):
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self , __a ):
return not self.__eq__(UpperCAmelCase__ )
| 57 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__snake_case =logging.get_logger(__name__)
__snake_case ={
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
__snake_case ={
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
__snake_case ={
"""facebook/blenderbot_small-90M""": 512,
}
class UpperCAmelCase_ ( __lowercase ):
lowerCamelCase : Tuple = VOCAB_FILES_NAMES
lowerCamelCase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : Optional[Any] = BlenderbotSmallTokenizer
def __init__( self : Any , UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : int="<|endoftext|>" , UpperCAmelCase__ : Dict="<|endoftext|>" , UpperCAmelCase__ : str="<|endoftext|>" , UpperCAmelCase__ : str=False , UpperCAmelCase__ : Tuple=True , **UpperCAmelCase__ : Optional[Any] , ) -> Any:
super().__init__(
ByteLevelBPETokenizer(
vocab=UpperCAmelCase__ , merges=UpperCAmelCase__ , add_prefix_space=UpperCAmelCase__ , trim_offsets=UpperCAmelCase__ , ) , bos_token=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , **UpperCAmelCase__ , )
lowerCAmelCase = add_prefix_space
def __UpperCAmelCase ( self : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict=None ) -> Any:
lowerCAmelCase = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
lowerCAmelCase = [self.sep_token_id]
lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 4 | 0 |
"""simple docstring"""
def __lowerCamelCase ( a_ : Dict ) -> List[str]:
__SCREAMING_SNAKE_CASE :List[Any] = [0] * len(a_ )
__SCREAMING_SNAKE_CASE :Tuple = []
__SCREAMING_SNAKE_CASE :List[str] = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
if indegree[i] == 0:
queue.append(a_ )
while queue:
__SCREAMING_SNAKE_CASE :Dict = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
__SCREAMING_SNAKE_CASE :List[str] = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(a_ )
print(max(a_ ) )
# Adjacency list of Graph
lowerCamelCase_ = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph) | 239 |
"""simple docstring"""
def __lowerCamelCase ( a_ : int , a_ : int ) -> int:
return int((input_a, input_a).count(0 ) == 0 )
def __lowerCamelCase ( ) -> None:
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
assert and_gate(1 , 1 ) == 1
if __name__ == "__main__":
test_and_gate()
print(and_gate(1, 0))
print(and_gate(0, 0))
print(and_gate(0, 1))
print(and_gate(1, 1)) | 239 | 1 |
def __lowercase ( lowerCamelCase : int ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 175 | def __lowercase ( lowerCamelCase : str , lowerCamelCase : str ):
def get_matched_characters(lowerCamelCase : str , lowerCamelCase : str ) -> str:
UpperCamelCase_ : Tuple = []
UpperCamelCase_ : List[Any] = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
UpperCamelCase_ : int = int(max(0 , i - limit ) )
UpperCamelCase_ : Dict = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowerCamelCase )
UpperCamelCase_ : Dict = F"{_stra[0:_stra.index(lowerCamelCase )]} {_stra[_stra.index(lowerCamelCase ) + 1:]}"
return "".join(lowerCamelCase )
# matching characters
UpperCamelCase_ : str = get_matched_characters(lowerCamelCase , lowerCamelCase )
UpperCamelCase_ : str = get_matched_characters(lowerCamelCase , lowerCamelCase )
UpperCamelCase_ : Union[str, Any] = len(lowerCamelCase )
# transposition
UpperCamelCase_ : int = (
len([(ca, ca) for ca, ca in zip(lowerCamelCase , lowerCamelCase ) if ca != ca] ) // 2
)
if not match_count:
UpperCamelCase_ : Union[str, Any] = 0.0
else:
UpperCamelCase_ : str = (
1
/ 3
* (
match_count / len(lowerCamelCase )
+ match_count / len(lowerCamelCase )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
UpperCamelCase_ : Dict = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world'))
| 175 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class _SCREAMING_SNAKE_CASE ( unittest.TestCase):
def _snake_case ( self , _SCREAMING_SNAKE_CASE )-> List[Any]:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["""bs"""] , model_result["""ss"""] ):
lowerCamelCase_ =model_result["""result"""][batch_size][sequence_length]
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def _snake_case ( self )-> Optional[int]:
lowerCamelCase_ ="""sshleifer/tiny-gpt2"""
lowerCamelCase_ =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ =TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self )-> Tuple:
lowerCamelCase_ ="""sgugger/tiny-distilbert-classification"""
lowerCamelCase_ =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , only_pretrain_model=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ =TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self )-> Optional[int]:
lowerCamelCase_ ="""sshleifer/tiny-gpt2"""
lowerCamelCase_ =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ =TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self )-> str:
lowerCamelCase_ ="""sshleifer/tiny-gpt2"""
lowerCamelCase_ =AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ =TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , [config] )
lowerCamelCase_ =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self )-> Union[str, Any]:
lowerCamelCase_ ="""sshleifer/tiny-gpt2"""
lowerCamelCase_ =AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ =TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , [config] )
lowerCamelCase_ =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self )-> Tuple:
lowerCamelCase_ ="""sshleifer/tiny-gpt2"""
lowerCamelCase_ =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ =TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ =benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _snake_case ( self )-> Optional[int]:
lowerCamelCase_ ="""sshleifer/tiny-gpt2"""
lowerCamelCase_ =AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ =TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , [config] )
lowerCamelCase_ =benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _snake_case ( self )-> Optional[int]:
lowerCamelCase_ ="""patrickvonplaten/t5-tiny-random"""
lowerCamelCase_ =AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ =TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , configs=[config] )
lowerCamelCase_ =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , """Cannot do xla on CPU.""" )
def _snake_case ( self )-> Dict:
lowerCamelCase_ ="""sshleifer/tiny-gpt2"""
lowerCamelCase_ =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , use_xla=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ =TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self )-> Tuple:
lowerCamelCase_ ="""sshleifer/tiny-gpt2"""
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase_ =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=SCREAMING_SNAKE_CASE_ , save_to_csv=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ , """inf_time.csv""" ) , inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ , """inf_mem.csv""" ) , env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ , """env.csv""" ) , multi_process=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ =TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
benchmark.run()
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , """inf_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , """inf_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , """env.csv""" ) ).exists() )
def _snake_case ( self )-> Optional[Any]:
lowerCamelCase_ ="""sshleifer/tiny-gpt2"""
def _check_summary_is_not_empty(_SCREAMING_SNAKE_CASE ):
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """sequential""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """cumulative""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """current""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """total""" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase_ =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(SCREAMING_SNAKE_CASE_ , """log.txt""" ) , log_print=SCREAMING_SNAKE_CASE_ , trace_memory_line_by_line=SCREAMING_SNAKE_CASE_ , eager_mode=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase_ =TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ =benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , """log.txt""" ) ).exists() )
| 355 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : Tuple = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[int] = ['GLPNFeatureExtractor']
__A : Dict = ['GLPNImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = [
'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
'GLPNForDepthEstimation',
'GLPNLayer',
'GLPNModel',
'GLPNPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_glpn import GLPNFeatureExtractor
from .image_processing_glpn import GLPNImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_glpn import (
GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
GLPNForDepthEstimation,
GLPNLayer,
GLPNModel,
GLPNPreTrainedModel,
)
else:
import sys
__A : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 49 | 0 |
import itertools
import string
from collections.abc import Generator, Iterable
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = iter(UpperCamelCase__ )
while True:
snake_case_ = tuple(itertools.islice(UpperCamelCase__ , UpperCamelCase__ ) )
if not chunk:
return
yield chunk
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = ''.join([c.upper() for c in dirty if c in string.ascii_letters] )
snake_case_ = ''
if len(UpperCamelCase__ ) < 2:
return dirty
for i in range(len(UpperCamelCase__ ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(UpperCamelCase__ ) & 1:
clean += "X"
return clean
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = '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
snake_case_ = []
# 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(UpperCamelCase__ )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(UpperCamelCase__ )
return table
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = generate_table(UpperCamelCase__ )
snake_case_ = prepare_input(UpperCamelCase__ )
snake_case_ = ''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(UpperCamelCase__ , 2 ):
snake_case_ , snake_case_ = divmod(table.index(UpperCamelCase__ ) , 5 )
snake_case_ , snake_case_ = divmod(table.index(UpperCamelCase__ ) , 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 __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = generate_table(UpperCamelCase__ )
snake_case_ = ''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(UpperCamelCase__ , 2 ):
snake_case_ , snake_case_ = divmod(table.index(UpperCamelCase__ ) , 5 )
snake_case_ , snake_case_ = divmod(table.index(UpperCamelCase__ ) , 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
| 285 |
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case_ = (
'\'table\' has to be of square shaped array but got a '
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(UpperCamelCase__ )
snake_case_ = np.zeros((rows, columns) )
snake_case_ = np.zeros((rows, columns) )
for i in range(UpperCamelCase__ ):
for j in range(UpperCamelCase__ ):
snake_case_ = sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__ ) )
if upper[j][j] == 0:
raise ArithmeticError('No LU decomposition exists' )
snake_case_ = (table[i][j] - total) / upper[j][j]
snake_case_ = 1
for j in range(UpperCamelCase__ , UpperCamelCase__ ):
snake_case_ = sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__ ) )
snake_case_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 285 | 1 |
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 117 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-encoder': (
'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-scorer': (
'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-openqa': (
'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
),
'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
# See all REALM models at https://huggingface.co/models?filter=realm
}
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
UpperCAmelCase__ : str = "realm"
def __init__( self , A_=30522 , A_=768 , A_=128 , A_=12 , A_=12 , A_=8 , A_=3072 , A_="gelu_new" , A_=0.1 , A_=0.1 , A_=512 , A_=2 , A_=0.02 , A_=1E-12 , A_=256 , A_=10 , A_=1E-3 , A_=5 , A_=320 , A_=13353718 , A_=5000 , A_=1 , A_=0 , A_=2 , **A_ , ) -> Dict:
super().__init__(pad_token_id=A_ , bos_token_id=A_ , eos_token_id=A_ , **A_ )
# Common config
__UpperCamelCase =vocab_size
__UpperCamelCase =max_position_embeddings
__UpperCamelCase =hidden_size
__UpperCamelCase =retriever_proj_size
__UpperCamelCase =num_hidden_layers
__UpperCamelCase =num_attention_heads
__UpperCamelCase =num_candidates
__UpperCamelCase =intermediate_size
__UpperCamelCase =hidden_act
__UpperCamelCase =hidden_dropout_prob
__UpperCamelCase =attention_probs_dropout_prob
__UpperCamelCase =initializer_range
__UpperCamelCase =type_vocab_size
__UpperCamelCase =layer_norm_eps
# Reader config
__UpperCamelCase =span_hidden_size
__UpperCamelCase =max_span_width
__UpperCamelCase =reader_layer_norm_eps
__UpperCamelCase =reader_beam_size
__UpperCamelCase =reader_seq_len
# Retrieval config
__UpperCamelCase =num_block_records
__UpperCamelCase =searcher_beam_size
| 117 | 1 |
def a ( A__ : Dict , A__ : Any , A__ : Dict , A__ : str ) -> Dict:
"""simple docstring"""
_lowercase =[False] * len(A__ )
_lowercase =[]
queue.append(A__ )
_lowercase =True
while queue:
_lowercase =queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(A__ )
_lowercase =True
_lowercase =u
return visited[t]
def a ( A__ : Dict , A__ : Any , A__ : List[Any] ) -> List[Any]:
"""simple docstring"""
_lowercase =[-1] * (len(A__ ))
_lowercase =0
while bfs(A__ , A__ , A__ , A__ ):
_lowercase =float('Inf' )
_lowercase =sink
while s != source:
# Find the minimum value in select path
_lowercase =min(A__ , graph[parent[s]][s] )
_lowercase =parent[s]
max_flow += path_flow
_lowercase =sink
while v != source:
_lowercase =parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
_lowercase =parent[v]
return max_flow
lowercase_ = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
lowercase_ = 0, 5
print(ford_fulkerson(graph, source, sink))
| 205 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
UpperCAmelCase__ : List[Any] = logging.get_logger(__name__)
logging.set_verbosity_info()
def lowerCamelCase__ ( a , a ) -> Dict:
if "xprophetnet" in prophetnet_checkpoint_path:
_A: List[Any] = XLMProphetNetForConditionalGenerationOld.from_pretrained(a )
_A , _A: Union[str, Any] = XLMProphetNetForConditionalGeneration.from_pretrained(
a , output_loading_info=a )
else:
_A: Dict = ProphetNetForConditionalGenerationOld.from_pretrained(a )
_A , _A: Tuple = ProphetNetForConditionalGeneration.from_pretrained(
a , output_loading_info=a )
_A: Optional[int] = ['''key_proj''', '''value_proj''', '''query_proj''']
_A: List[Any] = {
'''self_attn''': '''ngram_self_attn''',
'''cross_attn''': '''encoder_attn''',
'''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
'''feed_forward_layer_norm''': '''final_layer_norm''',
'''feed_forward''': '''''',
'''intermediate''': '''fc1''',
'''output''': '''fc2''',
'''key_proj''': '''k_proj''',
'''query_proj''': '''q_proj''',
'''value_proj''': '''v_proj''',
'''word_embeddings''': '''embed_tokens''',
'''embeddings_layer_norm''': '''emb_layer_norm''',
'''relative_pos_embeddings''': '''relative_linear''',
'''ngram_embeddings''': '''ngram_input_embed''',
'''position_embeddings''': '''embed_positions''',
}
for key in loading_info["missing_keys"]:
_A: List[str] = key.split('''.''' )
if attributes[0] == "lm_head":
_A: Optional[int] = prophet
_A: Tuple = prophet_old
else:
_A: Tuple = prophet.prophetnet
_A: Any = prophet_old.model
_A: int = False
for attribute in attributes:
if attribute in mapping:
_A: Optional[int] = mapping[attribute]
if not hasattr(a , a ) and len(a ) > 0:
_A: int = attribute
elif hasattr(a , a ):
_A: Tuple = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
_A: Union[str, Any] = old_model.weight
logger.info(f"""{attribute} is initialized.""" )
_A: Any = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
_A: str = old_model.bias
logger.info(f"""{attribute} is initialized""" )
_A: Dict = True
break
elif attribute in special_keys and hasattr(a , '''in_proj_weight''' ):
_A: Optional[int] = old_model.in_proj_weight.shape[0] // 3
_A: Tuple = getattr(a , a )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
_A: List[str] = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
_A: List[Any] = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
_A: int = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
_A: Optional[int] = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
_A: List[Any] = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
_A: int = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
_A: Tuple = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
_A: Union[str, Any] = nn.Parameter(old_model.embed_positions.weight[:5_12, :] )
_A: List[Any] = True
break
if attribute.isdigit():
_A: Tuple = model[int(a )]
_A: int = old_model[int(a )]
else:
_A: Union[str, Any] = getattr(a , a )
if old_attribute == "":
_A: Union[str, Any] = old_model
else:
if not hasattr(a , a ):
raise ValueError(f"""{old_model} does not have {old_attribute}""" )
_A: List[Any] = getattr(a , a )
if not is_key_init:
raise ValueError(f"""{key} was not correctly initialized!""" )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
prophet.save_pretrained(a )
if __name__ == "__main__":
UpperCAmelCase__ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
UpperCAmelCase__ : Tuple = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 121 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _A ( __lowercase ):
def lowercase__ ( self : str , __magic_name__ : float ) -> float:
"""simple docstring"""
return 0.0
def _a ( _lowerCamelCase , _lowerCamelCase ) -> tuple[int | float, int | float]:
"""simple docstring"""
__snake_case : Optional[Any] = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
__snake_case : List[Any] = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def _a ( _lowerCamelCase , _lowerCamelCase ) -> None:
"""simple docstring"""
__snake_case : Optional[int] = 512
__snake_case : Union[str, Any] = [1] + [0] * (size - 1)
__snake_case : Optional[Any] = [filter_type.process(_lowerCamelCase ) for item in inputs]
__snake_case : Optional[Any] = [0] * (samplerate - size) # zero-padding
outputs += filler
__snake_case : List[Any] = np.abs(np.fft.fft(_lowerCamelCase ) )
__snake_case : List[Any] = 20 * np.logaa(_lowerCamelCase )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("""Frequency (Hz)""" )
plt.xscale("""log""" )
# Display within reasonable bounds
__snake_case : Optional[int] = get_bounds(_lowerCamelCase , _lowerCamelCase )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel("""Gain (dB)""" )
plt.plot(_lowerCamelCase )
plt.show()
def _a ( _lowerCamelCase , _lowerCamelCase ) -> None:
"""simple docstring"""
__snake_case : Optional[Any] = 512
__snake_case : Optional[Any] = [1] + [0] * (size - 1)
__snake_case : List[Any] = [filter_type.process(_lowerCamelCase ) for item in inputs]
__snake_case : List[str] = [0] * (samplerate - size) # zero-padding
outputs += filler
__snake_case : Any = np.angle(np.fft.fft(_lowerCamelCase ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("""Frequency (Hz)""" )
plt.xscale("""log""" )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel("""Phase shift (Radians)""" )
plt.plot(np.unwrap(_lowerCamelCase , -2 * pi ) )
plt.show()
| 13 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
# See all BART models at https://huggingface.co/models?filter=bart
__UpperCamelCase = {
"vocab_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
},
"merges_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
},
"tokenizer_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
},
}
__UpperCamelCase = {
"facebook/bart-base": 1024,
"facebook/bart-large": 1024,
"facebook/bart-large-mnli": 1024,
"facebook/bart-large-cnn": 1024,
"facebook/bart-large-xsum": 1024,
"yjernite/bart_eli5": 1024,
}
class _A ( __lowercase ):
lowercase__: Any = VOCAB_FILES_NAMES
lowercase__: List[Any] = PRETRAINED_VOCAB_FILES_MAP
lowercase__: Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__: Optional[Any] = ['''input_ids''', '''attention_mask''']
lowercase__: List[str] = BartTokenizer
def __init__( self : Union[str, Any] , __magic_name__ : int=None , __magic_name__ : Tuple=None , __magic_name__ : Dict=None , __magic_name__ : Optional[Any]="replace" , __magic_name__ : int="<s>" , __magic_name__ : Dict="</s>" , __magic_name__ : Union[str, Any]="</s>" , __magic_name__ : Union[str, Any]="<s>" , __magic_name__ : str="<unk>" , __magic_name__ : List[Any]="<pad>" , __magic_name__ : Union[str, Any]="<mask>" , __magic_name__ : Optional[int]=False , __magic_name__ : str=True , **__magic_name__ : Tuple , ) -> List[str]:
"""simple docstring"""
super().__init__(
__magic_name__ , __magic_name__ , tokenizer_file=__magic_name__ , errors=__magic_name__ , bos_token=__magic_name__ , eos_token=__magic_name__ , sep_token=__magic_name__ , cls_token=__magic_name__ , unk_token=__magic_name__ , pad_token=__magic_name__ , mask_token=__magic_name__ , add_prefix_space=__magic_name__ , trim_offsets=__magic_name__ , **__magic_name__ , )
__snake_case : Any = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , __magic_name__ ) != add_prefix_space:
__snake_case : str = getattr(__magic_name__ , pre_tok_state.pop("""type""" ) )
__snake_case : str = add_prefix_space
__snake_case : Union[str, Any] = pre_tok_class(**__magic_name__ )
__snake_case : str = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
__snake_case : Any = """post_processor"""
__snake_case : Any = getattr(self.backend_tokenizer , __magic_name__ , __magic_name__ )
if tokenizer_component_instance:
__snake_case : str = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__snake_case : Tuple = tuple(state["""sep"""] )
if "cls" in state:
__snake_case : int = tuple(state["""cls"""] )
__snake_case : Optional[int] = False
if state.get("""add_prefix_space""" , __magic_name__ ) != add_prefix_space:
__snake_case : Optional[Any] = add_prefix_space
__snake_case : List[str] = True
if state.get("""trim_offsets""" , __magic_name__ ) != trim_offsets:
__snake_case : Optional[int] = trim_offsets
__snake_case : Any = True
if changes_to_apply:
__snake_case : str = getattr(__magic_name__ , state.pop("""type""" ) )
__snake_case : List[Any] = component_class(**__magic_name__ )
setattr(self.backend_tokenizer , __magic_name__ , __magic_name__ )
@property
def lowercase__ ( self : List[Any] ) -> str:
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""" )
return None
return str(self._mask_token )
@mask_token.setter
def lowercase__ ( self : Union[str, Any] , __magic_name__ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__snake_case : Dict = AddedToken(__magic_name__ , lstrip=__magic_name__ , rstrip=__magic_name__ ) if isinstance(__magic_name__ , __magic_name__ ) else value
__snake_case : Union[str, Any] = value
def lowercase__ ( self : Any , *__magic_name__ : Union[str, Any] , **__magic_name__ : Tuple ) -> BatchEncoding:
"""simple docstring"""
__snake_case : Union[str, Any] = kwargs.get("""is_split_into_words""" , __magic_name__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"""to use it with pretokenized inputs.""" )
return super()._batch_encode_plus(*__magic_name__ , **__magic_name__ )
def lowercase__ ( self : Dict , *__magic_name__ : Optional[int] , **__magic_name__ : List[Any] ) -> BatchEncoding:
"""simple docstring"""
__snake_case : Optional[Any] = kwargs.get("""is_split_into_words""" , __magic_name__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"""to use it with pretokenized inputs.""" )
return super()._encode_plus(*__magic_name__ , **__magic_name__ )
def lowercase__ ( self : List[Any] , __magic_name__ : str , __magic_name__ : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
__snake_case : List[str] = self._tokenizer.model.save(__magic_name__ , name=__magic_name__ )
return tuple(__magic_name__ )
def lowercase__ ( self : Dict , __magic_name__ : List[str] , __magic_name__ : Optional[Any]=None ) -> Optional[Any]:
"""simple docstring"""
__snake_case : Optional[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowercase__ ( self : str , __magic_name__ : List[int] , __magic_name__ : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
__snake_case : Optional[int] = [self.sep_token_id]
__snake_case : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 13 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
__lowercase = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def snake_case__ ( _A: Optional[int] , _A: List[str] , _A: Dict , _A: Union[str, Any] , _A: Any ) -> Any:
'''simple docstring'''
for attribute in key.split(""".""" ):
lowerCAmelCase = getattr(_A , _A )
if weight_type is not None:
lowerCAmelCase = getattr(_A , _A ).shape
else:
lowerCAmelCase = hf_pointer.shape
assert hf_shape == value.shape, (
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}"
)
if weight_type == "weight":
lowerCAmelCase = value
elif weight_type == "weight_g":
lowerCAmelCase = value
elif weight_type == "weight_v":
lowerCAmelCase = value
elif weight_type == "bias":
lowerCAmelCase = value
else:
lowerCAmelCase = value
logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def snake_case__ ( _A: Optional[Any] , _A: Union[str, Any] ) -> str:
'''simple docstring'''
lowerCAmelCase = []
lowerCAmelCase = fairseq_model.state_dict()
lowerCAmelCase = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
lowerCAmelCase = None
for name, value in fairseq_dict.items():
lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
_A , _A , _A , _A , hf_model.config.feat_extract_norm == """group""" , )
lowerCAmelCase = True
elif name.split(""".""" )[0] == "proj":
lowerCAmelCase = fairseq_model.proj
lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
lowerCAmelCase = True
if "*" in mapped_key:
lowerCAmelCase = name.split(_A )[0].split(""".""" )[-2]
lowerCAmelCase = mapped_key.replace("""*""" , _A )
if "weight_g" in name:
lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
lowerCAmelCase = """weight_v"""
elif "bias" in name:
lowerCAmelCase = """bias"""
elif "weight" in name:
lowerCAmelCase = """weight"""
else:
lowerCAmelCase = None
set_recursively(_A , _A , _A , _A , _A )
continue
if not is_used:
unused_weights.append(_A )
logger.warning(f"Unused weights: {unused_weights}" )
return proj_weight
def snake_case__ ( _A: Optional[Any] , _A: List[str] , _A: Union[str, Any] , _A: Any , _A: List[str] ) -> Any:
'''simple docstring'''
lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
lowerCAmelCase = name.split(""".""" )
lowerCAmelCase = int(items[0] )
lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
lowerCAmelCase = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
lowerCAmelCase = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
" found."
)
lowerCAmelCase = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
)
lowerCAmelCase = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(_A )
def snake_case__ ( _A: Union[str, Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase , lowerCAmelCase = emb.weight.shape
lowerCAmelCase = nn.Linear(_A , _A , bias=_A )
lowerCAmelCase = emb.weight.data
return lin_layer
def snake_case__ ( _A: List[Any] ) -> List[str]:
'''simple docstring'''
with open(_A , """r""" , encoding="""utf-8""" ) as f:
lowerCAmelCase = f.readlines()
lowerCAmelCase = [line.split(""" """ )[0] for line in lines]
lowerCAmelCase = len(_A )
lowerCAmelCase = {
"""<s>""": 0,
"""<pad>""": 1,
"""</s>""": 2,
"""<unk>""": 3,
}
vocab_dict.update(dict(zip(_A , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def snake_case__ ( _A: List[str] , _A: List[str] , _A: List[str] , _A: Tuple , _A: Optional[int] , _A: Optional[int] , _A: Optional[int] , ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase = WavaVecaConfig.from_pretrained(_A )
lowerCAmelCase = SpeechaTextaConfig.from_pretrained(
_A , vocab_size=_A , decoder_layers=_A , do_stable_layer_norm=_A )
lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_A , return_attention_mask=_A , )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
lowerCAmelCase = model[0].eval()
# set weights for wav2vec2 encoder
lowerCAmelCase = WavaVecaModel(_A )
lowerCAmelCase = recursively_load_weights_wavaveca(model.encoder , _A )
lowerCAmelCase = SpeechaTextaForCausalLM(_A )
lowerCAmelCase , lowerCAmelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=_A )
# set output linear layer
unexpected_keys.remove("""embed_out""" )
lowerCAmelCase = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}" )
logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}" )
lowerCAmelCase = SpeechEncoderDecoderModel(encoder=_A , decoder=_A )
lowerCAmelCase = False
# add projection layer
lowerCAmelCase = nn.Parameter(projection_layer.weight )
lowerCAmelCase = nn.Parameter(projection_layer.bias )
lowerCAmelCase = create_vocab_dict(_A )
with open(os.path.join(_A , """vocab.json""" ) , """w""" ) as fp:
json.dump(_A , _A )
lowerCAmelCase = SpeechaTextaTokenizer(os.path.join(_A , """vocab.json""" ) )
tokenizer.save_pretrained(_A )
lowerCAmelCase = hf_wavavec.config.to_dict()
lowerCAmelCase = tokenizer.pad_token_id
lowerCAmelCase = tokenizer.bos_token_id
lowerCAmelCase = tokenizer.eos_token_id
lowerCAmelCase = """speech_to_text_2"""
lowerCAmelCase = """wav2vec2"""
lowerCAmelCase = SpeechEncoderDecoderConfig.from_dict(_A )
hf_wavavec.save_pretrained(_A )
feature_extractor.save_pretrained(_A )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument(
'''--encoder_config_path''',
default='''facebook/wav2vec2-large-lv60''',
type=str,
help='''Path to hf encoder wav2vec2 checkpoint config''',
)
parser.add_argument(
'''--decoder_config_path''',
default='''facebook/s2t-small-mustc-en-fr-st''',
type=str,
help='''Path to hf decoder s2t checkpoint config''',
)
parser.add_argument('''--vocab_size''', default=1_0_2_2_4, type=int, help='''Vocab size of decoder''')
parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')
__lowercase = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
)
| 272 | '''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class a__( lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : Dict = MvpTokenizer
UpperCAmelCase_ : Optional[Any] = MvpTokenizerFast
UpperCAmelCase_ : str = True
UpperCAmelCase_ : List[Any] = filter_roberta_detectors
def a_ ( self):
"""simple docstring"""
super().setUp()
lowerCAmelCase = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
lowerCAmelCase = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase))))
lowerCAmelCase = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
lowerCAmelCase = {"""unk_token""": """<unk>"""}
lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""])
lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""])
with open(self.vocab_file , """w""" , encoding="""utf-8""") as fp:
fp.write(json.dumps(__lowerCAmelCase) + """\n""")
with open(self.merges_file , """w""" , encoding="""utf-8""") as fp:
fp.write("""\n""".join(__lowerCAmelCase))
def a_ ( self , **__lowerCAmelCase):
"""simple docstring"""
kwargs.update(self.special_tokens_map)
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__lowerCAmelCase)
def a_ ( self , **__lowerCAmelCase):
"""simple docstring"""
kwargs.update(self.special_tokens_map)
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **__lowerCAmelCase)
def a_ ( self , __lowerCAmelCase):
"""simple docstring"""
return "lower newer", "lower newer"
@cached_property
def a_ ( self):
"""simple docstring"""
return MvpTokenizer.from_pretrained("""RUCAIBox/mvp""")
@cached_property
def a_ ( self):
"""simple docstring"""
return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp""")
@require_torch
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
lowerCAmelCase = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase = tokenizer(__lowerCAmelCase , max_length=len(__lowerCAmelCase) , padding=__lowerCAmelCase , return_tensors="""pt""")
self.assertIsInstance(__lowerCAmelCase , __lowerCAmelCase)
self.assertEqual((2, 9) , batch.input_ids.shape)
self.assertEqual((2, 9) , batch.attention_mask.shape)
lowerCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase)
# Test that special tokens are reset
@require_torch
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase = tokenizer(__lowerCAmelCase , padding=__lowerCAmelCase , return_tensors="""pt""")
# check if input_ids are returned and no labels
self.assertIn("""input_ids""" , __lowerCAmelCase)
self.assertIn("""attention_mask""" , __lowerCAmelCase)
self.assertNotIn("""labels""" , __lowerCAmelCase)
self.assertNotIn("""decoder_attention_mask""" , __lowerCAmelCase)
@require_torch
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = [
"""Summary of the text.""",
"""Another summary.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase = tokenizer(text_target=__lowerCAmelCase , max_length=32 , padding="""max_length""" , return_tensors="""pt""")
self.assertEqual(32 , targets["""input_ids"""].shape[1])
@require_torch
def a_ ( self):
"""simple docstring"""
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase = tokenizer(
["""I am a small frog""" * 1024, """I am a small frog"""] , padding=__lowerCAmelCase , truncation=__lowerCAmelCase , return_tensors="""pt""")
self.assertIsInstance(__lowerCAmelCase , __lowerCAmelCase)
self.assertEqual(batch.input_ids.shape , (2, 1024))
@require_torch
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = ["""A long paragraph for summarization."""]
lowerCAmelCase = [
"""Summary of the text.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase = tokenizer(__lowerCAmelCase , text_target=__lowerCAmelCase , return_tensors="""pt""")
lowerCAmelCase = inputs["""input_ids"""]
lowerCAmelCase = inputs["""labels"""]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())
def a_ ( self):
"""simple docstring"""
pass
def a_ ( self):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase)
lowerCAmelCase = self.tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase)
lowerCAmelCase = """A, <mask> AllenNLP sentence."""
lowerCAmelCase = tokenizer_r.encode_plus(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , return_token_type_ids=__lowerCAmelCase)
lowerCAmelCase = tokenizer_p.encode_plus(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , return_token_type_ids=__lowerCAmelCase)
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["""token_type_ids"""]) , sum(tokens_p["""token_type_ids"""]))
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["""attention_mask"""]) / len(tokens_r["""attention_mask"""]) , sum(tokens_p["""attention_mask"""]) / len(tokens_p["""attention_mask"""]) , )
lowerCAmelCase = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
lowerCAmelCase = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""])
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["""input_ids"""] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
self.assertSequenceEqual(tokens_r["""input_ids"""] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
self.assertSequenceEqual(
__lowerCAmelCase , ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])
self.assertSequenceEqual(
__lowerCAmelCase , ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])
| 272 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : Any = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class __UpperCamelCase ( lowercase__ ):
SCREAMING_SNAKE_CASE = """trocr"""
SCREAMING_SNAKE_CASE = ["""past_key_values"""]
SCREAMING_SNAKE_CASE = {
"""num_attention_heads""": """decoder_attention_heads""",
"""hidden_size""": """d_model""",
"""num_hidden_layers""": """decoder_layers""",
}
def __init__(self : Optional[Any] , __SCREAMING_SNAKE_CASE : int=5_0_2_6_5 , __SCREAMING_SNAKE_CASE : Optional[int]=1_0_2_4 , __SCREAMING_SNAKE_CASE : List[Any]=1_2 , __SCREAMING_SNAKE_CASE : Optional[Any]=1_6 , __SCREAMING_SNAKE_CASE : int=4_0_9_6 , __SCREAMING_SNAKE_CASE : Optional[int]="gelu" , __SCREAMING_SNAKE_CASE : Union[str, Any]=5_1_2 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , __SCREAMING_SNAKE_CASE : Any=0.0 , __SCREAMING_SNAKE_CASE : Tuple=0.0 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : List[Any]=0.0_2 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : Tuple=True , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : str=1 , __SCREAMING_SNAKE_CASE : Dict=0 , __SCREAMING_SNAKE_CASE : Optional[Any]=2 , **__SCREAMING_SNAKE_CASE : Union[str, Any] , ):
A = vocab_size
A = d_model
A = decoder_layers
A = decoder_attention_heads
A = decoder_ffn_dim
A = activation_function
A = max_position_embeddings
A = dropout
A = attention_dropout
A = activation_dropout
A = init_std
A = decoder_layerdrop
A = use_cache
A = scale_embedding
A = use_learned_position_embeddings
A = layernorm_embedding
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , decoder_start_token_id=lowercase_ , **lowercase_ , )
| 351 |
"""simple docstring"""
import numpy as np
import datasets
__A : Optional[int] = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n'
__A : Any = '\\n@article{de2000mahalanobis,\n title={The mahalanobis distance},\n author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n journal={Chemometrics and intelligent laboratory systems},\n volume={50},\n number={1},\n pages={1--18},\n year={2000},\n publisher={Elsevier}\n}\n'
__A : List[str] = '\nArgs:\n X: List of datapoints to be compared with the `reference_distribution`.\n reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n >>> mahalanobis_metric = datasets.load_metric("mahalanobis")\n >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n >>> print(results)\n {\'mahalanobis\': array([0.5])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase ( datasets.Metric ):
def SCREAMING_SNAKE_CASE__ (self : Dict):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"X": datasets.Sequence(datasets.Value("float" , id="sequence") , id="X"),
}) , )
def SCREAMING_SNAKE_CASE__ (self : Optional[int] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any]):
# convert to numpy arrays
A = np.array(__SCREAMING_SNAKE_CASE)
A = np.array(__SCREAMING_SNAKE_CASE)
# Assert that arrays are 2D
if len(X.shape) != 2:
raise ValueError("Expected `X` to be a 2D vector")
if len(reference_distribution.shape) != 2:
raise ValueError("Expected `reference_distribution` to be a 2D vector")
if reference_distribution.shape[0] < 2:
raise ValueError(
"Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension")
# Get mahalanobis distance for each prediction
A = X - np.mean(__SCREAMING_SNAKE_CASE)
A = np.cov(reference_distribution.T)
try:
A = np.linalg.inv(__SCREAMING_SNAKE_CASE)
except np.linalg.LinAlgError:
A = np.linalg.pinv(__SCREAMING_SNAKE_CASE)
A = np.dot(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
A = np.dot(__SCREAMING_SNAKE_CASE , X_minus_mu.T).diagonal()
return {"mahalanobis": mahal_dist}
| 57 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class A_ ( _snake_case ):
'''simple docstring'''
def UpperCAmelCase_ ( self : Optional[int] ) -> str:
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def UpperCAmelCase_ ( self : Optional[int] ) -> str:
UpperCAmelCase : int = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
return Dataset.from_dict(lowercase_ )
def UpperCAmelCase_ ( self : str ) -> int:
UpperCAmelCase : Dict = self._create_example_records()
UpperCAmelCase : str = Dataset.from_list(lowercase_ )
self.assertListEqual(dset.column_names , ['col_1', 'col_2'] )
for i, r in enumerate(lowercase_ ):
self.assertDictEqual(lowercase_ , example_records[i] )
def UpperCAmelCase_ ( self : Tuple ) -> Tuple:
UpperCAmelCase : Optional[Any] = self._create_example_records()
UpperCAmelCase : Any = Dataset.from_list(lowercase_ )
UpperCAmelCase : Any = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info , dset_from_dict.info )
def UpperCAmelCase_ ( self : Any ) -> Optional[int]: # checks what happens with missing columns
UpperCAmelCase : str = [{'col_1': 1}, {'col_2': 'x'}]
UpperCAmelCase : str = Dataset.from_list(lowercase_ )
self.assertDictEqual(dset[0] , {'col_1': 1} )
self.assertDictEqual(dset[1] , {'col_1': None} ) # NB: first record is used for columns
def UpperCAmelCase_ ( self : Tuple ) -> List[str]: # checks if the type can be inferred from the second record
UpperCAmelCase : List[Any] = [{'col_1': []}, {'col_1': [1, 2]}]
UpperCAmelCase : Union[str, Any] = Dataset.from_list(lowercase_ )
self.assertEqual(dset.info.features['col_1'] , Sequence(Value('int64' ) ) )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
UpperCAmelCase : Tuple = Dataset.from_list([] )
self.assertEqual(len(lowercase_ ) , 0 )
self.assertListEqual(dset.column_names , [] )
| 151 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
2: [4, 5],
3: [4],
4: [2, 3],
5: [2],
6: [0, 7],
7: [6],
8: [],
9: [1],
},
{
0: [4],
1: [6],
2: [],
3: [5, 6, 7],
4: [0, 6],
5: [3, 8, 9],
6: [1, 3, 4, 7],
7: [3, 6, 8, 9],
8: [5, 7],
9: [5, 7],
},
{
0: [1, 3],
1: [0, 2, 4],
2: [1, 3, 4],
3: [0, 2, 4],
4: [1, 2, 3],
},
][index]
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : List[Any] = 0
UpperCAmelCase : Dict = len(UpperCAmelCase_ ) # No of vertices in graph
UpperCAmelCase : Tuple = [0] * n
UpperCAmelCase : List[Any] = [False] * n
def dfs(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : List[str] = True
UpperCAmelCase : Dict = id_
id_ += 1
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
dfs(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , id_ )
UpperCAmelCase : Optional[Any] = 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 : Dict = min(low[at] , low[to] )
UpperCAmelCase : list[tuple[int, int]] = []
for i in range(UpperCAmelCase_ ):
if not visited[i]:
dfs(UpperCAmelCase_ , -1 , UpperCAmelCase_ , id_ )
return bridges
if __name__ == "__main__":
import doctest
doctest.testmod()
| 151 | 1 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def _A ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str = None ):
"""simple docstring"""
if version.parse(hfh.__version__ ).release < version.parse("0.11.0" ).release:
# old versions of hfh don't url-encode the file path
a__ : List[Any] =quote(__lowerCAmelCase )
return hfh.hf_hub_url(__lowerCAmelCase , __lowerCAmelCase , repo_type="dataset" , revision=__lowerCAmelCase )
| 362 |
from maths.prime_check import is_prime
def _A ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
a__ : Dict =f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE )
if is_prime(SCREAMING_SNAKE_CASE ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 148 | 0 |
import math
def A ( a_ ) -> list[int]:
__UpperCamelCase : Any =[]
__UpperCamelCase : Dict =2
__UpperCamelCase : int =int(math.sqrt(a_ ) ) # Size of every segment
__UpperCamelCase : List[str] =[True] * (end + 1)
__UpperCamelCase : int =[]
while start <= end:
if temp[start] is True:
in_prime.append(a_ )
for i in range(start * start ,end + 1 ,a_ ):
__UpperCamelCase : Union[str, Any] =False
start += 1
prime += in_prime
__UpperCamelCase : Union[str, Any] =end + 1
__UpperCamelCase : List[Any] =min(2 * end ,a_ )
while low <= n:
__UpperCamelCase : Optional[int] =[True] * (high - low + 1)
for each in in_prime:
__UpperCamelCase : Optional[Any] =math.floor(low / each ) * each
if t < low:
t += each
for j in range(a_ ,high + 1 ,a_ ):
__UpperCamelCase : int =False
for j in range(len(a_ ) ):
if temp[j] is True:
prime.append(j + low )
__UpperCamelCase : str =high + 1
__UpperCamelCase : Optional[Any] =min(high + end ,a_ )
return prime
print(sieve(10**6))
| 71 |
import doctest
from collections import deque
import numpy as np
class A_ :
'''simple docstring'''
def __init__(self ) -> None:
__UpperCAmelCase = [2, 1, 2, -1]
__UpperCAmelCase = [1, 2, 3, 4]
def lowerCAmelCase_ (self ) -> list[float]:
__UpperCAmelCase = len(self.first_signal )
__UpperCAmelCase = len(self.second_signal )
__UpperCAmelCase = max(lowercase__ , lowercase__ )
# create a zero matrix of max_length x max_length
__UpperCAmelCase = [[0] * max_length for i in range(lowercase__ )]
# fills the smaller signal with zeros to make both signals of same length
if length_first_signal < length_second_signal:
self.first_signal += [0] * (max_length - length_first_signal)
elif length_first_signal > length_second_signal:
self.second_signal += [0] * (max_length - length_second_signal)
for i in range(lowercase__ ):
__UpperCAmelCase = deque(self.second_signal )
rotated_signal.rotate(lowercase__ )
for j, item in enumerate(lowercase__ ):
matrix[i][j] += item
# multiply the matrix with the first signal
__UpperCAmelCase = np.matmul(np.transpose(lowercase__ ) , np.transpose(self.first_signal ) )
# rounding-off to two decimal places
return [round(lowercase__ , 2 ) for i in final_signal]
if __name__ == "__main__":
doctest.testmod()
| 333 | 0 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def UpperCAmelCase_ (__a : List[str] , __a : Union[str, Any] , __a : str ):
"""simple docstring"""
_a : Any = 0
if start < end:
_a : Optional[Any] = randint(__a , __a )
_a : Union[str, Any] = a[end]
_a : Optional[Any] = a[pivot]
_a : Any = temp
_a, _a : Any = _in_place_partition(__a , __a , __a )
count += _in_place_quick_sort(__a , __a , p - 1 )
count += _in_place_quick_sort(__a , p + 1 , __a )
return count
def UpperCAmelCase_ (__a : Tuple , __a : Union[str, Any] , __a : Tuple ):
"""simple docstring"""
_a : List[Any] = 0
_a : Tuple = randint(__a , __a )
_a : Optional[int] = a[end]
_a : List[Any] = a[pivot]
_a : Dict = temp
_a : Dict = start - 1
for index in range(__a , __a ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
_a : Optional[Any] = new_pivot_index + 1
_a : Optional[Any] = a[new_pivot_index]
_a : Tuple = a[index]
_a : Tuple = temp
_a : List[Any] = a[new_pivot_index + 1]
_a : Optional[int] = a[end]
_a : Tuple = temp
return new_pivot_index + 1, count
__lowerCAmelCase = TemporaryFile()
__lowerCAmelCase = 1_0_0 # 1000 elements are to be sorted
__lowerCAmelCase , __lowerCAmelCase = 0, 1 # mean and standard deviation
__lowerCAmelCase = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)
outfile.seek(0) # using the same array
__lowerCAmelCase = np.load(outfile)
__lowerCAmelCase = len(M) - 1
__lowerCAmelCase = _in_place_quick_sort(M, 0, r)
print(
"""No of Comparisons for 100 elements selected from a standard normal distribution"""
"""is :"""
)
print(z)
| 5 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
__lowerCAmelCase = threading.Lock()
__lowerCAmelCase = None
__lowerCAmelCase = {
"""debug""": logging.DEBUG,
"""info""": logging.INFO,
"""warning""": logging.WARNING,
"""error""": logging.ERROR,
"""critical""": logging.CRITICAL,
}
__lowerCAmelCase = logging.WARNING
__lowerCAmelCase = True
def UpperCAmelCase_ ():
"""simple docstring"""
_a : Dict = os.getenv('TRANSFORMERS_VERBOSITY' , __a )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
f"""has to be one of: { ', '.join(log_levels.keys() ) }""" )
return _default_log_level
def UpperCAmelCase_ ():
"""simple docstring"""
return __name__.split('.' )[0]
def UpperCAmelCase_ ():
"""simple docstring"""
return logging.getLogger(_get_library_name() )
def UpperCAmelCase_ ():
"""simple docstring"""
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
_a : str = logging.StreamHandler() # Set sys.stderr as stream.
_a : Optional[Any] = sys.stderr.flush
# Apply our default configuration to the library root logger.
_a : List[Any] = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
_a : List[str] = False
def UpperCAmelCase_ ():
"""simple docstring"""
global _default_handler
with _lock:
if not _default_handler:
return
_a : int = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
_a : str = None
def UpperCAmelCase_ ():
"""simple docstring"""
return log_levels
def UpperCAmelCase_ (__a : Optional[str] = None ):
"""simple docstring"""
if name is None:
_a : List[Any] = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def UpperCAmelCase_ (__a : int ):
"""simple docstring"""
_configure_library_root_logger()
_get_library_root_logger().setLevel(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
return set_verbosity(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
return set_verbosity(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
return set_verbosity(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
return set_verbosity(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def UpperCAmelCase_ ():
"""simple docstring"""
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def UpperCAmelCase_ (__a : logging.Handler ):
"""simple docstring"""
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(__a )
def UpperCAmelCase_ (__a : logging.Handler ):
"""simple docstring"""
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
_configure_library_root_logger()
_a : Union[str, Any] = False
def UpperCAmelCase_ ():
"""simple docstring"""
_configure_library_root_logger()
_a : Dict = True
def UpperCAmelCase_ ():
"""simple docstring"""
_a : Any = _get_library_root_logger().handlers
for handler in handlers:
_a : Union[str, Any] = logging.Formatter('[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s' )
handler.setFormatter(__a )
def UpperCAmelCase_ ():
"""simple docstring"""
_a : Union[str, Any] = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(__a )
def UpperCAmelCase_ (self : Union[str, Any] , *__a : Union[str, Any] , **__a : Union[str, Any] ):
"""simple docstring"""
_a : Union[str, Any] = os.getenv('TRANSFORMERS_NO_ADVISORY_WARNINGS' , __a )
if no_advisory_warnings:
return
self.warning(*__a , **__a )
__lowerCAmelCase = warning_advice
@functools.lru_cache(__a )
def UpperCAmelCase_ (self : int , *__a : Optional[Any] , **__a : Any ):
"""simple docstring"""
self.warning(*__a , **__a )
__lowerCAmelCase = warning_once
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : Any ,*_a : Tuple ,**_a : int ): # pylint: disable=unused-argument
'''simple docstring'''
_a : int = args[0] if args else None
def __iter__( self : str ):
'''simple docstring'''
return iter(self._iterator )
def __getattr__( self : List[Any] ,_a : int ):
'''simple docstring'''
def empty_fn(*_a : Optional[Any] ,**_a : Any ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self : List[str] ):
'''simple docstring'''
return self
def __exit__( self : List[str] ,_a : str ,_a : List[Any] ,_a : str ):
'''simple docstring'''
return
class UpperCAmelCase__ :
"""simple docstring"""
def __call__( self : Union[str, Any] ,*_a : Tuple ,**_a : Tuple ):
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm(*_a ,**_a )
else:
return EmptyTqdm(*_a ,**_a )
def __lowercase ( self : str ,*_a : List[Any] ,**_a : Any ):
'''simple docstring'''
_a : Any = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*_a ,**_a )
def __lowercase ( self : List[str] ):
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
__lowerCAmelCase = _tqdm_cls()
def UpperCAmelCase_ ():
"""simple docstring"""
global _tqdm_active
return bool(_tqdm_active )
def UpperCAmelCase_ ():
"""simple docstring"""
global _tqdm_active
_a : str = True
hf_hub_utils.enable_progress_bars()
def UpperCAmelCase_ ():
"""simple docstring"""
global _tqdm_active
_a : Dict = False
hf_hub_utils.disable_progress_bars()
| 5 | 1 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def A ( ) -> int:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
__UpperCamelCase : Dict ='__test_patch_submodule_mock__'
with patch_submodule(_test_patching ,'os.path.join' ,a_ ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os ,_PatchedModuleObj )
assert isinstance(_test_patching.os.path ,_PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path ,_PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os ,_PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path ,_PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path ,_PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def A ( ) -> Any:
assert _test_patching.open is open
__UpperCamelCase : Optional[int] ='__test_patch_submodule_builtin_mock__'
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching ,'open' ,a_ ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def A ( ) -> Union[str, Any]:
# pandas.read_csv is not present in _test_patching
__UpperCamelCase : List[str] ='__test_patch_submodule_missing_mock__'
with patch_submodule(_test_patching ,'pandas.read_csv' ,a_ ):
pass
def A ( ) -> str:
# builtin should always be mocked even if they're not in the globals
# in case they're loaded at one point
__UpperCamelCase : Any ='__test_patch_submodule_missing_builtin_mock__'
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching ,'len' ,a_ ) is None
with patch_submodule(_test_patching ,'len' ,a_ ):
assert _test_patching.len is mock
assert _test_patching.len is len
def A ( ) -> Dict:
__UpperCamelCase : List[Any] ='__test_patch_submodule_start_and_stop_mock__'
__UpperCamelCase : Tuple =patch_submodule(_test_patching ,'open' ,a_ )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def A ( ) -> Optional[int]:
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
__UpperCamelCase : str ='__test_patch_submodule_successive_join__'
__UpperCamelCase : Optional[int] ='__test_patch_submodule_successive_dirname__'
__UpperCamelCase : Any ='__test_patch_submodule_successive_rename__'
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching ,'os.path.join' ,a_ ):
with patch_submodule(_test_patching ,'os.rename' ,a_ ):
with patch_submodule(_test_patching ,'os.path.dirname' ,a_ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching ,'os.rename' ,a_ ):
with patch_submodule(_test_patching ,'os.path.join' ,a_ ):
with patch_submodule(_test_patching ,'os.path.dirname' ,a_ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def A ( ) -> Any:
__UpperCamelCase : Optional[Any] ='__test_patch_submodule_doesnt_exist_mock__'
with patch_submodule(_test_patching ,'__module_that_doesn_exist__.__attribute_that_doesn_exist__' ,a_ ):
pass
with patch_submodule(_test_patching ,'os.__attribute_that_doesn_exist__' ,a_ ):
pass
| 71 |
"""simple docstring"""
def lowercase (_lowerCAmelCase = 100_0000 ):
__lowerCAmelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , _lowerCAmelCase ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 301 | 0 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> str:
'''simple docstring'''
if isinstance(lowerCAmelCase_, torch.Tensor ):
return image
elif isinstance(lowerCAmelCase_, PIL.Image.Image ):
snake_case_ = [image]
if isinstance(image[0], PIL.Image.Image ):
snake_case_ = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION['''lanczos'''] ) )[None, :] for i in image]
snake_case_ = np.concatenate(lowerCAmelCase_, axis=0 )
snake_case_ = np.array(lowerCAmelCase_ ).astype(np.floataa ) / 2_5_5.0
snake_case_ = image.transpose(0, 3, 1, 2 )
snake_case_ = 2.0 * image - 1.0
snake_case_ = torch.from_numpy(lowerCAmelCase_ )
elif isinstance(image[0], torch.Tensor ):
snake_case_ = torch.cat(lowerCAmelCase_, dim=0 )
return image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=0.9_9_9_5 ) -> Optional[int]:
'''simple docstring'''
if not isinstance(lowerCAmelCase_, np.ndarray ):
snake_case_ = True
snake_case_ = va.device
snake_case_ = va.cpu().numpy()
snake_case_ = va.cpu().numpy()
snake_case_ = np.sum(va * va / (np.linalg.norm(lowerCAmelCase_ ) * np.linalg.norm(lowerCAmelCase_ )) )
if np.abs(lowerCAmelCase_ ) > DOT_THRESHOLD:
snake_case_ = (1 - t) * va + t * va
else:
snake_case_ = np.arccos(lowerCAmelCase_ )
snake_case_ = np.sin(lowerCAmelCase_ )
snake_case_ = theta_a * t
snake_case_ = np.sin(lowerCAmelCase_ )
snake_case_ = np.sin(theta_a - theta_t ) / sin_theta_a
snake_case_ = sin_theta_t / sin_theta_a
snake_case_ = sa * va + sa * va
if inputs_are_torch:
snake_case_ = torch.from_numpy(lowerCAmelCase_ ).to(lowerCAmelCase_ )
return va
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
snake_case_ = F.normalize(lowerCAmelCase_, dim=-1 )
snake_case_ = F.normalize(lowerCAmelCase_, dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
for param in model.parameters():
snake_case_ = value
class a ( _UpperCamelCase ):
def __init__( self : Tuple , lowercase_ : AutoencoderKL , lowercase_ : CLIPTextModel , lowercase_ : CLIPModel , lowercase_ : CLIPTokenizer , lowercase_ : UNetaDConditionModel , lowercase_ : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] , lowercase_ : CLIPFeatureExtractor , lowercase_ : List[str]=None , lowercase_ : str=None , lowercase_ : List[Any]=None , ):
super().__init__()
self.register_modules(
vae=_UpperCAmelCase , text_encoder=_UpperCAmelCase , clip_model=_UpperCAmelCase , tokenizer=_UpperCAmelCase , unet=_UpperCAmelCase , scheduler=_UpperCAmelCase , feature_extractor=_UpperCAmelCase , coca_model=_UpperCAmelCase , coca_tokenizer=_UpperCAmelCase , coca_transform=_UpperCAmelCase , )
snake_case_ = (
feature_extractor.size
if isinstance(feature_extractor.size , _UpperCAmelCase )
else feature_extractor.size['shortest_edge']
)
snake_case_ = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , _UpperCAmelCase )
set_requires_grad(self.clip_model , _UpperCAmelCase )
def A_ ( self : Tuple , lowercase_ : Optional[Union[str, int]] = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
snake_case_ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_UpperCAmelCase )
def A_ ( self : int ):
self.enable_attention_slicing(_UpperCAmelCase )
def A_ ( self : Optional[Any] ):
set_requires_grad(self.vae , _UpperCAmelCase )
def A_ ( self : Any ):
set_requires_grad(self.vae , _UpperCAmelCase )
def A_ ( self : List[Any] ):
set_requires_grad(self.unet , _UpperCAmelCase )
def A_ ( self : Optional[Any] ):
set_requires_grad(self.unet , _UpperCAmelCase )
def A_ ( self : Any , lowercase_ : Union[str, Any] , lowercase_ : Any , lowercase_ : Dict ):
# get the original timestep using init_timestep
snake_case_ = min(int(num_inference_steps * strength ) , _UpperCAmelCase )
snake_case_ = max(num_inference_steps - init_timestep , 0 )
snake_case_ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def A_ ( self : Optional[int] , lowercase_ : Any , lowercase_ : Dict , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[int]=None ):
if not isinstance(_UpperCAmelCase , torch.Tensor ):
raise ValueError(F"`image` has to be of type `torch.Tensor` but is {type(_UpperCAmelCase )}" )
snake_case_ = image.to(device=_UpperCAmelCase , dtype=_UpperCAmelCase )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
snake_case_ = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(_UpperCAmelCase )
]
snake_case_ = torch.cat(_UpperCAmelCase , dim=0 )
else:
snake_case_ = self.vae.encode(_UpperCAmelCase ).latent_dist.sample(_UpperCAmelCase )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
snake_case_ = 0.1_8215 * init_latents
snake_case_ = init_latents.repeat_interleave(_UpperCAmelCase , dim=0 )
snake_case_ = randn_tensor(init_latents.shape , generator=_UpperCAmelCase , device=_UpperCAmelCase , dtype=_UpperCAmelCase )
# get latents
snake_case_ = self.scheduler.add_noise(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
snake_case_ = init_latents
return latents
def A_ ( self : int , lowercase_ : Optional[Any] ):
snake_case_ = self.coca_transform(_UpperCAmelCase ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
snake_case_ = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
snake_case_ = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('''<end_of_text>''' )[0].replace('''<start_of_text>''' , '''''' ).rstrip(''' .,''' )
def A_ ( self : List[Any] , lowercase_ : Any , lowercase_ : List[str] ):
snake_case_ = self.feature_extractor.preprocess(_UpperCAmelCase )
snake_case_ = torch.from_numpy(clip_image_input['''pixel_values'''][0] ).unsqueeze(0 ).to(self.device ).half()
snake_case_ = self.clip_model.get_image_features(_UpperCAmelCase )
snake_case_ = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=_UpperCAmelCase )
snake_case_ = image_embeddings_clip.repeat_interleave(_UpperCAmelCase , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def A_ ( self : Optional[Any] , lowercase_ : int , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : Any , ):
snake_case_ = latents.detach().requires_grad_()
snake_case_ = self.scheduler.scale_model_input(_UpperCAmelCase , _UpperCAmelCase )
# predict the noise residual
snake_case_ = self.unet(_UpperCAmelCase , _UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
snake_case_ = self.scheduler.alphas_cumprod[timestep]
snake_case_ = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
snake_case_ = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
snake_case_ = torch.sqrt(_UpperCAmelCase )
snake_case_ = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , _UpperCAmelCase ):
snake_case_ = self.scheduler.sigmas[index]
snake_case_ = latents - sigma * noise_pred
else:
raise ValueError(F"scheduler type {type(self.scheduler )} not supported" )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
snake_case_ = 1 / 0.1_8215 * sample
snake_case_ = self.vae.decode(_UpperCAmelCase ).sample
snake_case_ = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ = transforms.Resize(self.feature_extractor_size )(_UpperCAmelCase )
snake_case_ = self.normalize(_UpperCAmelCase ).to(latents.dtype )
snake_case_ = self.clip_model.get_image_features(_UpperCAmelCase )
snake_case_ = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=_UpperCAmelCase )
snake_case_ = spherical_dist_loss(_UpperCAmelCase , _UpperCAmelCase ).mean() * clip_guidance_scale
snake_case_ = -torch.autograd.grad(_UpperCAmelCase , _UpperCAmelCase )[0]
if isinstance(self.scheduler , _UpperCAmelCase ):
snake_case_ = latents.detach() + grads * (sigma**2)
snake_case_ = noise_pred_original
else:
snake_case_ = noise_pred_original - torch.sqrt(_UpperCAmelCase ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self : Union[str, Any] , lowercase_ : Union[torch.FloatTensor, PIL.Image.Image] , lowercase_ : Union[torch.FloatTensor, PIL.Image.Image] , lowercase_ : Optional[str] = None , lowercase_ : Optional[str] = None , lowercase_ : Optional[int] = 512 , lowercase_ : Optional[int] = 512 , lowercase_ : float = 0.6 , lowercase_ : Optional[int] = 50 , lowercase_ : Optional[float] = 7.5 , lowercase_ : Optional[int] = 1 , lowercase_ : float = 0.0 , lowercase_ : Optional[float] = 100 , lowercase_ : Optional[torch.Generator] = None , lowercase_ : Optional[str] = "pil" , lowercase_ : bool = True , lowercase_ : float = 0.8 , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , ):
if isinstance(_UpperCAmelCase , _UpperCAmelCase ) and len(_UpperCAmelCase ) != batch_size:
raise ValueError(F"You have passed {batch_size} batch_size, but only {len(_UpperCAmelCase )} generators." )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}." )
if isinstance(_UpperCAmelCase , torch.Generator ) and batch_size > 1:
snake_case_ = [generator] + [None] * (batch_size - 1)
snake_case_ = [
('model', self.coca_model is None),
('tokenizer', self.coca_tokenizer is None),
('transform', self.coca_transform is None),
]
snake_case_ = [x[0] for x in coca_is_none if x[1]]
snake_case_ = ', '.join(_UpperCAmelCase )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(_UpperCAmelCase ):
raise ValueError(
F"Content prompt is None and CoCa [{coca_is_none_str}] is None."
F"Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
snake_case_ = self.get_image_description(_UpperCAmelCase )
if style_prompt is None:
if len(_UpperCAmelCase ):
raise ValueError(
F"Style prompt is None and CoCa [{coca_is_none_str}] is None."
F" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
snake_case_ = self.get_image_description(_UpperCAmelCase )
# get prompt text embeddings for content and style
snake_case_ = self.tokenizer(
_UpperCAmelCase , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=_UpperCAmelCase , return_tensors='''pt''' , )
snake_case_ = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
snake_case_ = self.tokenizer(
_UpperCAmelCase , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=_UpperCAmelCase , return_tensors='''pt''' , )
snake_case_ = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
snake_case_ = slerp(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# duplicate text embeddings for each generation per prompt
snake_case_ = text_embeddings.repeat_interleave(_UpperCAmelCase , dim=0 )
# set timesteps
snake_case_ = 'offset' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
snake_case_ = {}
if accepts_offset:
snake_case_ = 1
self.scheduler.set_timesteps(_UpperCAmelCase , **_UpperCAmelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
snake_case_ = self.get_timesteps(_UpperCAmelCase , _UpperCAmelCase , self.device )
snake_case_ = timesteps[:1].repeat(_UpperCAmelCase )
# Preprocess image
snake_case_ = preprocess(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
snake_case_ = self.prepare_latents(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , text_embeddings.dtype , self.device , _UpperCAmelCase )
snake_case_ = preprocess(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
snake_case_ = self.prepare_latents(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , text_embeddings.dtype , self.device , _UpperCAmelCase )
snake_case_ = slerp(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if clip_guidance_scale > 0:
snake_case_ = self.get_clip_image_embeddings(_UpperCAmelCase , _UpperCAmelCase )
snake_case_ = self.get_clip_image_embeddings(_UpperCAmelCase , _UpperCAmelCase )
snake_case_ = slerp(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
snake_case_ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
snake_case_ = content_text_input.input_ids.shape[-1]
snake_case_ = self.tokenizer([''''''] , padding='''max_length''' , max_length=_UpperCAmelCase , return_tensors='''pt''' )
snake_case_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
snake_case_ = uncond_embeddings.repeat_interleave(_UpperCAmelCase , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
snake_case_ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
snake_case_ = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
snake_case_ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
snake_case_ = torch.randn(_UpperCAmelCase , generator=_UpperCAmelCase , device='''cpu''' , dtype=_UpperCAmelCase ).to(
self.device )
else:
snake_case_ = torch.randn(_UpperCAmelCase , generator=_UpperCAmelCase , device=self.device , dtype=_UpperCAmelCase )
else:
if latents.shape != latents_shape:
raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
snake_case_ = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
snake_case_ = 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]
snake_case_ = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
snake_case_ = {}
if accepts_eta:
snake_case_ = eta
# check if the scheduler accepts generator
snake_case_ = 'generator' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
snake_case_ = generator
with self.progress_bar(total=_UpperCAmelCase ):
for i, t in enumerate(_UpperCAmelCase ):
# expand the latents if we are doing classifier free guidance
snake_case_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
snake_case_ = self.scheduler.scale_model_input(_UpperCAmelCase , _UpperCAmelCase )
# predict the noise residual
snake_case_ = self.unet(_UpperCAmelCase , _UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
snake_case_ = noise_pred.chunk(2 )
snake_case_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
snake_case_ = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
snake_case_ = self.cond_fn(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
# compute the previous noisy sample x_t -> x_t-1
snake_case_ = self.scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
snake_case_ = 1 / 0.1_8215 * latents
snake_case_ = self.vae.decode(_UpperCAmelCase ).sample
snake_case_ = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ = self.numpy_to_pil(_UpperCAmelCase )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=_UpperCAmelCase , nsfw_content_detected=_UpperCAmelCase )
| 368 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> list[list]:
'''simple docstring'''
snake_case_ = current_set.copy()
for row_index, row in enumerate(__UpperCAmelCase ):
snake_case_ = row[0]
for column_index, column in enumerate(__UpperCAmelCase ):
if magnitude == 0:
snake_case_ = column
continue
snake_case_ = column / magnitude
# Subtract to cancel term
snake_case_ = current_set[0]
snake_case_ = [first_row]
snake_case_ = current_set[1::]
for row in current_set:
snake_case_ = []
# If first term is 0, it is already in form we want, so we preserve it
if row[0] == 0:
final_set.append(__UpperCAmelCase )
continue
for column_index in range(len(__UpperCAmelCase ) ):
temp_row.append(first_row[column_index] - row[column_index] )
final_set.append(__UpperCAmelCase )
# Create next recursion iteration set
if len(final_set[0] ) != 3:
snake_case_ = final_set[0]
snake_case_ = []
snake_case_ = []
for row in final_set[1::]:
current_first_column.append(row[0] )
next_iteration.append(row[1::] )
snake_case_ = simplify(__UpperCAmelCase )
for i in range(len(__UpperCAmelCase ) ):
resultant[i].insert(0, current_first_column[i] )
resultant.insert(0, __UpperCAmelCase )
snake_case_ = resultant
return final_set
def __magic_name__ ( __UpperCAmelCase ) -> list:
'''simple docstring'''
if len(__UpperCAmelCase ) == 0:
raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
snake_case_ = len(__UpperCAmelCase ) + 1
if any(len(__UpperCAmelCase ) != _length for item in equations ):
raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
for row in equations:
if any(not isinstance(__UpperCAmelCase, (int, float) ) for column in row ):
raise ValueError('''solve_simultaneous() requires lists of integers''' )
if len(__UpperCAmelCase ) == 1:
return [equations[0][-1] / equations[0][0]]
snake_case_ = equations.copy()
if any(0 in row for row in data_set ):
snake_case_ = data_set.copy()
snake_case_ = []
for row_index, row in enumerate(__UpperCAmelCase ):
if 0 not in row:
snake_case_ = data_set.pop(__UpperCAmelCase )
break
if not full_row:
raise ValueError('''solve_simultaneous() requires at least 1 full equation''' )
data_set.insert(0, __UpperCAmelCase )
snake_case_ = data_set.copy()
snake_case_ = simplify(__UpperCAmelCase )
snake_case_ = simplified[::-1]
snake_case_ = []
for row in simplified:
snake_case_ = row[-1]
if not solutions:
if row[-2] == 0:
solutions.append(0 )
continue
solutions.append(current_solution / row[-2] )
continue
snake_case_ = row.copy()[: len(__UpperCAmelCase ) - 1 :]
while temp_row[0] == 0:
temp_row.pop(0 )
if len(__UpperCAmelCase ) == 0:
solutions.append(0 )
continue
snake_case_ = temp_row[1::]
snake_case_ = temp_row[::-1]
for column_index, column in enumerate(__UpperCAmelCase ):
current_solution -= column * solutions[column_index]
solutions.append(__UpperCAmelCase )
snake_case_ = []
for item in solutions:
final.append(float(round(__UpperCAmelCase, 5 ) ) )
return final[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
a : str = [
[2, 1, 1, 1, 1, 4],
[1, 2, 1, 1, 1, 5],
[1, 1, 2, 1, 1, 6],
[1, 1, 1, 2, 1, 7],
[1, 1, 1, 1, 2, 8],
]
print(solve_simultaneous(eq))
print(solve_simultaneous([[4, 2]]))
| 72 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import requests
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
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
lowerCAmelCase_ : Any = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class __SCREAMING_SNAKE_CASE (unittest.TestCase ):
"""simple docstring"""
def __init__( self : Optional[int] , __a : List[str] , __a : Tuple=7 , __a : Optional[int]=3 , __a : Optional[Any]=18 , __a : Any=30 , __a : Dict=4_00 , __a : List[Any]=None , __a : List[str]=True , __a : Tuple=True , __a : int=None , ):
_a = size if size is not None else {"height": 20, "width": 20}
_a = parent
_a = batch_size
_a = num_channels
_a = image_size
_a = min_resolution
_a = max_resolution
_a = size
_a = do_normalize
_a = do_convert_rgb
_a = [5_12, 10_24, 20_48, 40_96]
_a = patch_size if patch_size is not None else {"height": 16, "width": 16}
def UpperCamelCase__ ( self : Optional[Any] ):
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def UpperCamelCase__ ( self : Tuple ):
_a = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
_a = Image.open(requests.get(__A , stream=__A ).raw ).convert("RGB" )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE (__lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
__a =PixaStructImageProcessor if is_vision_available() else None
def UpperCamelCase__ ( self : List[Any] ):
_a = PixaStructImageProcessingTester(self )
@property
def UpperCamelCase__ ( self : str ):
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase__ ( self : List[str] ):
_a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__A , "do_normalize" ) )
self.assertTrue(hasattr(__A , "do_convert_rgb" ) )
def UpperCamelCase__ ( self : Tuple ):
_a = self.image_processor_tester.prepare_dummy_image()
_a = self.image_processing_class(**self.image_processor_dict )
_a = 20_48
_a = image_processor(__A , return_tensors="pt" , max_patches=__A )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1e-3 , rtol=1e-3 ) )
def UpperCamelCase__ ( self : Union[str, Any] ):
# Initialize image_processor
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__A )
for image in image_inputs:
self.assertIsInstance(__A , Image.Image )
# Test not batched input
_a = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_a = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_a = image_processor(
__A , return_tensors="pt" , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def UpperCamelCase__ ( self : int ):
# Initialize image_processor
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__A )
for image in image_inputs:
self.assertIsInstance(__A , Image.Image )
# Test not batched input
_a = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
_a = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(__A ):
_a = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=__A ).flattened_patches
_a = "Hello"
_a = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=__A , header_text=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_a = image_processor(
__A , return_tensors="pt" , max_patches=__A , header_text=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def UpperCamelCase__ ( self : Tuple ):
# Initialize image_processor
_a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__A , numpify=__A )
for image in image_inputs:
self.assertIsInstance(__A , np.ndarray )
_a = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_a = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_a = image_processor(
__A , return_tensors="pt" , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def UpperCamelCase__ ( self : Optional[int] ):
# Initialize image_processor
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__A , torchify=__A )
for image in image_inputs:
self.assertIsInstance(__A , torch.Tensor )
# Test not batched input
_a = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_a = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_a = image_processor(
__A , return_tensors="pt" , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE (__lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
__a =PixaStructImageProcessor if is_vision_available() else None
def UpperCamelCase__ ( self : Dict ):
_a = PixaStructImageProcessingTester(self , num_channels=4 )
_a = 3
@property
def UpperCamelCase__ ( self : Optional[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase__ ( self : Dict ):
_a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__A , "do_normalize" ) )
self.assertTrue(hasattr(__A , "do_convert_rgb" ) )
def UpperCamelCase__ ( self : Union[str, Any] ):
# Initialize image_processor
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__A )
for image in image_inputs:
self.assertIsInstance(__A , Image.Image )
# Test not batched input
_a = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_a = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_a = image_processor(
__A , return_tensors="pt" , max_patches=__A ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 63 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : List[Any] =logging.get_logger(__name__)
a__ : List[Any] ={
'''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json''',
# See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple ="altclip_text_model"
def __init__( self : str , __A : List[Any]=2_5_0_0_0_2 , __A : Any=1_0_2_4 , __A : int=2_4 , __A : Dict=1_6 , __A : Optional[Any]=4_0_9_6 , __A : Union[str, Any]="gelu" , __A : Dict=0.1 , __A : Dict=0.1 , __A : List[str]=5_1_4 , __A : Optional[int]=1 , __A : int=0.02 , __A : Optional[Any]=0.02 , __A : Optional[Any]=1e-05 , __A : Dict=1 , __A : List[Any]=0 , __A : int=2 , __A : Tuple="absolute" , __A : Optional[Any]=True , __A : Optional[int]=7_6_8 , **__A : List[str] , ):
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
__UpperCamelCase = vocab_size
__UpperCamelCase = hidden_size
__UpperCamelCase = num_hidden_layers
__UpperCamelCase = num_attention_heads
__UpperCamelCase = hidden_act
__UpperCamelCase = intermediate_size
__UpperCamelCase = hidden_dropout_prob
__UpperCamelCase = attention_probs_dropout_prob
__UpperCamelCase = max_position_embeddings
__UpperCamelCase = type_vocab_size
__UpperCamelCase = initializer_range
__UpperCamelCase = initializer_factor
__UpperCamelCase = layer_norm_eps
__UpperCamelCase = position_embedding_type
__UpperCamelCase = use_cache
__UpperCamelCase = project_dim
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple ="altclip_vision_model"
def __init__( self : List[Any] , __A : Union[str, Any]=7_6_8 , __A : Optional[int]=3_0_7_2 , __A : Optional[Any]=5_1_2 , __A : Tuple=1_2 , __A : Union[str, Any]=1_2 , __A : Optional[int]=3 , __A : Dict=2_2_4 , __A : Tuple=3_2 , __A : str="quick_gelu" , __A : Dict=1e-5 , __A : Optional[int]=0.0 , __A : List[Any]=0.02 , __A : int=1.0 , **__A : Optional[int] , ):
super().__init__(**__A )
__UpperCamelCase = hidden_size
__UpperCamelCase = intermediate_size
__UpperCamelCase = projection_dim
__UpperCamelCase = num_hidden_layers
__UpperCamelCase = num_attention_heads
__UpperCamelCase = num_channels
__UpperCamelCase = patch_size
__UpperCamelCase = image_size
__UpperCamelCase = initializer_range
__UpperCamelCase = initializer_factor
__UpperCamelCase = attention_dropout
__UpperCamelCase = layer_norm_eps
__UpperCamelCase = hidden_act
@classmethod
def _lowerCamelCase ( cls : Optional[Any] , __A : Union[str, os.PathLike] , **__A : Optional[Any] ):
cls._set_token_in_kwargs(__A )
__UpperCamelCase , __UpperCamelCase = cls.get_config_dict(__A , **__A )
# get the vision config dict if we are loading from AltCLIPConfig
if config_dict.get('model_type' ) == "altclip":
__UpperCamelCase = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__A , **__A )
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] ="altclip"
SCREAMING_SNAKE_CASE_ : Optional[int] =True
def __init__( self : Any , __A : List[str]=None , __A : List[Any]=None , __A : List[str]=7_6_8 , __A : List[str]=2.6592 , **__A : Dict ):
# If `_config_dict` exist, we use them for the backward compatibility.
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
# of confusion!).
__UpperCamelCase = kwargs.pop('text_config_dict' , __A )
__UpperCamelCase = kwargs.pop('vision_config_dict' , __A )
super().__init__(**__A )
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
__UpperCamelCase = {}
# This is the complete result when using `text_config_dict`.
__UpperCamelCase = AltCLIPTextConfig(**__A ).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
__UpperCamelCase = (
f'''`{key}` is found in both `text_config_dict` and `text_config` but with different values. '''
f'''The value `text_config_dict["{key}"]` will be used instead.'''
)
# If inferred from default argument values (just to be super careful)
else:
__UpperCamelCase = (
f'''`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '''
f'''value `text_config["{key}"]` will be overriden.'''
)
logger.warning(__A )
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict )
if vision_config_dict is not None:
if vision_config is None:
__UpperCamelCase = {}
# This is the complete result when using `vision_config_dict`.
__UpperCamelCase = AltCLIPVisionConfig(**__A ).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
__UpperCamelCase = {
str(__A ): value for key, value in _vision_config_dict['id2label'].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
__UpperCamelCase = (
f'''`{key}` is found in both `vision_config_dict` and `vision_config` but with different '''
f'''values. The value `vision_config_dict["{key}"]` will be used instead.'''
)
# If inferred from default argument values (just to be super careful)
else:
__UpperCamelCase = (
f'''`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '''
f'''The value `vision_config["{key}"]` will be overriden.'''
)
logger.warning(__A )
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict )
if text_config is None:
__UpperCamelCase = {}
logger.info('`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.' )
if vision_config is None:
__UpperCamelCase = {}
logger.info('`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.' )
__UpperCamelCase = AltCLIPTextConfig(**__A )
__UpperCamelCase = AltCLIPVisionConfig(**__A )
__UpperCamelCase = projection_dim
__UpperCamelCase = logit_scale_init_value
__UpperCamelCase = 1.0
@classmethod
def _lowerCamelCase ( cls : Union[str, Any] , __A : AltCLIPTextConfig , __A : AltCLIPVisionConfig , **__A : Optional[Any] ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__A )
def _lowerCamelCase ( self : List[Any] ):
__UpperCamelCase = copy.deepcopy(self.__dict__ )
__UpperCamelCase = self.text_config.to_dict()
__UpperCamelCase = self.vision_config.to_dict()
__UpperCamelCase = self.__class__.model_type
return output
| 53 | 0 |
def _a ( lowerCamelCase: str , lowerCamelCase: str ) -> Optional[int]:
'''simple docstring'''
assert x is not None
assert y is not None
__A = len(lowerCamelCase )
__A = len(lowerCamelCase )
# declaring the array for storing the dp values
__A = [[0] * (n + 1) for _ in range(m + 1 )] # noqa: E741
for i in range(1 , m + 1 ):
for j in range(1 , n + 1 ):
__A = 1 if x[i - 1] == y[j - 1] else 0
__A = max(l[i - 1][j] , l[i][j - 1] , l[i - 1][j - 1] + match )
__A = ''''''
__A , __A = m, n
while i > 0 and j > 0:
__A = 1 if x[i - 1] == y[j - 1] else 0
if l[i][j] == l[i - 1][j - 1] + match:
if match == 1:
__A = x[i - 1] + seq
i -= 1
j -= 1
elif l[i][j] == l[i - 1][j]:
i -= 1
else:
j -= 1
return l[m][n], seq
if __name__ == "__main__":
snake_case__ : List[Any] = 'AGGTAB'
snake_case__ : Dict = 'GXTXAYB'
snake_case__ : Tuple = 4
snake_case__ : Optional[int] = 'GTAB'
snake_case__ : List[str] = longest_common_subsequence(a, b)
print('len =', ln, ', sub-sequence =', subseq)
import doctest
doctest.testmod()
| 358 |
import math
def _a ( lowerCamelCase: int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _a ( lowerCamelCase: float = 0.1 ) -> int:
'''simple docstring'''
__A = 3
__A = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(lowerCamelCase )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 250 | 0 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
_lowerCAmelCase = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
from run_translation import main # noqa
set_seed(42)
_lowerCAmelCase = '''sshleifer/student_marian_en_ro_6_1'''
_lowerCAmelCase = '''sshleifer/tiny-mbart'''
@require_torch
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def UpperCAmelCase_ ( self ,__UpperCAmelCase=False ,__UpperCAmelCase=None ,__UpperCAmelCase=True ,__UpperCAmelCase=True ,__UpperCAmelCase=True ,__UpperCAmelCase=True ,) -> Tuple:
lowerCAmelCase__ : Tuple = self.run_trainer(
eval_steps=1 ,max_len=12 ,model_name=__UpperCAmelCase ,num_train_epochs=1 ,distributed=__UpperCAmelCase ,extra_args_str=__UpperCAmelCase ,predict_with_generate=__UpperCAmelCase ,do_train=__UpperCAmelCase ,do_eval=__UpperCAmelCase ,do_predict=__UpperCAmelCase ,)
lowerCAmelCase__ : List[Any] = TrainerState.load_from_json(os.path.join(__UpperCAmelCase ,"""trainer_state.json""" ) ).log_history
if not do_eval:
return
lowerCAmelCase__ : List[Any] = [log for log in logs if """eval_loss""" in log.keys()]
lowerCAmelCase__ : Optional[Any] = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
lowerCAmelCase__ : Optional[int] = eval_metrics[-1]
assert isinstance(last_step_stats["""eval_bleu"""] ,__UpperCAmelCase )
assert not math.isnan(float(last_step_stats["""eval_loss"""] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def UpperCAmelCase_ ( self ) -> Optional[int]:
self.run_seqaseq_quick()
@require_torch_multi_gpu
def UpperCAmelCase_ ( self ) -> List[Any]:
self.run_seqaseq_quick(distributed=__UpperCAmelCase )
@require_torch_multi_gpu
def UpperCAmelCase_ ( self ) -> Tuple:
self.run_seqaseq_quick(distributed=__UpperCAmelCase )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCAmelCase_ ( self ) -> Any:
self.run_seqaseq_quick(distributed=__UpperCAmelCase ,extra_args_str="""--sharded_ddp simple""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCAmelCase_ ( self ) -> Dict:
self.run_seqaseq_quick(distributed=__UpperCAmelCase ,extra_args_str="""--sharded_ddp simple --fp16""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCAmelCase_ ( self ) -> Dict:
self.run_seqaseq_quick(distributed=__UpperCAmelCase ,extra_args_str="""--sharded_ddp zero_dp_2""" ,predict_with_generate=__UpperCAmelCase )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCAmelCase_ ( self ) -> List[Any]:
self.run_seqaseq_quick(
distributed=__UpperCAmelCase ,extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" ,predict_with_generate=__UpperCAmelCase )
@require_apex
@require_torch_gpu
def UpperCAmelCase_ ( self ) -> Tuple:
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=__UpperCAmelCase ,extra_args_str="""--fp16 --fp16_backend=apex""" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=__UpperCAmelCase ,extra_args_str="""--fp16 --fp16_backend=apex""" )
@parameterized.expand(["""base""", """low""", """high""", """mixed"""] )
@require_torch_multi_gpu
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> Any:
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
lowerCAmelCase__ : Optional[Any] = {
# test with the default log_level - should be info and thus log info once
"""base""": {"""extra_args_str""": """""", """n_matches""": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"""low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"""high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"""mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
}
lowerCAmelCase__ : Any = experiments[experiment_id]
lowerCAmelCase__ : Optional[Any] = {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
lowerCAmelCase__ : Optional[int] = """Running training"""
with CaptureStderr() as cl:
self.run_seqaseq_quick(**__UpperCAmelCase ,extra_args_str=data["""extra_args_str"""] )
lowerCAmelCase__ : Union[str, Any] = len(re.findall(__UpperCAmelCase ,cl.err ) )
self.assertEqual(__UpperCAmelCase ,data["""n_matches"""] )
@slow
def UpperCAmelCase_ ( self ) -> Optional[Any]:
lowerCAmelCase__ : Tuple = self.run_trainer(
eval_steps=2 ,max_len=128 ,model_name=__UpperCAmelCase ,learning_rate=3E-4 ,num_train_epochs=10 ,distributed=__UpperCAmelCase ,)
# Check metrics
lowerCAmelCase__ : str = TrainerState.load_from_json(os.path.join(__UpperCAmelCase ,"""trainer_state.json""" ) ).log_history
lowerCAmelCase__ : Union[str, Any] = [log for log in logs if """eval_loss""" in log.keys()]
lowerCAmelCase__ : Optional[Any] = eval_metrics[0]
lowerCAmelCase__ : Tuple = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["""eval_bleu"""] ,__UpperCAmelCase )
# test if do_predict saves generations and metrics
lowerCAmelCase__ : Optional[Any] = os.listdir(__UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = {os.path.basename(__UpperCAmelCase ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
from transformers.training_args import OptimizerNames
def train_and_return_metrics(__UpperCAmelCase ) -> Tuple[int, float]:
lowerCAmelCase__ : List[Any] = """--skip_memory_metrics 0"""
lowerCAmelCase__ : Dict = self.run_trainer(
max_len=128 ,model_name=__UpperCAmelCase ,learning_rate=3E-4 ,num_train_epochs=1 ,optim=__UpperCAmelCase ,distributed=__UpperCAmelCase ,extra_args_str=__UpperCAmelCase ,do_eval=__UpperCAmelCase ,do_predict=__UpperCAmelCase ,n_gpus_to_use=1 ,)
# Check metrics
lowerCAmelCase__ : List[str] = TrainerState.load_from_json(Path(__UpperCAmelCase ,"""trainer_state.json""" ) ).log_history
lowerCAmelCase__ : Tuple = int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20 )
lowerCAmelCase__ : int = int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20 )
lowerCAmelCase__ : Any = logs[0]["""train_loss"""]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : str = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
lowerCAmelCase__ : Optional[Any] = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
lowerCAmelCase__ : List[Any] = gpu_peak_mem_orig + gpu_alloc_mem_orig
lowerCAmelCase__ : Optional[int] = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
lowerCAmelCase__ : Any = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
lowerCAmelCase__ : Any = 120
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
__UpperCAmelCase ,__UpperCAmelCase ,"""should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
F""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
F""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB""" ,)
self.assertGreater(
__UpperCAmelCase ,__UpperCAmelCase ,"""should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
F""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
F""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB""" ,)
self.assertEqual(
__UpperCAmelCase ,__UpperCAmelCase ,F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}""" )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase = 3E-3 ,__UpperCAmelCase = "adafactor" ,__UpperCAmelCase = False ,__UpperCAmelCase = None ,__UpperCAmelCase = 0 ,__UpperCAmelCase = True ,__UpperCAmelCase = True ,__UpperCAmelCase = True ,__UpperCAmelCase = True ,__UpperCAmelCase = None ,) -> List[str]:
lowerCAmelCase__ : List[str] = self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
lowerCAmelCase__ : List[str] = self.get_auto_remove_tmp_dir()
lowerCAmelCase__ : Optional[int] = F"""
--model_name_or_path {model_name}
--train_file {data_dir}/train.json
--validation_file {data_dir}/val.json
--test_file {data_dir}/test.json
--output_dir {output_dir}
--overwrite_output_dir
--max_train_samples 8
--max_source_length {max_len}
--max_target_length {max_len}
--do_train
--num_train_epochs {str(__UpperCAmelCase )}
--per_device_train_batch_size 4
--learning_rate {learning_rate}
--warmup_steps 8
--logging_steps 0
--logging_strategy no
--save_steps {str(__UpperCAmelCase )}
--group_by_length
--label_smoothing_factor 0.1
--target_lang ro_RO
--source_lang en_XX
""".split()
lowerCAmelCase__ : Union[str, Any] = F"""
--do_eval
--per_device_eval_batch_size 4
--max_eval_samples 8
--val_max_target_length {max_len}
--evaluation_strategy steps
--eval_steps {str(__UpperCAmelCase )}
""".split()
lowerCAmelCase__ : Optional[int] = """
--do_predict
""".split()
lowerCAmelCase__ : Dict = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F"""--optim {optim}""".split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
lowerCAmelCase__ : Dict = get_gpu_count()
lowerCAmelCase__ : Optional[Any] = get_torch_dist_unique_port()
lowerCAmelCase__ : Optional[Any] = F"""
-m torch.distributed.run
--nproc_per_node={n_gpus_to_use}
--master_port={master_port}
{self.examples_dir_str}/pytorch/translation/run_translation.py
""".split()
lowerCAmelCase__ : List[str] = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(__UpperCAmelCase ,env=self.get_env() )
else:
lowerCAmelCase__ : List[Any] = ["""run_translation.py"""] + args
with patch.object(__UpperCAmelCase ,"""argv""" ,__UpperCAmelCase ):
main()
return output_dir
| 37 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A_ : Optional[Any] = logging.get_logger(__name__)
A_ : Optional[Any] = {
"facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class lowerCamelCase (A__ ):
lowerCamelCase__ : Dict = 'detr'
lowerCamelCase__ : Union[str, Any] = ['past_key_values']
lowerCamelCase__ : Tuple = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Optional[int] , __UpperCAmelCase : Any=True , __UpperCAmelCase : Union[str, Any]=None , __UpperCAmelCase : Optional[int]=3 , __UpperCAmelCase : int=1_0_0 , __UpperCAmelCase : Optional[Any]=6 , __UpperCAmelCase : List[str]=2_0_4_8 , __UpperCAmelCase : List[str]=8 , __UpperCAmelCase : Optional[int]=6 , __UpperCAmelCase : Dict=2_0_4_8 , __UpperCAmelCase : List[str]=8 , __UpperCAmelCase : Union[str, Any]=0.0 , __UpperCAmelCase : Dict=0.0 , __UpperCAmelCase : Optional[Any]=True , __UpperCAmelCase : Any="relu" , __UpperCAmelCase : Dict=2_5_6 , __UpperCAmelCase : List[str]=0.1 , __UpperCAmelCase : int=0.0 , __UpperCAmelCase : str=0.0 , __UpperCAmelCase : Optional[int]=0.02 , __UpperCAmelCase : Optional[int]=1.0 , __UpperCAmelCase : Dict=False , __UpperCAmelCase : str="sine" , __UpperCAmelCase : str="resnet50" , __UpperCAmelCase : List[Any]=True , __UpperCAmelCase : int=False , __UpperCAmelCase : Tuple=1 , __UpperCAmelCase : Optional[Any]=5 , __UpperCAmelCase : Tuple=2 , __UpperCAmelCase : Optional[Any]=1 , __UpperCAmelCase : Union[str, Any]=1 , __UpperCAmelCase : Union[str, Any]=5 , __UpperCAmelCase : Any=2 , __UpperCAmelCase : List[str]=0.1 , **__UpperCAmelCase : Dict , ) -> Optional[int]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
SCREAMING_SNAKE_CASE__ = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = backbone_config.get("""model_type""" )
SCREAMING_SNAKE_CASE__ = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE__ = config_class.from_dict(__UpperCAmelCase )
# set timm attributes to None
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None, None, None
SCREAMING_SNAKE_CASE__ = use_timm_backbone
SCREAMING_SNAKE_CASE__ = backbone_config
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = num_queries
SCREAMING_SNAKE_CASE__ = d_model
SCREAMING_SNAKE_CASE__ = encoder_ffn_dim
SCREAMING_SNAKE_CASE__ = encoder_layers
SCREAMING_SNAKE_CASE__ = encoder_attention_heads
SCREAMING_SNAKE_CASE__ = decoder_ffn_dim
SCREAMING_SNAKE_CASE__ = decoder_layers
SCREAMING_SNAKE_CASE__ = decoder_attention_heads
SCREAMING_SNAKE_CASE__ = dropout
SCREAMING_SNAKE_CASE__ = attention_dropout
SCREAMING_SNAKE_CASE__ = activation_dropout
SCREAMING_SNAKE_CASE__ = activation_function
SCREAMING_SNAKE_CASE__ = init_std
SCREAMING_SNAKE_CASE__ = init_xavier_std
SCREAMING_SNAKE_CASE__ = encoder_layerdrop
SCREAMING_SNAKE_CASE__ = decoder_layerdrop
SCREAMING_SNAKE_CASE__ = encoder_layers
SCREAMING_SNAKE_CASE__ = auxiliary_loss
SCREAMING_SNAKE_CASE__ = position_embedding_type
SCREAMING_SNAKE_CASE__ = backbone
SCREAMING_SNAKE_CASE__ = use_pretrained_backbone
SCREAMING_SNAKE_CASE__ = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE__ = class_cost
SCREAMING_SNAKE_CASE__ = bbox_cost
SCREAMING_SNAKE_CASE__ = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE__ = mask_loss_coefficient
SCREAMING_SNAKE_CASE__ = dice_loss_coefficient
SCREAMING_SNAKE_CASE__ = bbox_loss_coefficient
SCREAMING_SNAKE_CASE__ = giou_loss_coefficient
SCREAMING_SNAKE_CASE__ = eos_coefficient
super().__init__(is_encoder_decoder=__UpperCAmelCase , **__UpperCAmelCase )
@property
def SCREAMING_SNAKE_CASE ( self : str ) -> int:
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
return self.d_model
@classmethod
def SCREAMING_SNAKE_CASE ( cls : str , __UpperCAmelCase : PretrainedConfig , **__UpperCAmelCase : Dict ) -> List[Any]:
return cls(backbone_config=__UpperCAmelCase , **__UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Dict[str, any]:
SCREAMING_SNAKE_CASE__ = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
SCREAMING_SNAKE_CASE__ = self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE__ = self.__class__.model_type
return output
class lowerCamelCase (A__ ):
lowerCamelCase__ : Union[str, Any] = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE ( self : str ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def SCREAMING_SNAKE_CASE ( self : str ) -> float:
return 1e-5
@property
def SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
return 1_2
| 165 | 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
lowerCAmelCase__ = pytest.mark.integration
@require_faiss
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase = Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(lowercase__ ) for x in np.arange(3_0 ).tolist()]} )
return dset
def SCREAMING_SNAKE_CASE ( self : List[str] ):
import faiss
__lowercase = self._create_dummy_dataset()
__lowercase = dset.map(
lambda lowercase__ ,lowercase__ : {"vecs": i * np.ones(5 ,dtype=np.floataa )} ,with_indices=lowercase__ ,keep_in_memory=lowercase__ )
__lowercase = dset.add_faiss_index('''vecs''' ,batch_size=1_0_0 ,metric_type=faiss.METRIC_INNER_PRODUCT )
__lowercase , __lowercase = dset.get_nearest_examples('''vecs''' ,np.ones(5 ,dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] ,'''my_name-train_29''' )
dset.drop_index('''vecs''' )
def SCREAMING_SNAKE_CASE ( self : Dict ):
import faiss
__lowercase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((3_0, 5) ) * np.arange(3_0 ).reshape(-1 ,1 ) ,index_name='''vecs''' ,batch_size=1_0_0 ,metric_type=faiss.METRIC_INNER_PRODUCT ,)
__lowercase , __lowercase = dset.get_nearest_examples('''vecs''' ,np.ones(5 ,dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] ,'''my_name-train_29''' )
def SCREAMING_SNAKE_CASE ( self : int ):
import faiss
__lowercase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((3_0, 5) ) * np.arange(3_0 ).reshape(-1 ,1 ) ,index_name='''vecs''' ,metric_type=faiss.METRIC_INNER_PRODUCT ,)
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=lowercase__ ) as tmp_file:
dset.save_faiss_index('''vecs''' ,tmp_file.name )
dset.load_faiss_index('''vecs2''' ,tmp_file.name )
os.unlink(tmp_file.name )
__lowercase , __lowercase = dset.get_nearest_examples('''vecs2''' ,np.ones(5 ,dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] ,'''my_name-train_29''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((3_0, 5) ) * np.arange(3_0 ).reshape(-1 ,1 ) ,index_name='''vecs''' )
dset.drop_index('''vecs''' )
self.assertRaises(lowercase__ ,partial(dset.get_nearest_examples ,'''vecs2''' ,np.ones(5 ,dtype=np.floataa ) ) )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
from elasticsearch import Elasticsearch
__lowercase = self._create_dummy_dataset()
with patch('''elasticsearch.Elasticsearch.search''' ) as mocked_search, patch(
'''elasticsearch.client.IndicesClient.create''' ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''' ) as mocked_bulk:
__lowercase = {'''acknowledged''': True}
mocked_bulk.return_value([(True, None)] * 3_0 )
__lowercase = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 2_9}]}}
__lowercase = Elasticsearch()
dset.add_elasticsearch_index('''filename''' ,es_client=lowercase__ )
__lowercase , __lowercase = dset.get_nearest_examples('''filename''' ,'''my_name-train_29''' )
self.assertEqual(examples['''filename'''][0] ,'''my_name-train_29''' )
@require_faiss
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Tuple ):
import faiss
__lowercase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal ,5 )
index.add_vectors(np.zeros((5, 5) ,dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal ,1_0 )
# single query
__lowercase = np.zeros(5 ,dtype=np.floataa )
__lowercase = 1
__lowercase , __lowercase = index.search(lowercase__ )
self.assertRaises(lowercase__ ,index.search ,query.reshape(-1 ,1 ) )
self.assertGreater(scores[0] ,0 )
self.assertEqual(indices[0] ,1 )
# batched queries
__lowercase = np.eye(5 ,dtype=np.floataa )[::-1]
__lowercase , __lowercase = index.search_batch(lowercase__ )
self.assertRaises(lowercase__ ,index.search_batch ,queries[0] )
__lowercase = [scores[0] for scores in total_scores]
__lowercase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(lowercase__ ) ,0 )
self.assertListEqual([4, 3, 2, 1, 0] ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
import faiss
__lowercase = FaissIndex(string_factory='''Flat''' )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index ,faiss.IndexFlat )
__lowercase = FaissIndex(string_factory='''LSH''' )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index ,faiss.IndexLSH )
with self.assertRaises(lowercase__ ):
__lowercase = FaissIndex(string_factory='''Flat''' ,custom_index=faiss.IndexFlat(5 ) )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
import faiss
__lowercase = faiss.IndexFlat(5 )
__lowercase = FaissIndex(custom_index=lowercase__ )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index ,faiss.IndexFlat )
def SCREAMING_SNAKE_CASE ( self : Any ):
import faiss
__lowercase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=lowercase__ ) as tmp_file:
index.save(tmp_file.name )
__lowercase = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
__lowercase = np.zeros(5 ,dtype=np.floataa )
__lowercase = 1
__lowercase , __lowercase = index.search(lowercase__ )
self.assertGreater(scores[0] ,0 )
self.assertEqual(indices[0] ,1 )
@require_faiss
def _A ( A__ ):
"""simple docstring"""
import faiss
__lowercase = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
__lowercase = '''index.faiss'''
__lowercase = F"mock://{index_name}"
index.save(A__ , storage_options=mockfs.storage_options )
__lowercase = FaissIndex.load(A__ , storage_options=mockfs.storage_options )
__lowercase = np.zeros(5 , dtype=np.floataa )
__lowercase = 1
__lowercase , __lowercase = index.search(A__ )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
from elasticsearch import Elasticsearch
with patch('''elasticsearch.Elasticsearch.search''' ) as mocked_search, patch(
'''elasticsearch.client.IndicesClient.create''' ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''' ) as mocked_bulk:
__lowercase = Elasticsearch()
__lowercase = {'''acknowledged''': True}
__lowercase = ElasticSearchIndex(es_client=lowercase__ )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['''foo''', '''bar''', '''foobar'''] )
# single query
__lowercase = '''foo'''
__lowercase = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
__lowercase , __lowercase = index.search(lowercase__ )
self.assertEqual(scores[0] ,1 )
self.assertEqual(indices[0] ,0 )
# single query with timeout
__lowercase = '''foo'''
__lowercase = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
__lowercase , __lowercase = index.search(lowercase__ ,request_timeout=3_0 )
self.assertEqual(scores[0] ,1 )
self.assertEqual(indices[0] ,0 )
# batched queries
__lowercase = ['''foo''', '''bar''', '''foobar''']
__lowercase = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
__lowercase , __lowercase = index.search_batch(lowercase__ )
__lowercase = [scores[0] for scores in total_scores]
__lowercase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(lowercase__ ) ,0 )
self.assertListEqual([1, 1, 1] ,lowercase__ )
# batched queries with timeout
__lowercase = ['''foo''', '''bar''', '''foobar''']
__lowercase = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
__lowercase , __lowercase = index.search_batch(lowercase__ ,request_timeout=3_0 )
__lowercase = [scores[0] for scores in total_scores]
__lowercase = [indices[0] for indices in total_indices]
self.assertGreater(np.min(lowercase__ ) ,0 )
self.assertListEqual([1, 1, 1] ,lowercase__ )
| 350 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TimesformerModel''',
'''TimesformerForVideoClassification''',
'''TimesformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 52 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowercase : Optional[int] = logging.get_logger(__name__)
_lowercase : Optional[int] = {
"nielsr/canine-s": 2048,
}
# Unicode defines 1,114,112 total “codepoints”
_lowercase : List[str] = 1114112
# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_lowercase : Optional[int] = 0
_lowercase : int = 0xE_0_0_0
_lowercase : List[Any] = 0xE_0_0_1
_lowercase : Dict = 0xE_0_0_2
_lowercase : Optional[Any] = 0xE_0_0_3
_lowercase : Optional[int] = 0xE_0_0_4
# Maps special codepoints to human-readable names.
_lowercase : Dict[int, str] = {
# Special symbols are represented using codepoints values that are valid,
# but designated as "Private Use", meaning that they will never be assigned
# characters by the Unicode Consortium, and are thus safe for use here.
#
# NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
# excluded and should fail with a hard error.
CLS: "[CLS]",
SEP: "[SEP]",
BOS: "[BOS]",
MASK: "[MASK]",
PAD: "[PAD]",
RESERVED: "[RESERVED]",
}
# Maps special codepoint human-readable names to their codepoint values.
_lowercase : Dict[str, int] = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : str , lowercase_ : Optional[Any]=chr(lowercase_ ) , lowercase_ : Tuple=chr(lowercase_ ) , lowercase_ : Dict=chr(lowercase_ ) , lowercase_ : Dict=chr(lowercase_ ) , lowercase_ : List[str]=chr(lowercase_ ) , lowercase_ : Union[str, Any]=chr(lowercase_ ) , lowercase_ : Union[str, Any]=False , lowercase_ : List[str]=2048 , **lowercase_ : str , ):
lowercase_ : Any = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else bos_token
lowercase_ : Dict = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else eos_token
lowercase_ : int = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else sep_token
lowercase_ : List[str] = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else cls_token
lowercase_ : List[Any] = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowercase_ : int = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else mask_token
super().__init__(
bos_token=lowercase_ , eos_token=lowercase_ , sep_token=lowercase_ , cls_token=lowercase_ , pad_token=lowercase_ , mask_token=lowercase_ , add_prefix_space=lowercase_ , model_max_length=lowercase_ , **lowercase_ , )
# Creates a mapping for looking up the IDs of special symbols.
lowercase_ : Dict[str, int] = {}
for codepoint, name in SPECIAL_CODEPOINTS.items():
lowercase_ : List[str] = codepoint
# Creates a mapping for looking up the string forms of special symbol IDs.
lowercase_ : Dict[int, str] = {
codepoint: name for name, codepoint in self._special_codepoints.items()
}
lowercase_ : str = UNICODE_VOCAB_SIZE
lowercase_ : str = len(self._special_codepoints )
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
return self._unicode_vocab_size
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : str ):
return list(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : str ):
try:
return ord(lowercase_ )
except TypeError:
raise ValueError(f'''invalid token: \'{token}\'''' )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : int ):
try:
if index in SPECIAL_CODEPOINTS:
return SPECIAL_CODEPOINTS[index]
return chr(lowercase_ )
except TypeError:
raise ValueError(f'''invalid id: {index}''' )
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : str ):
return "".join(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
lowercase_ : Optional[Any] = [self.sep_token_id]
lowercase_ : Any = [self.cls_token_id]
lowercase_ : List[Any] = cls + token_ids_a + sep
if token_ids_a is not None:
result += token_ids_a + sep
return result
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None , lowercase_ : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase_ , token_ids_a=lowercase_ , already_has_special_tokens=lowercase_ )
lowercase_ : Optional[int] = [1] + ([0] * len(lowercase_ )) + [1]
if token_ids_a is not None:
result += ([0] * len(lowercase_ )) + [1]
return result
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
lowercase_ : List[Any] = [self.sep_token_id]
lowercase_ : List[str] = [self.cls_token_id]
lowercase_ : int = len(cls + token_ids_a + sep ) * [0]
if token_ids_a is not None:
result += len(token_ids_a + sep ) * [1]
return result
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : str , lowercase_ : Optional[str] = None ):
return ()
| 239 | '''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Dict = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Dict = ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
_lowercase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 239 | 1 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
lowerCamelCase_ : Optional[List[str]] = None
lowerCamelCase_ : Dict = """<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowerCamelCase_ : Tuple = [
np.dtype("""|b1"""),
np.dtype("""|u1"""),
np.dtype("""<u2"""),
np.dtype(""">u2"""),
np.dtype("""<i2"""),
np.dtype(""">i2"""),
np.dtype("""<u4"""),
np.dtype(""">u4"""),
np.dtype("""<i4"""),
np.dtype(""">i4"""),
np.dtype("""<f4"""),
np.dtype(""">f4"""),
np.dtype("""<f8"""),
np.dtype(""">f8"""),
]
@dataclass
class _UpperCamelCase :
'''simple docstring'''
__UpperCamelCase : bool = True
__UpperCamelCase : Optional[str] = None
# Automatically constructed
__UpperCamelCase : ClassVar[str] = "PIL.Image.Image"
__UpperCamelCase : ClassVar[Any] = pa.struct({"""bytes""": pa.binary(), """path""": pa.string()} )
__UpperCamelCase : str = field(default="""Image""" , init=_A , repr=_A )
def __call__( self : Dict ):
return self.pa_type
def lowerCAmelCase__ ( self : Any , snake_case_ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(snake_case_ , snake_case_ ):
UpperCamelCase_: List[Any] = np.array(snake_case_ )
if isinstance(snake_case_ , snake_case_ ):
return {"path": value, "bytes": None}
elif isinstance(snake_case_ , snake_case_ ):
return {"path": None, "bytes": value}
elif isinstance(snake_case_ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(snake_case_ )
elif isinstance(snake_case_ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(snake_case_ )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def lowerCAmelCase__ ( self : Optional[Any] , snake_case_ : dict , snake_case_ : Optional[int]=None ):
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
UpperCamelCase_: Union[str, Any] = {}
UpperCamelCase_, UpperCamelCase_: List[str] = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(snake_case_ ):
UpperCamelCase_: str = PIL.Image.open(snake_case_ )
else:
UpperCamelCase_: str = path.split("""::""" )[-1]
try:
UpperCamelCase_: Tuple = string_to_dict(snake_case_ , config.HUB_DATASETS_URL )["""repo_id"""]
UpperCamelCase_: Tuple = token_per_repo_id.get(snake_case_ )
except ValueError:
UpperCamelCase_: Tuple = None
with xopen(snake_case_ , """rb""" , use_auth_token=snake_case_ ) as f:
UpperCamelCase_: Tuple = BytesIO(f.read() )
UpperCamelCase_: List[str] = PIL.Image.open(bytes_ )
else:
UpperCamelCase_: Optional[int] = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def lowerCAmelCase__ ( self : List[str] ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def lowerCAmelCase__ ( self : Any , snake_case_ : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
UpperCamelCase_: Union[str, Any] = pa.array([None] * len(snake_case_ ) , type=pa.binary() )
UpperCamelCase_: Any = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
UpperCamelCase_: List[Any] = pa.array([None] * len(snake_case_ ) , type=pa.string() )
UpperCamelCase_: List[Any] = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
UpperCamelCase_: Dict = storage.field("""bytes""" )
else:
UpperCamelCase_: Tuple = pa.array([None] * len(snake_case_ ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
UpperCamelCase_: Optional[Any] = storage.field("""path""" )
else:
UpperCamelCase_: Any = pa.array([None] * len(snake_case_ ) , type=pa.string() )
UpperCamelCase_: Optional[int] = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
UpperCamelCase_: Any = pa.array(
[encode_np_array(np.array(snake_case_ ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
UpperCamelCase_: int = pa.array([None] * len(snake_case_ ) , type=pa.string() )
UpperCamelCase_: Optional[Any] = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(snake_case_ , self.pa_type )
def lowerCAmelCase__ ( self : Optional[Any] , snake_case_ : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(snake_case_ : Optional[int] ):
with xopen(snake_case_ , """rb""" ) as f:
UpperCamelCase_: Optional[int] = f.read()
return bytes_
UpperCamelCase_: int = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
UpperCamelCase_: str = pa.array(
[os.path.basename(snake_case_ ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
UpperCamelCase_: Optional[int] = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(snake_case_ , self.pa_type )
def A__ ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
UpperCamelCase_: Dict = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def A__ ( lowerCamelCase ) -> bytes:
UpperCamelCase_: Any = BytesIO()
if image.format in list_image_compression_formats():
UpperCamelCase_: Union[str, Any] = image.format
else:
UpperCamelCase_: Any = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(lowerCamelCase , format=lowerCamelCase )
return buffer.getvalue()
def A__ ( lowerCamelCase ) -> dict:
if hasattr(lowerCamelCase , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(lowerCamelCase )}
def A__ ( lowerCamelCase ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
UpperCamelCase_: Tuple = array.dtype
UpperCamelCase_: int = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
UpperCamelCase_: Any = dtype.kind
UpperCamelCase_: Any = dtype.itemsize
UpperCamelCase_: Any = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
UpperCamelCase_: int = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
UpperCamelCase_: List[str] = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
UpperCamelCase_: List[str] = dtype_byteorder + dtype_kind + str(lowerCamelCase )
UpperCamelCase_: Dict = np.dtype(lowerCamelCase )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
UpperCamelCase_: List[str] = PIL.Image.fromarray(array.astype(lowerCamelCase ) )
return {"path": None, "bytes": image_to_bytes(lowerCamelCase )}
def A__ ( lowerCamelCase ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
UpperCamelCase_, UpperCamelCase_: Optional[Any] = first_non_null_value(lowerCamelCase )
if isinstance(lowerCamelCase , lowerCamelCase ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(lowerCamelCase , np.ndarray ):
UpperCamelCase_: Optional[int] = no_op_if_value_is_null(lowerCamelCase )
return [obj_to_image_dict_func(lowerCamelCase ) for obj in objs]
elif isinstance(lowerCamelCase , PIL.Image.Image ):
UpperCamelCase_: Tuple = no_op_if_value_is_null(lowerCamelCase )
return [obj_to_image_dict_func(lowerCamelCase ) for obj in objs]
else:
return objs
else:
return objs
| 223 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCamelCase_ : Optional[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class _UpperCamelCase ( datasets.BuilderConfig ):
'''simple docstring'''
__UpperCamelCase : Optional[datasets.Features] = None
__UpperCamelCase : str = "utf-8"
__UpperCamelCase : Optional[str] = None
__UpperCamelCase : Optional[str] = None
__UpperCamelCase : bool = True # deprecated
__UpperCamelCase : Optional[int] = None # deprecated
__UpperCamelCase : int = 10 << 20 # 10MB
__UpperCamelCase : Optional[bool] = None
class _UpperCamelCase ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
__UpperCamelCase : Tuple = JsonConfig
def lowerCAmelCase__ ( self : int ):
if self.config.block_size is not None:
logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead""" )
UpperCamelCase_: List[str] = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
"""The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore.""" )
if self.config.newlines_in_values is not None:
raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported""" )
return datasets.DatasetInfo(features=self.config.features )
def lowerCAmelCase__ ( self : Dict , snake_case_ : str ):
if not self.config.data_files:
raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
UpperCamelCase_: Dict = dl_manager.download_and_extract(self.config.data_files )
if isinstance(snake_case_ , (str, list, tuple) ):
UpperCamelCase_: List[Any] = data_files
if isinstance(snake_case_ , snake_case_ ):
UpperCamelCase_: str = [files]
UpperCamelCase_: Any = [dl_manager.iter_files(snake_case_ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )]
UpperCamelCase_: Dict = []
for split_name, files in data_files.items():
if isinstance(snake_case_ , snake_case_ ):
UpperCamelCase_: Tuple = [files]
UpperCamelCase_: Optional[int] = [dl_manager.iter_files(snake_case_ ) for file in files]
splits.append(datasets.SplitGenerator(name=snake_case_ , gen_kwargs={"""files""": files} ) )
return splits
def lowerCAmelCase__ ( self : str , snake_case_ : pa.Table ):
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
UpperCamelCase_: Union[str, Any] = self.config.features.arrow_schema.field(snake_case_ ).type
UpperCamelCase_: Tuple = pa_table.append_column(snake_case_ , pa.array([None] * len(snake_case_ ) , type=snake_case_ ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
UpperCamelCase_: int = table_cast(snake_case_ , self.config.features.arrow_schema )
return pa_table
def lowerCAmelCase__ ( self : Dict , snake_case_ : Optional[Any] ):
for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case_ ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(snake_case_ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
UpperCamelCase_: Dict = json.load(snake_case_ )
# We keep only the field we are interested in
UpperCamelCase_: Optional[int] = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(snake_case_ , (list, tuple) ):
UpperCamelCase_: Optional[int] = set().union(*[row.keys() for row in dataset] )
UpperCamelCase_: int = {col: [row.get(snake_case_ ) for row in dataset] for col in keys}
else:
UpperCamelCase_: Optional[int] = dataset
UpperCamelCase_: List[str] = pa.Table.from_pydict(snake_case_ )
yield file_idx, self._cast_table(snake_case_ )
# If the file has one json object per line
else:
with open(snake_case_ , """rb""" ) as f:
UpperCamelCase_: Optional[int] = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
UpperCamelCase_: Optional[int] = max(self.config.chunksize // 32 , 16 << 10 )
UpperCamelCase_: Tuple = (
self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
)
while True:
UpperCamelCase_: int = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(snake_case_ )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
UpperCamelCase_: Tuple = batch.decode(self.config.encoding , errors=snake_case_ ).encode("""utf-8""" )
try:
while True:
try:
UpperCamelCase_: Tuple = paj.read_json(
io.BytesIO(snake_case_ ) , read_options=paj.ReadOptions(block_size=snake_case_ ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(snake_case_ , pa.ArrowInvalid )
and "straddling" not in str(snake_case_ )
or block_size > len(snake_case_ )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f'''Batch of {len(snake_case_ )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.''' )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
snake_case_ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
UpperCamelCase_: Optional[Any] = json.load(snake_case_ )
except json.JSONDecodeError:
logger.error(f'''Failed to read file \'{file}\' with error {type(snake_case_ )}: {e}''' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(snake_case_ , snake_case_ ): # list is the only sequence type supported in JSON
try:
UpperCamelCase_: Any = set().union(*[row.keys() for row in dataset] )
UpperCamelCase_: List[str] = {col: [row.get(snake_case_ ) for row in dataset] for col in keys}
UpperCamelCase_: int = pa.Table.from_pydict(snake_case_ )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f'''Failed to read file \'{file}\' with error {type(snake_case_ )}: {e}''' )
raise ValueError(f'''Not able to read records in the JSON file at {file}.''' ) from None
yield file_idx, self._cast_table(snake_case_ )
break
else:
logger.error(f'''Failed to read file \'{file}\' with error {type(snake_case_ )}: {e}''' )
raise ValueError(
f'''Not able to read records in the JSON file at {file}. '''
f'''You should probably indicate the field of the JSON file containing your records. '''
f'''This JSON file contain the following fields: {str(list(dataset.keys() ) )}. '''
f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ''' ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(snake_case_ )
batch_idx += 1
| 223 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=a__ )
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
__UpperCamelCase : str = field(default='''audio-classification''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
__UpperCamelCase : ClassVar[Features] = Features({'''audio''': Audio()} )
__UpperCamelCase : ClassVar[Features] = Features({'''labels''': ClassLabel} )
__UpperCamelCase : str = "audio"
__UpperCamelCase : str = "labels"
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
if self.label_column not in features:
raise ValueError(F'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , SCREAMING_SNAKE_CASE__ ):
raise ValueError(F'''Column {self.label_column} is not a ClassLabel.''' )
SCREAMING_SNAKE_CASE__ : List[Any] = copy.deepcopy(self )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.label_schema.copy()
SCREAMING_SNAKE_CASE__ : Dict = features[self.label_column]
SCREAMING_SNAKE_CASE__ : Any = label_schema
return task_template
@property
def __magic_name__ (self ) -> Dict[str, str]:
"""simple docstring"""
return {
self.audio_column: "audio",
self.label_column: "labels",
}
| 25 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __snake_case ( ):
__a , __a = 9, 14 # noqa: F841
__a = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
__a = defaultdict(_UpperCAmelCase )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
__a = mst(_UpperCAmelCase )
__a = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
__a = tuple(answer[:2] )
__a = tuple(edge[::-1] )
assert edge in result or reverse in result
| 49 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Union[str, Any] = {'vocab_file': 'sentencepiece.bpe.model'}
A_ : Union[str, Any] = {
'vocab_file': {
'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez-orangesum-title': (
'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
),
},
}
A_ : Union[str, Any] = {
'moussaKam/mbarthez': 1024,
'moussaKam/barthez': 1024,
'moussaKam/barthez-orangesum-title': 1024,
}
A_ : str = '▁'
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : Tuple =VOCAB_FILES_NAMES
a : str =PRETRAINED_VOCAB_FILES_MAP
a : Tuple =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a : str =['''input_ids''', '''attention_mask''']
def __init__( self , _lowerCamelCase , _lowerCamelCase="<s>" , _lowerCamelCase="</s>" , _lowerCamelCase="</s>" , _lowerCamelCase="<s>" , _lowerCamelCase="<unk>" , _lowerCamelCase="<pad>" , _lowerCamelCase="<mask>" , _lowerCamelCase = None , **_lowerCamelCase , ):
# Mask token behave like a normal word, i.e. include the space before it
UpperCamelCase_: List[str] = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else mask_token
UpperCamelCase_: str = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , sep_token=_lowerCamelCase , cls_token=_lowerCamelCase , pad_token=_lowerCamelCase , mask_token=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCamelCase , )
UpperCamelCase_: Optional[int] = vocab_file
UpperCamelCase_: int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_lowerCamelCase ) )
UpperCamelCase_: Optional[Any] = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
UpperCamelCase_: int = len(self.sp_model ) - 1
UpperCamelCase_: Dict = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def _a ( self , _lowerCamelCase , _lowerCamelCase = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCamelCase_: Dict = [self.cls_token_id]
UpperCamelCase_: Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _a ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(_lowerCamelCase )) + [1]
return [1] + ([0] * len(_lowerCamelCase )) + [1, 1] + ([0] * len(_lowerCamelCase )) + [1]
def _a ( self , _lowerCamelCase , _lowerCamelCase = None ):
UpperCamelCase_: List[Any] = [self.sep_token_id]
UpperCamelCase_: Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def _a ( self ):
return len(self.sp_model )
def _a ( self ):
UpperCamelCase_: Any = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _a ( self , _lowerCamelCase ):
return self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
def _a ( self , _lowerCamelCase ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCamelCase_: Dict = self.sp_model.PieceToId(_lowerCamelCase )
return spm_id if spm_id else self.unk_token_id
def _a ( self , _lowerCamelCase ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(_lowerCamelCase )
def _a ( self , _lowerCamelCase ):
UpperCamelCase_: List[str] = []
UpperCamelCase_: Optional[Any] = ''
UpperCamelCase_: List[Any] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_lowerCamelCase ) + token
UpperCamelCase_: Union[str, Any] = True
UpperCamelCase_: Tuple = []
else:
current_sub_tokens.append(_lowerCamelCase )
UpperCamelCase_: List[str] = False
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def __getstate__( self ):
UpperCamelCase_: Any = self.__dict__.copy()
UpperCamelCase_: str = None
return state
def __setstate__( self , _lowerCamelCase ):
UpperCamelCase_: Tuple = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
UpperCamelCase_: Union[str, Any] = {}
UpperCamelCase_: int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a ( self , _lowerCamelCase , _lowerCamelCase = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase_: List[Any] = os.path.join(
_lowerCamelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , 'wb' ) as fi:
UpperCamelCase_: int = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 352 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Tuple = logging.get_logger(__name__)
A_ : Dict = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : Tuple ='''xglm'''
a : List[Any] =['''past_key_values''']
a : Union[str, Any] ={
'''num_attention_heads''': '''attention_heads''',
'''hidden_size''': '''d_model''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowerCamelCase=2_5_6_0_0_8 , _lowerCamelCase=2_0_4_8 , _lowerCamelCase=1_0_2_4 , _lowerCamelCase=4_0_9_6 , _lowerCamelCase=2_4 , _lowerCamelCase=1_6 , _lowerCamelCase="gelu" , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=0.0 , _lowerCamelCase=0.0 , _lowerCamelCase=0.0_2 , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=2 , _lowerCamelCase=1 , _lowerCamelCase=0 , _lowerCamelCase=2 , **_lowerCamelCase , ):
UpperCamelCase_: Optional[Any] = vocab_size
UpperCamelCase_: Optional[int] = max_position_embeddings
UpperCamelCase_: List[str] = d_model
UpperCamelCase_: List[Any] = ffn_dim
UpperCamelCase_: List[Any] = num_layers
UpperCamelCase_: List[Any] = attention_heads
UpperCamelCase_: Tuple = activation_function
UpperCamelCase_: Tuple = dropout
UpperCamelCase_: Tuple = attention_dropout
UpperCamelCase_: Optional[Any] = activation_dropout
UpperCamelCase_: List[str] = layerdrop
UpperCamelCase_: Any = init_std
UpperCamelCase_: Any = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase_: Union[str, Any] = use_cache
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , ) | 292 | 0 |
from __future__ import annotations
def _a ( lowerCamelCase: int , lowerCamelCase: int ) -> tuple[int, int]:
'''simple docstring'''
if b == 0:
return (1, 0)
((__A) , (__A)) = extended_euclid(lowerCamelCase , a % b )
__A = a // b
return (y, x - k * y)
def _a ( lowerCamelCase: int , lowerCamelCase: int , lowerCamelCase: int , lowerCamelCase: int ) -> int:
'''simple docstring'''
((__A) , (__A)) = extended_euclid(lowerCamelCase , lowerCamelCase )
__A = na * na
__A = ra * x * na + ra * y * na
return (n % m + m) % m
def _a ( lowerCamelCase: int , lowerCamelCase: int ) -> int:
'''simple docstring'''
((__A) , (__A)) = extended_euclid(lowerCamelCase , lowerCamelCase )
if b < 0:
__A = (b % n + n) % n
return b
def _a ( lowerCamelCase: int , lowerCamelCase: int , lowerCamelCase: int , lowerCamelCase: int ) -> int:
'''simple docstring'''
__A , __A = invert_modulo(lowerCamelCase , lowerCamelCase ), invert_modulo(lowerCamelCase , lowerCamelCase )
__A = na * na
__A = ra * x * na + ra * y * na
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name='chinese_remainder_theorem', verbose=True)
testmod(name='chinese_remainder_theorem2', verbose=True)
testmod(name='invert_modulo', verbose=True)
testmod(name='extended_euclid', verbose=True)
| 117 |
# 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
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case__ : List[str] = {
'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Tuple = ['VivitImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Any = [
'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'VivitModel',
'VivitPreTrainedModel',
'VivitForVideoClassification',
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
snake_case__ : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 117 | 1 |
"""simple docstring"""
import re
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
if len(re.findall('[ATCG]' , _UpperCAmelCase ) ) != len(_UpperCAmelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 350 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : list[str] | None = None ):
lowerCAmelCase = word_bank or []
# create a table
lowerCAmelCase = len(_UpperCAmelCase ) + 1
lowerCAmelCase = []
for _ in range(_UpperCAmelCase ):
table.append([] )
# seed value
lowerCAmelCase = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(_UpperCAmelCase ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(_UpperCAmelCase )] == word:
lowerCAmelCase = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(_UpperCAmelCase )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(_UpperCAmelCase )]:
combination.reverse()
return table[len(_UpperCAmelCase )]
if __name__ == "__main__":
print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
print(
all_construct(
'''hexagonosaurus''',
['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
)
)
| 309 | 0 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase__ : float):
return 0.0
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
SCREAMING_SNAKE_CASE_: str = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Dict = 5_12
SCREAMING_SNAKE_CASE_: Optional[Any] = [1] + [0] * (size - 1)
SCREAMING_SNAKE_CASE_: str = [filter_type.process(_UpperCAmelCase ) for item in inputs]
SCREAMING_SNAKE_CASE_: str = [0] * (samplerate - size) # zero-padding
outputs += filler
SCREAMING_SNAKE_CASE_: Optional[int] = np.abs(np.fft.fft(_UpperCAmelCase ) )
SCREAMING_SNAKE_CASE_: int = 20 * np.logaa(_UpperCAmelCase )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
# Display within reasonable bounds
SCREAMING_SNAKE_CASE_: Union[str, Any] = get_bounds(_UpperCAmelCase , _UpperCAmelCase )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel("Gain (dB)" )
plt.plot(_UpperCAmelCase )
plt.show()
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = 5_12
SCREAMING_SNAKE_CASE_: Tuple = [1] + [0] * (size - 1)
SCREAMING_SNAKE_CASE_: str = [filter_type.process(_UpperCAmelCase ) for item in inputs]
SCREAMING_SNAKE_CASE_: Any = [0] * (samplerate - size) # zero-padding
outputs += filler
SCREAMING_SNAKE_CASE_: Tuple = np.angle(np.fft.fft(_UpperCAmelCase ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel("Phase shift (Radians)" )
plt.plot(np.unwrap(_UpperCAmelCase , -2 * pi ) )
plt.show()
| 13 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase = False ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = f"Expected string as input, found {type(_UpperCAmelCase )}"
raise ValueError(_UpperCAmelCase )
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Optional[Any] = f"Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase )}"
raise ValueError(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: Tuple = input_str.split("_" )
SCREAMING_SNAKE_CASE_: str = 0 if use_pascal else 1
SCREAMING_SNAKE_CASE_: int = words[start_index:]
SCREAMING_SNAKE_CASE_: List[str] = [word[0].upper() + word[1:] for word in words_to_capitalize]
SCREAMING_SNAKE_CASE_: List[Any] = "" if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 13 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def lowerCAmelCase_ ( ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ = ArgumentParser("Transformers CLI tool" , usage="transformers-cli <command> [<args>]" )
UpperCAmelCase_ = parser.add_subparsers(help="transformers-cli command helpers" )
# Register commands
ConvertCommand.register_subcommand(snake_case_ )
DownloadCommand.register_subcommand(snake_case_ )
EnvironmentCommand.register_subcommand(snake_case_ )
RunCommand.register_subcommand(snake_case_ )
ServeCommand.register_subcommand(snake_case_ )
UserCommands.register_subcommand(snake_case_ )
AddNewModelCommand.register_subcommand(snake_case_ )
AddNewModelLikeCommand.register_subcommand(snake_case_ )
LfsCommands.register_subcommand(snake_case_ )
PTtoTFCommand.register_subcommand(snake_case_ )
# Let's go
UpperCAmelCase_ = parser.parse_args()
if not hasattr(snake_case_ , "func" ):
parser.print_help()
exit(1 )
# Run
UpperCAmelCase_ = args.func(snake_case_ )
service.run()
if __name__ == "__main__":
main()
| 106 | '''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_: Any =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: List[Any] =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'),
]
)
SCREAMING_SNAKE_CASE_: List[Any] =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'),
]
)
SCREAMING_SNAKE_CASE_: Any =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'),
]
)
SCREAMING_SNAKE_CASE_: int =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'),
]
)
SCREAMING_SNAKE_CASE_: str =OrderedDict(
[
# Model for Image-classsification
('beit', 'FlaxBeitForImageClassification'),
('regnet', 'FlaxRegNetForImageClassification'),
('resnet', 'FlaxResNetForImageClassification'),
('vit', 'FlaxViTForImageClassification'),
]
)
SCREAMING_SNAKE_CASE_: str =OrderedDict(
[
('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
]
)
SCREAMING_SNAKE_CASE_: List[Any] =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'),
]
)
SCREAMING_SNAKE_CASE_: Optional[int] =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'),
]
)
SCREAMING_SNAKE_CASE_: Any =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'),
]
)
SCREAMING_SNAKE_CASE_: Any =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'),
]
)
SCREAMING_SNAKE_CASE_: Optional[Any] =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'),
]
)
SCREAMING_SNAKE_CASE_: int =OrderedDict(
[
('bert', 'FlaxBertForNextSentencePrediction'),
]
)
SCREAMING_SNAKE_CASE_: List[Any] =OrderedDict(
[
('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
('whisper', 'FlaxWhisperForConditionalGeneration'),
]
)
SCREAMING_SNAKE_CASE_: Optional[int] =OrderedDict(
[
('whisper', 'FlaxWhisperForAudioClassification'),
]
)
SCREAMING_SNAKE_CASE_: str =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_: str =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_: List[str] =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_: List[Any] =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_: Optional[Any] =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_: Dict =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_: Optional[Any] =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_: Any =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_: List[str] =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_: Dict =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_: Any =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_: Dict =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_: Dict =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_: Optional[Any] =_LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __A ( _BaseAutoModelClass ):
a__ : int = FLAX_MODEL_MAPPING
SCREAMING_SNAKE_CASE_: Dict =auto_class_update(FlaxAutoModel)
class __A ( _BaseAutoModelClass ):
a__ : str = FLAX_MODEL_FOR_PRETRAINING_MAPPING
SCREAMING_SNAKE_CASE_: Dict =auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')
class __A ( _BaseAutoModelClass ):
a__ : Optional[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_: Tuple =auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')
class __A ( _BaseAutoModelClass ):
a__ : Optional[Any] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
SCREAMING_SNAKE_CASE_: Optional[Any] =auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')
class __A ( _BaseAutoModelClass ):
a__ : List[Any] = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_: Optional[Any] =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
SCREAMING_SNAKE_CASE_: Optional[int] =auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)
class __A ( _BaseAutoModelClass ):
a__ : Optional[int] = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
SCREAMING_SNAKE_CASE_: List[Any] =auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')
class __A ( _BaseAutoModelClass ):
a__ : Tuple = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_: List[Any] =auto_class_update(
FlaxAutoModelForTokenClassification, head_doc='token classification'
)
class __A ( _BaseAutoModelClass ):
a__ : Dict = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
SCREAMING_SNAKE_CASE_: Any =auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')
class __A ( _BaseAutoModelClass ):
a__ : Union[str, Any] = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
SCREAMING_SNAKE_CASE_: int =auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)
class __A ( _BaseAutoModelClass ):
a__ : int = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_: Dict =auto_class_update(
FlaxAutoModelForImageClassification, head_doc='image classification'
)
class __A ( _BaseAutoModelClass ):
a__ : Any = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_: Optional[int] =auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')
class __A ( _BaseAutoModelClass ):
a__ : List[Any] = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_: Union[str, Any] =auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)
| 106 | 1 |
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 ):
def __init__( self, lowerCamelCase, lowerCamelCase=13, lowerCamelCase=7, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=99, lowerCamelCase=32, lowerCamelCase=5, lowerCamelCase=4, lowerCamelCase=37, lowerCamelCase="gelu", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=5_12, lowerCamelCase=16, lowerCamelCase=2, lowerCamelCase=0.0_2, lowerCamelCase=False, lowerCamelCase=True, lowerCamelCase="None", lowerCamelCase=3, lowerCamelCase=4, lowerCamelCase=None, ) -> str:
"""simple docstring"""
_lowercase : Dict = parent
_lowercase : Optional[int] = batch_size
_lowercase : Tuple = seq_length
_lowercase : Tuple = is_training
_lowercase : Any = use_input_mask
_lowercase : Optional[Any] = use_token_type_ids
_lowercase : Tuple = use_labels
_lowercase : Any = vocab_size
_lowercase : int = hidden_size
_lowercase : Optional[Any] = num_hidden_layers
_lowercase : Optional[int] = num_attention_heads
_lowercase : Union[str, Any] = intermediate_size
_lowercase : str = hidden_act
_lowercase : List[str] = hidden_dropout_prob
_lowercase : Tuple = attention_probs_dropout_prob
_lowercase : Tuple = max_position_embeddings
_lowercase : int = type_vocab_size
_lowercase : Tuple = type_sequence_label_size
_lowercase : Any = initializer_range
_lowercase : Optional[Any] = num_labels
_lowercase : Tuple = num_choices
_lowercase : Optional[int] = relative_attention
_lowercase : Optional[int] = position_biased_input
_lowercase : Tuple = pos_att_type
_lowercase : Dict = scope
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
_lowercase : str = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
_lowercase : Any = None
if self.use_input_mask:
_lowercase : Optional[int] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
_lowercase : Optional[int] = None
if self.use_token_type_ids:
_lowercase : List[Any] = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
_lowercase : List[str] = None
_lowercase : Optional[int] = None
_lowercase : List[Any] = None
if self.use_labels:
_lowercase : Union[str, Any] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowercase : Any = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
_lowercase : Optional[Any] = ids_tensor([self.batch_size], self.num_choices)
_lowercase : Optional[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self) -> Union[str, Any]:
"""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) -> int:
"""simple docstring"""
_lowercase : List[Any] = self.get_config()
_lowercase : str = 3_00
return config
def UpperCamelCase ( self, lowerCamelCase) -> Tuple:
"""simple docstring"""
self.parent.assertListEqual(list(result.loss.size()), [])
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> List[str]:
"""simple docstring"""
_lowercase : Optional[int] = DebertaModel(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Optional[int] = model(lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase)[0]
_lowercase : Union[str, Any] = model(lowerCamelCase, token_type_ids=lowerCamelCase)[0]
_lowercase : Any = model(lowerCamelCase)[0]
self.parent.assertListEqual(list(sequence_output.size()), [self.batch_size, self.seq_length, self.hidden_size])
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> Optional[Any]:
"""simple docstring"""
_lowercase : Dict = DebertaForMaskedLM(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : str = model(lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, labels=lowerCamelCase)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> Union[str, Any]:
"""simple docstring"""
_lowercase : List[Any] = self.num_labels
_lowercase : int = DebertaForSequenceClassification(lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Dict = model(lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, labels=lowerCamelCase)
self.parent.assertListEqual(list(result.logits.size()), [self.batch_size, self.num_labels])
self.check_loss_output(lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> Optional[int]:
"""simple docstring"""
_lowercase : Union[str, Any] = self.num_labels
_lowercase : Optional[int] = DebertaForTokenClassification(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : List[str] = model(lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, labels=lowerCamelCase)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> int:
"""simple docstring"""
_lowercase : Tuple = DebertaForQuestionAnswering(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Optional[Any] = model(
lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, start_positions=lowerCamelCase, end_positions=lowerCamelCase, )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def UpperCamelCase ( self) -> Tuple:
"""simple docstring"""
_lowercase : List[Any] = self.prepare_config_and_inputs()
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
) : int = config_and_inputs
_lowercase : Dict = {'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 ):
lowercase_ : Optional[int] = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowercase_ : Dict = (
{
"""feature-extraction""": DebertaModel,
"""fill-mask""": DebertaForMaskedLM,
"""question-answering""": DebertaForQuestionAnswering,
"""text-classification""": DebertaForSequenceClassification,
"""token-classification""": DebertaForTokenClassification,
"""zero-shot""": DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase_ : Union[str, Any] = True
lowercase_ : Optional[int] = False
lowercase_ : Union[str, Any] = False
lowercase_ : Optional[int] = False
lowercase_ : Dict = False
def UpperCamelCase ( self) -> int:
"""simple docstring"""
_lowercase : Any = DebertaModelTester(self)
_lowercase : List[str] = ConfigTester(self, config_class=lowerCamelCase, hidden_size=37)
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase ( self) -> int:
"""simple docstring"""
_lowercase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*lowerCamelCase)
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase)
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
_lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase)
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase)
def UpperCamelCase ( self) -> str:
"""simple docstring"""
_lowercase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase)
@slow
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase : Optional[int] = DebertaModel.from_pretrained(lowerCamelCase)
self.assertIsNotNone(lowerCamelCase)
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase( unittest.TestCase ):
@unittest.skip(reason='Model not available yet')
def UpperCamelCase ( self) -> Tuple:
"""simple docstring"""
pass
@slow
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
_lowercase : List[str] = DebertaModel.from_pretrained('microsoft/deberta-base')
_lowercase : List[Any] = torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
_lowercase : int = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
with torch.no_grad():
_lowercase : List[str] = model(lowerCamelCase, attention_mask=lowerCamelCase)[0]
# compare the actual values for a slice.
_lowercase : Optional[Any] = 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], lowerCamelCase, atol=1E-4), F'''{output[:, 1:4, 1:4]}''')
| 21 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _UpperCamelCase ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , __a ):
super().__init__()
__lowerCAmelCase = model
__lowerCAmelCase = 2
__lowerCAmelCase = nn.Linear(self.model.config.hidden_size , self.num_labels )
def snake_case ( self ):
pass
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = LongformerModel.from_pretrained(_UpperCamelCase )
__lowerCAmelCase = LightningModel(_UpperCamelCase )
__lowerCAmelCase = torch.load(_UpperCamelCase , map_location=torch.device("cpu" ) )
lightning_model.load_state_dict(ckpt["state_dict"] )
# init longformer question answering model
__lowerCAmelCase = LongformerForQuestionAnswering.from_pretrained(_UpperCamelCase )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(_UpperCamelCase )
print(f"Conversion successful. Model saved under {pytorch_dump_folder_path}" )
if __name__ == "__main__":
A : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--longformer_model",
default=None,
type=str,
required=True,
help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
)
parser.add_argument(
"--longformer_question_answering_ckpt_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch Lightning Checkpoint.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
A : Optional[int] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 57 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
class lowercase_ ( nn.Module ):
def __init__( self , lowercase_=3 , lowercase_=3 , lowercase_=("DownEncoderBlock2D",) , lowercase_=(64,) , lowercase_=2 , lowercase_=32 , lowercase_="silu" , lowercase_=True , ):
super().__init__()
_snake_case : List[Any] = layers_per_block
_snake_case : Tuple = torch.nn.Convad(
__UpperCAmelCase , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
_snake_case : Optional[int] = None
_snake_case : Optional[int] = nn.ModuleList([] )
# down
_snake_case : Tuple = block_out_channels[0]
for i, down_block_type in enumerate(__UpperCAmelCase ):
_snake_case : Dict = output_channel
_snake_case : str = block_out_channels[i]
_snake_case : Optional[int] = i == len(__UpperCAmelCase ) - 1
_snake_case : List[Any] = get_down_block(
__UpperCAmelCase , num_layers=self.layers_per_block , in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , add_downsample=not is_final_block , resnet_eps=1e-6 , downsample_padding=0 , resnet_act_fn=__UpperCAmelCase , resnet_groups=__UpperCAmelCase , attention_head_dim=__UpperCAmelCase , temb_channels=__UpperCAmelCase , )
self.down_blocks.append(__UpperCAmelCase )
# mid
_snake_case : Tuple = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=__UpperCAmelCase , output_scale_factor=1 , resnet_time_scale_shift="default" , attention_head_dim=block_out_channels[-1] , resnet_groups=__UpperCAmelCase , temb_channels=__UpperCAmelCase , )
# out
_snake_case : List[Any] = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=__UpperCAmelCase , eps=1e-6 )
_snake_case : Union[str, Any] = nn.SiLU()
_snake_case : Dict = 2 * out_channels if double_z else out_channels
_snake_case : Optional[int] = nn.Convad(block_out_channels[-1] , __UpperCAmelCase , 3 , padding=1 )
_snake_case : Tuple = False
def UpperCamelCase ( self , lowercase_ ):
_snake_case : Optional[Any] = x
_snake_case : Any = self.conv_in(__UpperCAmelCase )
if self.training and self.gradient_checkpointing:
def create_custom_forward(lowercase_ ):
def custom_forward(*lowercase_ ):
return module(*__UpperCAmelCase )
return custom_forward
# down
if is_torch_version(">=" , "1.11.0" ):
for down_block in self.down_blocks:
_snake_case : int = torch.utils.checkpoint.checkpoint(
create_custom_forward(__UpperCAmelCase ) , __UpperCAmelCase , use_reentrant=__UpperCAmelCase )
# middle
_snake_case : Dict = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , __UpperCAmelCase , use_reentrant=__UpperCAmelCase )
else:
for down_block in self.down_blocks:
_snake_case : Optional[Any] = torch.utils.checkpoint.checkpoint(create_custom_forward(__UpperCAmelCase ) , __UpperCAmelCase )
# middle
_snake_case : str = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , __UpperCAmelCase )
else:
# down
for down_block in self.down_blocks:
_snake_case : Dict = down_block(__UpperCAmelCase )
# middle
_snake_case : List[str] = self.mid_block(__UpperCAmelCase )
# post-process
_snake_case : Dict = self.conv_norm_out(__UpperCAmelCase )
_snake_case : Dict = self.conv_act(__UpperCAmelCase )
_snake_case : List[str] = self.conv_out(__UpperCAmelCase )
return sample
class lowercase_ ( nn.Module ):
def __init__( self , lowercase_=3 , lowercase_=3 , lowercase_=("UpDecoderBlock2D",) , lowercase_=(64,) , lowercase_=2 , lowercase_=32 , lowercase_="silu" , lowercase_="group" , ):
super().__init__()
_snake_case : Optional[int] = layers_per_block
_snake_case : Any = nn.Convad(
__UpperCAmelCase , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
_snake_case : List[str] = None
_snake_case : Optional[Any] = nn.ModuleList([] )
_snake_case : str = in_channels if norm_type == "spatial" else None
# mid
_snake_case : Optional[int] = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=__UpperCAmelCase , output_scale_factor=1 , resnet_time_scale_shift="default" if norm_type == "group" else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=__UpperCAmelCase , temb_channels=__UpperCAmelCase , )
# up
_snake_case : int = list(reversed(__UpperCAmelCase ) )
_snake_case : str = reversed_block_out_channels[0]
for i, up_block_type in enumerate(__UpperCAmelCase ):
_snake_case : Any = output_channel
_snake_case : List[Any] = reversed_block_out_channels[i]
_snake_case : Union[str, Any] = i == len(__UpperCAmelCase ) - 1
_snake_case : List[Any] = get_up_block(
__UpperCAmelCase , num_layers=self.layers_per_block + 1 , in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , prev_output_channel=__UpperCAmelCase , add_upsample=not is_final_block , resnet_eps=1e-6 , resnet_act_fn=__UpperCAmelCase , resnet_groups=__UpperCAmelCase , attention_head_dim=__UpperCAmelCase , temb_channels=__UpperCAmelCase , resnet_time_scale_shift=__UpperCAmelCase , )
self.up_blocks.append(__UpperCAmelCase )
_snake_case : List[str] = output_channel
# out
if norm_type == "spatial":
_snake_case : Any = SpatialNorm(block_out_channels[0] , __UpperCAmelCase )
else:
_snake_case : str = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=__UpperCAmelCase , eps=1e-6 )
_snake_case : Any = nn.SiLU()
_snake_case : List[Any] = nn.Convad(block_out_channels[0] , __UpperCAmelCase , 3 , padding=1 )
_snake_case : Optional[int] = False
def UpperCamelCase ( self , lowercase_ , lowercase_=None ):
_snake_case : Any = z
_snake_case : Any = self.conv_in(__UpperCAmelCase )
_snake_case : List[str] = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(lowercase_ ):
def custom_forward(*lowercase_ ):
return module(*__UpperCAmelCase )
return custom_forward
if is_torch_version(">=" , "1.11.0" ):
# middle
_snake_case : str = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , __UpperCAmelCase , __UpperCAmelCase , use_reentrant=__UpperCAmelCase )
_snake_case : Union[str, Any] = sample.to(__UpperCAmelCase )
# up
for up_block in self.up_blocks:
_snake_case : Optional[Any] = torch.utils.checkpoint.checkpoint(
create_custom_forward(__UpperCAmelCase ) , __UpperCAmelCase , __UpperCAmelCase , use_reentrant=__UpperCAmelCase )
else:
# middle
_snake_case : Dict = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , __UpperCAmelCase , __UpperCAmelCase )
_snake_case : Tuple = sample.to(__UpperCAmelCase )
# up
for up_block in self.up_blocks:
_snake_case : Optional[int] = torch.utils.checkpoint.checkpoint(create_custom_forward(__UpperCAmelCase ) , __UpperCAmelCase , __UpperCAmelCase )
else:
# middle
_snake_case : Optional[int] = self.mid_block(__UpperCAmelCase , __UpperCAmelCase )
_snake_case : Tuple = sample.to(__UpperCAmelCase )
# up
for up_block in self.up_blocks:
_snake_case : Optional[int] = up_block(__UpperCAmelCase , __UpperCAmelCase )
# post-process
if latent_embeds is None:
_snake_case : Union[str, Any] = self.conv_norm_out(__UpperCAmelCase )
else:
_snake_case : Any = self.conv_norm_out(__UpperCAmelCase , __UpperCAmelCase )
_snake_case : Union[str, Any] = self.conv_act(__UpperCAmelCase )
_snake_case : Union[str, Any] = self.conv_out(__UpperCAmelCase )
return sample
class lowercase_ ( nn.Module ):
def __init__( self , lowercase_ , lowercase_ , lowercase_ , lowercase_=None , lowercase_="random" , lowercase_=False , lowercase_=True ):
super().__init__()
_snake_case : str = n_e
_snake_case : Dict = vq_embed_dim
_snake_case : Optional[Any] = beta
_snake_case : Union[str, Any] = legacy
_snake_case : List[Any] = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
_snake_case : Dict = remap
if self.remap is not None:
self.register_buffer("used" , torch.tensor(np.load(self.remap ) ) )
_snake_case : int = self.used.shape[0]
_snake_case : Union[str, Any] = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
_snake_case : int = self.re_embed
_snake_case : Dict = self.re_embed + 1
print(
f"""Remapping {self.n_e} indices to {self.re_embed} indices. """
f"""Using {self.unknown_index} for unknown indices.""" )
else:
_snake_case : List[Any] = n_e
_snake_case : Any = sane_index_shape
def UpperCamelCase ( self , lowercase_ ):
_snake_case : Optional[Any] = inds.shape
assert len(__UpperCAmelCase ) > 1
_snake_case : Optional[Any] = inds.reshape(ishape[0] , -1 )
_snake_case : List[str] = self.used.to(__UpperCAmelCase )
_snake_case : Optional[int] = (inds[:, :, None] == used[None, None, ...]).long()
_snake_case : Any = match.argmax(-1 )
_snake_case : str = match.sum(2 ) < 1
if self.unknown_index == "random":
_snake_case : int = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
_snake_case : Optional[Any] = self.unknown_index
return new.reshape(__UpperCAmelCase )
def UpperCamelCase ( self , lowercase_ ):
_snake_case : List[Any] = inds.shape
assert len(__UpperCAmelCase ) > 1
_snake_case : int = inds.reshape(ishape[0] , -1 )
_snake_case : int = self.used.to(__UpperCAmelCase )
if self.re_embed > self.used.shape[0]: # extra token
_snake_case : Union[str, Any] = 0 # simply set to zero
_snake_case : Optional[int] = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , __UpperCAmelCase )
return back.reshape(__UpperCAmelCase )
def UpperCamelCase ( self , lowercase_ ):
_snake_case : Optional[Any] = z.permute(0 , 2 , 3 , 1 ).contiguous()
_snake_case : Tuple = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
_snake_case : List[str] = torch.argmin(torch.cdist(__UpperCAmelCase , self.embedding.weight ) , dim=1 )
_snake_case : List[Any] = self.embedding(__UpperCAmelCase ).view(z.shape )
_snake_case : Dict = None
_snake_case : str = None
# compute loss for embedding
if not self.legacy:
_snake_case : int = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
_snake_case : Optional[int] = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
_snake_case : List[str] = z + (z_q - z).detach()
# reshape back to match original input shape
_snake_case : Union[str, Any] = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
_snake_case : str = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
_snake_case : List[str] = self.remap_to_used(__UpperCAmelCase )
_snake_case : List[str] = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
_snake_case : Dict = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def UpperCamelCase ( self , lowercase_ , lowercase_ ):
if self.remap is not None:
_snake_case : Any = indices.reshape(shape[0] , -1 ) # add batch axis
_snake_case : Any = self.unmap_to_all(__UpperCAmelCase )
_snake_case : List[str] = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
_snake_case : Optional[int] = self.embedding(__UpperCAmelCase )
if shape is not None:
_snake_case : Dict = z_q.view(__UpperCAmelCase )
# reshape back to match original input shape
_snake_case : Tuple = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class lowercase_ ( __snake_case ):
def __init__( self , lowercase_ , lowercase_=False ):
_snake_case : Union[str, Any] = parameters
_snake_case ,_snake_case : Any = torch.chunk(__UpperCAmelCase , 2 , dim=1 )
_snake_case : List[str] = torch.clamp(self.logvar , -30.0 , 20.0 )
_snake_case : Dict = deterministic
_snake_case : Dict = torch.exp(0.5 * self.logvar )
_snake_case : Optional[Any] = torch.exp(self.logvar )
if self.deterministic:
_snake_case : Union[str, Any] = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def UpperCamelCase ( self , lowercase_ = None ):
_snake_case : str = randn_tensor(
self.mean.shape , generator=__UpperCAmelCase , device=self.parameters.device , dtype=self.parameters.dtype )
_snake_case : Any = self.mean + self.std * sample
return x
def UpperCamelCase ( self , lowercase_=None ):
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def UpperCamelCase ( self , lowercase_ , lowercase_=[1, 2, 3] ):
if self.deterministic:
return torch.Tensor([0.0] )
_snake_case : Dict = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=__UpperCAmelCase )
def UpperCamelCase ( self ):
return self.mean
| 354 | # 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
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__SCREAMING_SNAKE_CASE : Any = {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = [
'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MraForMaskedLM',
'MraForMultipleChoice',
'MraForQuestionAnswering',
'MraForSequenceClassification',
'MraForTokenClassification',
'MraLayer',
'MraModel',
'MraPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : str = _LazyModule(__name__, globals()['__file__'], _import_structure) | 284 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
a_ : List[str] = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def a_ ( __snake_case : str = "mumbai" ) -> Generator[tuple[str, str], None, None]:
"""simple docstring"""
lowerCamelCase_ =BeautifulSoup(requests.get(url + location ).content , '''html.parser''' )
# This attribute finds out all the specifics listed in a job
for job in soup.find_all('''div''' , attrs={'''data-tn-component''': '''organicJob'''} ):
lowerCamelCase_ =job.find('''a''' , attrs={'''data-tn-element''': '''jobTitle'''} ).text.strip()
lowerCamelCase_ =job.find('''span''' , {'''class''': '''company'''} ).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs("""Bangalore"""), 1):
print(F"""Job {i:>2} is {job[0]} at {job[1]}""")
| 75 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class lowerCamelCase__ ( lowerCamelCase_ , lowerCamelCase_ ):
a__ : Tuple = 1
@register_to_config
def __init__( self , SCREAMING_SNAKE_CASE=2_000 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=20 , SCREAMING_SNAKE_CASE=1E-3 ):
"""simple docstring"""
snake_case : Optional[Any] = None
snake_case : List[str] = None
snake_case : Any = None
def lowerCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None ):
"""simple docstring"""
snake_case : Any = torch.linspace(1 , self.config.sampling_eps , SCREAMING_SNAKE_CASE , device=SCREAMING_SNAKE_CASE )
def lowerCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None ):
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
snake_case : Any = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
snake_case : Optional[int] = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
snake_case : List[str] = std.flatten()
while len(std.shape ) < len(score.shape ):
snake_case : Any = std.unsqueeze(-1 )
snake_case : Optional[Any] = -score / std
# compute
snake_case : int = -1.0 / len(self.timesteps )
snake_case : str = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
snake_case : str = beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
snake_case : Union[str, Any] = beta_t.unsqueeze(-1 )
snake_case : Tuple = -0.5 * beta_t * x
snake_case : Tuple = torch.sqrt(SCREAMING_SNAKE_CASE )
snake_case : List[str] = drift - diffusion**2 * score
snake_case : List[str] = x + drift * dt
# add noise
snake_case : Optional[int] = randn_tensor(x.shape , layout=x.layout , generator=SCREAMING_SNAKE_CASE , device=x.device , dtype=x.dtype )
snake_case : Dict = x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self ):
"""simple docstring"""
return self.config.num_train_timesteps
| 148 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def __UpperCAmelCase ( UpperCAmelCase_ : int ) -> bool:
'''simple docstring'''
__snake_case : Dict = 0
__snake_case : Union[str, Any] = number
while duplicate > 0:
__snake_case , __snake_case : str = divmod(UpperCAmelCase_ , 10 )
fact_sum += factorial(UpperCAmelCase_ )
return fact_sum == number
if __name__ == "__main__":
print("Program to check whether a number is a Krisnamurthy Number or not.")
_a : int= int(input("Enter number: ").strip())
print(
f'''{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number.'''
)
| 95 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class UpperCamelCase ( unittest.TestCase ):
def _lowercase (self : Union[str, Any]) -> Optional[int]:
__snake_case : Optional[Any] = 0
def _lowercase (self : Tuple) -> int:
__snake_case : Optional[Any] = AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
self.assertIsInstance(_A , _A)
def _lowercase (self : str) -> List[str]:
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : List[str] = Path(_A) / 'preprocessor_config.json'
__snake_case : Optional[Any] = Path(_A) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
json.dump({'model_type': 'clip'} , open(_A , 'w'))
__snake_case : Optional[int] = AutoImageProcessor.from_pretrained(_A)
self.assertIsInstance(_A , _A)
def _lowercase (self : Any) -> Optional[int]:
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : Any = Path(_A) / 'preprocessor_config.json'
__snake_case : List[Any] = Path(_A) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
json.dump({'model_type': 'clip'} , open(_A , 'w'))
__snake_case : Tuple = AutoImageProcessor.from_pretrained(_A)
self.assertIsInstance(_A , _A)
def _lowercase (self : List[Any]) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__snake_case : List[Any] = Path(_A) / 'preprocessor_config.json'
__snake_case : Optional[Any] = Path(_A) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
json.dump({'model_type': 'clip'} , open(_A , 'w'))
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__snake_case : List[str] = AutoImageProcessor.from_pretrained(_A).to_dict()
config_dict.pop('image_processor_type')
__snake_case : Optional[int] = CLIPImageProcessor(**_A)
# save in new folder
model_config.save_pretrained(_A)
config.save_pretrained(_A)
__snake_case : Optional[int] = AutoImageProcessor.from_pretrained(_A)
# make sure private variable is not incorrectly saved
__snake_case : int = json.loads(config.to_json_string())
self.assertTrue('_processor_class' not in dict_as_saved)
self.assertIsInstance(_A , _A)
def _lowercase (self : Union[str, Any]) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : int = Path(_A) / 'preprocessor_config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
__snake_case : List[str] = AutoImageProcessor.from_pretrained(_A)
self.assertIsInstance(_A , _A)
def _lowercase (self : Optional[int]) -> Dict:
with self.assertRaisesRegex(
_A , 'clip-base is not a local folder and is not a valid model identifier'):
__snake_case : Tuple = AutoImageProcessor.from_pretrained('clip-base')
def _lowercase (self : str) -> int:
with self.assertRaisesRegex(
_A , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
__snake_case : str = AutoImageProcessor.from_pretrained(_A , revision='aaaaaa')
def _lowercase (self : List[Any]) -> str:
with self.assertRaisesRegex(
_A , 'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.' , ):
__snake_case : List[Any] = AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model')
def _lowercase (self : Optional[int]) -> List[str]:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_A):
__snake_case : Any = AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
# If remote code is disabled, we can't load this config.
with self.assertRaises(_A):
__snake_case : Tuple = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_A)
__snake_case : Union[str, Any] = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_A)
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor')
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_A)
__snake_case : Optional[int] = AutoImageProcessor.from_pretrained(_A , trust_remote_code=_A)
self.assertEqual(reloaded_image_processor.__class__.__name__ , 'NewImageProcessor')
def _lowercase (self : int) -> Optional[int]:
try:
AutoConfig.register('custom' , _A)
AutoImageProcessor.register(_A , _A)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_A):
AutoImageProcessor.register(_A , _A)
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : Tuple = Path(_A) / 'preprocessor_config.json'
__snake_case : Dict = Path(_A) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(_A , 'w') , )
json.dump({'model_type': 'clip'} , open(_A , 'w'))
__snake_case : Tuple = CustomImageProcessor.from_pretrained(_A)
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_A)
__snake_case : Tuple = AutoImageProcessor.from_pretrained(_A)
self.assertIsInstance(_A , _A)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _lowercase (self : List[Any]) -> Tuple:
class UpperCamelCase ( lowercase ):
UpperCAmelCase : str = True
try:
AutoConfig.register('custom' , _A)
AutoImageProcessor.register(_A , _A)
# If remote code is not set, the default is to use local
__snake_case : Tuple = AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor')
self.assertTrue(image_processor.is_local)
# If remote code is disabled, we load the local one.
__snake_case : Optional[int] = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_A)
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor')
self.assertTrue(image_processor.is_local)
# If remote is enabled, we load from the Hub
__snake_case : List[Any] = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_A)
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor')
self.assertTrue(not hasattr(_A , 'is_local'))
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 95 | 1 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case ) -> Dict:
"""simple docstring"""
_lowercase =0
if start < end:
_lowercase =randint(__snake_case , __snake_case )
_lowercase =a[end]
_lowercase =a[pivot]
_lowercase =temp
_lowercase , _lowercase =_in_place_partition(__snake_case , __snake_case , __snake_case )
count += _in_place_quick_sort(__snake_case , __snake_case , p - 1 )
count += _in_place_quick_sort(__snake_case , p + 1 , __snake_case )
return count
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case ) -> Union[str, Any]:
"""simple docstring"""
_lowercase =0
_lowercase =randint(__snake_case , __snake_case )
_lowercase =a[end]
_lowercase =a[pivot]
_lowercase =temp
_lowercase =start - 1
for index in range(__snake_case , __snake_case ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
_lowercase =new_pivot_index + 1
_lowercase =a[new_pivot_index]
_lowercase =a[index]
_lowercase =temp
_lowercase =a[new_pivot_index + 1]
_lowercase =a[end]
_lowercase =temp
return new_pivot_index + 1, count
UpperCAmelCase__ = TemporaryFile()
UpperCAmelCase__ = 100 # 1000 elements are to be sorted
UpperCAmelCase__ ,UpperCAmelCase__ = 0, 1 # mean and standard deviation
UpperCAmelCase__ = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)
outfile.seek(0) # using the same array
UpperCAmelCase__ = np.load(outfile)
UpperCAmelCase__ = len(M) - 1
UpperCAmelCase__ = _in_place_quick_sort(M, 0, r)
print(
'''No of Comparisons for 100 elements selected from a standard normal distribution'''
'''is :'''
)
print(z)
| 5 |
UpperCAmelCase__ = 8.31_44_62 # Unit - J mol-1 K-1
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 5 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''caidas/swin2sr-classicalsr-x2-64''': (
'''https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'''
),
}
class __snake_case ( A__):
snake_case__ : Dict = "swin2sr"
snake_case__ : Tuple = {
"hidden_size": "embed_dim",
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : int , __lowerCAmelCase : str=6_4 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Optional[Any]=3 , __lowerCAmelCase : Optional[int]=1_8_0 , __lowerCAmelCase : Dict=[6, 6, 6, 6, 6, 6] , __lowerCAmelCase : Tuple=[6, 6, 6, 6, 6, 6] , __lowerCAmelCase : Dict=8 , __lowerCAmelCase : str=2.0 , __lowerCAmelCase : Any=True , __lowerCAmelCase : List[Any]=0.0 , __lowerCAmelCase : List[Any]=0.0 , __lowerCAmelCase : Any=0.1 , __lowerCAmelCase : Tuple="gelu" , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : Any=0.02 , __lowerCAmelCase : int=1E-5 , __lowerCAmelCase : Dict=2 , __lowerCAmelCase : Tuple=1.0 , __lowerCAmelCase : Dict="1conv" , __lowerCAmelCase : Dict="pixelshuffle" , **__lowerCAmelCase : Dict , ):
"""simple docstring"""
super().__init__(**__A )
_lowerCamelCase : List[str] = image_size
_lowerCamelCase : Union[str, Any] = patch_size
_lowerCamelCase : Dict = num_channels
_lowerCamelCase : Any = embed_dim
_lowerCamelCase : List[Any] = depths
_lowerCamelCase : Tuple = len(__A )
_lowerCamelCase : int = num_heads
_lowerCamelCase : Optional[int] = window_size
_lowerCamelCase : str = mlp_ratio
_lowerCamelCase : List[Any] = qkv_bias
_lowerCamelCase : Tuple = hidden_dropout_prob
_lowerCamelCase : str = attention_probs_dropout_prob
_lowerCamelCase : Optional[int] = drop_path_rate
_lowerCamelCase : Tuple = hidden_act
_lowerCamelCase : int = use_absolute_embeddings
_lowerCamelCase : Dict = layer_norm_eps
_lowerCamelCase : Optional[Any] = initializer_range
_lowerCamelCase : List[str] = upscale
_lowerCamelCase : Union[str, Any] = img_range
_lowerCamelCase : int = resi_connection
_lowerCamelCase : Dict = upsampler
| 351 |
"""simple docstring"""
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowerCAmelCase__ = logging.getLogger(__name__)
class __snake_case ( _lowercase):
def __init__( self : Union[str, Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : int=None ):
"""simple docstring"""
super().__init__(
__lowerCAmelCase , question_encoder_tokenizer=__lowerCAmelCase , generator_tokenizer=__lowerCAmelCase , index=__lowerCAmelCase , init_retrieval=__lowerCAmelCase , )
_lowerCamelCase : Dict = None
def SCREAMING_SNAKE_CASE ( self : Dict , __lowerCAmelCase : int ):
"""simple docstring"""
logger.info('''initializing retrieval''' )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info('''dist initialized''' )
# needs to be set manually
_lowerCamelCase : List[str] = self._infer_socket_ifname()
# avoid clash with the NCCL port
_lowerCamelCase : Dict = str(distributed_port + 1 )
_lowerCamelCase : str = dist.new_group(ranks=__lowerCAmelCase , backend='''gloo''' )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info('''dist not initialized / main''' )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
"""simple docstring"""
return dist.get_rank(group=self.process_group ) == 0
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple=torch.floataa ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = torch.empty(__lowerCAmelCase , dtype=__lowerCAmelCase )
dist.scatter(__lowerCAmelCase , src=0 , scatter_list=__lowerCAmelCase , group=self.process_group )
return target_tensor
def SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
_lowerCamelCase : List[str] = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
_lowerCamelCase : str = next((addr for addr in addrs if addr.startswith('''e''' )) , __lowerCAmelCase )
return ifname
def SCREAMING_SNAKE_CASE ( self : str , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : int ):
"""simple docstring"""
if not dist.is_initialized():
_lowerCamelCase , _lowerCamelCase : Any = self._main_retrieve(__lowerCAmelCase , __lowerCAmelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCAmelCase )
# distributed training
_lowerCamelCase : Dict = dist.get_world_size(group=self.process_group )
# gather logic
_lowerCamelCase : str = None
if self._is_main():
_lowerCamelCase : List[Any] = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(__lowerCAmelCase )]
dist.gather(torch.tensor(__lowerCAmelCase ) , dst=0 , gather_list=__lowerCAmelCase , group=self.process_group )
# scatter logic
_lowerCamelCase : int = question_hidden_states.shape[0]
_lowerCamelCase : str = []
_lowerCamelCase : Optional[int] = []
if self._is_main():
assert len(__lowerCAmelCase ) == world_size
_lowerCamelCase , _lowerCamelCase : Tuple = self._main_retrieve(torch.cat(__lowerCAmelCase ).numpy() , __lowerCAmelCase )
_lowerCamelCase , _lowerCamelCase : List[str] = torch.tensor(__lowerCAmelCase ), torch.tensor(__lowerCAmelCase )
_lowerCamelCase : List[Any] = self._chunk_tensor(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : Optional[Any] = self._chunk_tensor(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : int = self._scattered(__lowerCAmelCase , [n_queries, n_docs] , target_type=torch.intaa )
_lowerCamelCase : str = self._scattered(__lowerCAmelCase , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__lowerCAmelCase )
| 175 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __UpperCamelCase ( a__ , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
@property
def __a ( self ) -> Optional[int]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __a ( self ) -> Any:
a : Union[str, Any] = ort.SessionOptions()
a : Any = False
return options
def __a ( self ) -> Dict:
a : List[str] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
a : List[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
a : int = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
a : Optional[int] = "A red cat sitting on a park bench"
a : Dict = np.random.RandomState(0 )
a : str = pipe(
prompt=lowerCAmelCase__ , image=lowerCAmelCase__ , mask_image=lowerCAmelCase__ , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCAmelCase__ , output_type="np" , )
a : Union[str, Any] = output.images
a : str = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
a : int = np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def __a ( self ) -> Tuple:
a : Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
a : Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
a : Dict = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
a : Union[str, Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=lowerCAmelCase__ , safety_checker=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
a : List[Any] = "A red cat sitting on a park bench"
a : List[Any] = np.random.RandomState(0 )
a : List[Any] = pipe(
prompt=lowerCAmelCase__ , image=lowerCAmelCase__ , mask_image=lowerCAmelCase__ , guidance_scale=7.5 , num_inference_steps=20 , generator=lowerCAmelCase__ , output_type="np" , )
a : Any = output.images
a : List[Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
a : Any = np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 105 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''ReformerTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''ReformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ReformerAttention''',
'''ReformerForMaskedLM''',
'''ReformerForQuestionAnswering''',
'''ReformerForSequenceClassification''',
'''ReformerLayer''',
'''ReformerModel''',
'''ReformerModelWithLMHead''',
'''ReformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 72 | 0 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if "model" in orig_key:
snake_case_ = orig_key.replace('model.' , '' )
if "norm1" in orig_key:
snake_case_ = orig_key.replace('norm1' , 'attention.output.LayerNorm' )
if "norm2" in orig_key:
snake_case_ = orig_key.replace('norm2' , 'output.LayerNorm' )
if "norm" in orig_key:
snake_case_ = orig_key.replace('norm' , 'LayerNorm' )
if "transformer" in orig_key:
snake_case_ = orig_key.split('.' )[0].split('_' )[-1]
snake_case_ = orig_key.replace(F'''transformer_{layer_num}''' , F'''encoder.layer.{layer_num}''' )
if "mha.attn" in orig_key:
snake_case_ = orig_key.replace('mha.attn' , 'attention.self' )
if "mha" in orig_key:
snake_case_ = orig_key.replace('mha' , 'attention' )
if "W_q" in orig_key:
snake_case_ = orig_key.replace('W_q' , 'self.query' )
if "W_k" in orig_key:
snake_case_ = orig_key.replace('W_k' , 'self.key' )
if "W_v" in orig_key:
snake_case_ = orig_key.replace('W_v' , 'self.value' )
if "ff1" in orig_key:
snake_case_ = orig_key.replace('ff1' , 'intermediate.dense' )
if "ff2" in orig_key:
snake_case_ = orig_key.replace('ff2' , 'output.dense' )
if "ff" in orig_key:
snake_case_ = orig_key.replace('ff' , 'output.dense' )
if "mlm_class" in orig_key:
snake_case_ = orig_key.replace('mlm.mlm_class' , 'cls.predictions.decoder' )
if "mlm" in orig_key:
snake_case_ = orig_key.replace('mlm' , 'cls.predictions.transform' )
if "cls" not in orig_key:
snake_case_ = 'yoso.' + orig_key
return orig_key
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
snake_case_ = orig_state_dict.pop(UpperCamelCase__ )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
snake_case_ = val
snake_case_ = orig_state_dict['cls.predictions.decoder.bias']
snake_case_ = torch.arange(UpperCamelCase__ ).expand((1, -1) ) + 2
return orig_state_dict
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = torch.load(UpperCamelCase__ , map_location='cpu' )['model_state_dict']
snake_case_ = YosoConfig.from_json_file(UpperCamelCase__ )
snake_case_ = YosoForMaskedLM(UpperCamelCase__ )
snake_case_ = convert_checkpoint_helper(config.max_position_embeddings , UpperCamelCase__ )
print(model.load_state_dict(UpperCamelCase__ ) )
model.eval()
model.save_pretrained(UpperCamelCase__ )
print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
if __name__ == "__main__":
_UpperCAmelCase : Any = 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 : str = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 200 |
from __future__ import annotations
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None ):
'''simple docstring'''
if start is None:
snake_case_ = 0
if end is None:
snake_case_ = len(UpperCamelCase__ ) - 1
if start >= end:
return
snake_case_ = (start + end) // 2
slowsort(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
slowsort(UpperCamelCase__ , mid + 1 , UpperCamelCase__ )
if sequence[end] < sequence[mid]:
snake_case_ , snake_case_ = sequence[mid], sequence[end]
slowsort(UpperCamelCase__ , UpperCamelCase__ , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 200 | 1 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : str = 1 / sqrt(2 ) ):
'''simple docstring'''
lowerCAmelCase = tau * frequency / samplerate
lowerCAmelCase = sin(SCREAMING_SNAKE_CASE )
lowerCAmelCase = cos(SCREAMING_SNAKE_CASE )
lowerCAmelCase = _sin / (2 * q_factor)
lowerCAmelCase = (1 - _cos) / 2
lowerCAmelCase = 1 - _cos
lowerCAmelCase = 1 + alpha
lowerCAmelCase = -2 * _cos
lowerCAmelCase = 1 - alpha
lowerCAmelCase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : List[Any] = 1 / sqrt(2 ) ):
'''simple docstring'''
lowerCAmelCase = tau * frequency / samplerate
lowerCAmelCase = sin(SCREAMING_SNAKE_CASE )
lowerCAmelCase = cos(SCREAMING_SNAKE_CASE )
lowerCAmelCase = _sin / (2 * q_factor)
lowerCAmelCase = (1 + _cos) / 2
lowerCAmelCase = -1 - _cos
lowerCAmelCase = 1 + alpha
lowerCAmelCase = -2 * _cos
lowerCAmelCase = 1 - alpha
lowerCAmelCase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : List[str] = 1 / sqrt(2 ) ):
'''simple docstring'''
lowerCAmelCase = tau * frequency / samplerate
lowerCAmelCase = sin(SCREAMING_SNAKE_CASE )
lowerCAmelCase = cos(SCREAMING_SNAKE_CASE )
lowerCAmelCase = _sin / (2 * q_factor)
lowerCAmelCase = _sin / 2
lowerCAmelCase = 0
lowerCAmelCase = -ba
lowerCAmelCase = 1 + alpha
lowerCAmelCase = -2 * _cos
lowerCAmelCase = 1 - alpha
lowerCAmelCase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int = 1 / sqrt(2 ) ):
'''simple docstring'''
lowerCAmelCase = tau * frequency / samplerate
lowerCAmelCase = sin(SCREAMING_SNAKE_CASE )
lowerCAmelCase = cos(SCREAMING_SNAKE_CASE )
lowerCAmelCase = _sin / (2 * q_factor)
lowerCAmelCase = 1 - alpha
lowerCAmelCase = -2 * _cos
lowerCAmelCase = 1 + alpha
lowerCAmelCase = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Any = 1 / sqrt(2 ) , ):
'''simple docstring'''
lowerCAmelCase = tau * frequency / samplerate
lowerCAmelCase = sin(SCREAMING_SNAKE_CASE )
lowerCAmelCase = cos(SCREAMING_SNAKE_CASE )
lowerCAmelCase = _sin / (2 * q_factor)
lowerCAmelCase = 10 ** (gain_db / 40)
lowerCAmelCase = 1 + alpha * big_a
lowerCAmelCase = -2 * _cos
lowerCAmelCase = 1 - alpha * big_a
lowerCAmelCase = 1 + alpha / big_a
lowerCAmelCase = -2 * _cos
lowerCAmelCase = 1 - alpha / big_a
lowerCAmelCase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Optional[int] = 1 / sqrt(2 ) , ):
'''simple docstring'''
lowerCAmelCase = tau * frequency / samplerate
lowerCAmelCase = sin(SCREAMING_SNAKE_CASE )
lowerCAmelCase = cos(SCREAMING_SNAKE_CASE )
lowerCAmelCase = _sin / (2 * q_factor)
lowerCAmelCase = 10 ** (gain_db / 40)
lowerCAmelCase = (big_a + 1) - (big_a - 1) * _cos
lowerCAmelCase = (big_a + 1) + (big_a - 1) * _cos
lowerCAmelCase = (big_a - 1) - (big_a + 1) * _cos
lowerCAmelCase = (big_a - 1) + (big_a + 1) * _cos
lowerCAmelCase = 2 * sqrt(SCREAMING_SNAKE_CASE ) * alpha
lowerCAmelCase = big_a * (pmc + aaa)
lowerCAmelCase = 2 * big_a * mpc
lowerCAmelCase = big_a * (pmc - aaa)
lowerCAmelCase = ppmc + aaa
lowerCAmelCase = -2 * pmpc
lowerCAmelCase = ppmc - aaa
lowerCAmelCase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Tuple = 1 / sqrt(2 ) , ):
'''simple docstring'''
lowerCAmelCase = tau * frequency / samplerate
lowerCAmelCase = sin(SCREAMING_SNAKE_CASE )
lowerCAmelCase = cos(SCREAMING_SNAKE_CASE )
lowerCAmelCase = _sin / (2 * q_factor)
lowerCAmelCase = 10 ** (gain_db / 40)
lowerCAmelCase = (big_a + 1) - (big_a - 1) * _cos
lowerCAmelCase = (big_a + 1) + (big_a - 1) * _cos
lowerCAmelCase = (big_a - 1) - (big_a + 1) * _cos
lowerCAmelCase = (big_a - 1) + (big_a + 1) * _cos
lowerCAmelCase = 2 * sqrt(SCREAMING_SNAKE_CASE ) * alpha
lowerCAmelCase = big_a * (ppmc + aaa)
lowerCAmelCase = -2 * big_a * pmpc
lowerCAmelCase = big_a * (ppmc - aaa)
lowerCAmelCase = pmc + aaa
lowerCAmelCase = 2 * mpc
lowerCAmelCase = pmc - aaa
lowerCAmelCase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 46 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_snake_case = logging.getLogger(__name__)
def _A ( snake_case , snake_case ) -> List[Any]:
# save results
if os.path.exists(snake_case ):
if os.path.exists(os.path.join(snake_case , "config.json" ) ) and os.path.isfile(
os.path.join(snake_case , "config.json" ) ):
os.remove(os.path.join(snake_case , "config.json" ) )
if os.path.exists(os.path.join(snake_case , "pytorch_model.bin" ) ) and os.path.isfile(
os.path.join(snake_case , "pytorch_model.bin" ) ):
os.remove(os.path.join(snake_case , "pytorch_model.bin" ) )
else:
os.makedirs(snake_case )
model.save_pretrained(snake_case )
def _A ( snake_case , snake_case=False ) -> int:
_lowercase : Union[str, Any] = 2
if unlogit:
_lowercase : Optional[Any] = torch.pow(snake_case , snake_case )
_lowercase : List[Any] = p * torch.log(snake_case )
_lowercase : str = 0
return -plogp.sum(dim=-1 )
def _A ( snake_case ) -> List[Any]:
logger.info("lv, h >\t" + "\t".join(F'''{x + 1}''' for x in range(len(snake_case ) ) ) )
for row in range(len(snake_case ) ):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + "\t".join(F'''{x:.5f}''' for x in tensor[row].cpu().data ) )
else:
logger.info(F'''layer {row + 1}:\t''' + "\t".join(F'''{x:d}''' for x in tensor[row].cpu().data ) )
def _A ( snake_case , snake_case , snake_case , snake_case=True , snake_case=True , snake_case=None , snake_case=False ) -> Optional[int]:
_lowercase , _lowercase : Union[str, Any] = model.config.num_hidden_layers, model.config.num_attention_heads
_lowercase : Optional[int] = torch.zeros(snake_case , snake_case ).to(args.device )
_lowercase : str = torch.zeros(snake_case , snake_case ).to(args.device )
if head_mask is None:
_lowercase : Any = torch.ones(snake_case , snake_case ).to(args.device )
head_mask.requires_grad_(requires_grad=snake_case )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
_lowercase : int = None
_lowercase : List[str] = 0.0
_lowercase : str = 0.0
for step, inputs in enumerate(tqdm(snake_case , desc="Iteration" , disable=args.local_rank not in [-1, 0] ) ):
_lowercase : Dict = tuple(t.to(args.device ) for t in inputs )
((_lowercase) , ) : Any = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
_lowercase : str = model(snake_case , labels=snake_case , head_mask=snake_case )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
_lowercase , _lowercase , _lowercase : Optional[int] = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(snake_case ):
_lowercase : Optional[int] = entropy(attn.detach() , snake_case )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(snake_case ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
_lowercase : List[str] = 2
_lowercase : Dict = torch.pow(torch.pow(snake_case , snake_case ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1E-20
if not args.dont_normalize_global_importance:
_lowercase : str = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info("Attention entropies" )
print_ad_tensor(snake_case )
if compute_importance:
logger.info("Head importance scores" )
print_ad_tensor(snake_case )
logger.info("Head ranked by importance scores" )
_lowercase : Any = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
_lowercase : Union[str, Any] = torch.arange(
head_importance.numel() , device=args.device )
_lowercase : Optional[Any] = head_ranks.view_as(snake_case )
print_ad_tensor(snake_case )
return attn_entropy, head_importance, total_loss
def _A ( snake_case , snake_case , snake_case ) -> Optional[Any]:
_lowercase , _lowercase , _lowercase : Union[str, Any] = compute_heads_importance(snake_case , snake_case , snake_case , compute_entropy=snake_case )
_lowercase : int = 1 / loss # instead of downsteam score use the LM loss
logger.info("Pruning: original score: %f, threshold: %f" , snake_case , original_score * args.masking_threshold )
_lowercase : List[Any] = torch.ones_like(snake_case )
_lowercase : Dict = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
_lowercase : Union[str, Any] = original_score
while current_score >= original_score * args.masking_threshold:
_lowercase : Any = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
_lowercase : Dict = float("Inf" )
_lowercase : Union[str, Any] = head_importance.view(-1 ).sort()[1]
if len(snake_case ) <= num_to_mask:
print("BREAK BY num_to_mask" )
break
# mask heads
_lowercase : List[str] = current_heads_to_mask[:num_to_mask]
logger.info("Heads to mask: %s" , str(current_heads_to_mask.tolist() ) )
_lowercase : int = new_head_mask.view(-1 )
_lowercase : Union[str, Any] = 0.0
_lowercase : Dict = new_head_mask.view_as(snake_case )
_lowercase : str = new_head_mask.clone().detach()
print_ad_tensor(snake_case )
# Compute metric and head importance again
_lowercase , _lowercase , _lowercase : Any = compute_heads_importance(
snake_case , snake_case , snake_case , compute_entropy=snake_case , head_mask=snake_case )
_lowercase : str = 1 / loss
logger.info(
"Masking: current score: %f, remaining heads %d (%.1f percents)" , snake_case , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 1_00 , )
logger.info("Final head mask" )
print_ad_tensor(snake_case )
np.save(os.path.join(args.output_dir , "head_mask.npy" ) , head_mask.detach().cpu().numpy() )
return head_mask
def _A ( snake_case , snake_case , snake_case , snake_case ) -> Any:
_lowercase : List[Any] = datetime.now()
_lowercase , _lowercase , _lowercase : List[Any] = compute_heads_importance(
snake_case , snake_case , snake_case , compute_entropy=snake_case , compute_importance=snake_case , head_mask=snake_case )
_lowercase : Tuple = 1 / loss
_lowercase : List[Any] = datetime.now() - before_time
_lowercase : int = sum(p.numel() for p in model.parameters() )
_lowercase : str = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(snake_case ) )
}
for k, v in heads_to_prune.items():
if isinstance(snake_case , snake_case ):
_lowercase : Optional[Any] = [
v,
]
assert sum(len(snake_case ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(snake_case )
_lowercase : List[str] = sum(p.numel() for p in model.parameters() )
_lowercase : int = datetime.now()
_lowercase , _lowercase , _lowercase : Any = compute_heads_importance(
snake_case , snake_case , snake_case , compute_entropy=snake_case , compute_importance=snake_case , head_mask=snake_case , actually_pruned=snake_case , )
_lowercase : List[Any] = 1 / loss
_lowercase : int = datetime.now() - before_time
logger.info(
"Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)" , snake_case , snake_case , pruned_num_params / original_num_params * 1_00 , )
logger.info("Pruning: score with masking: %f score with pruning: %f" , snake_case , snake_case )
logger.info("Pruning: speed ratio (original timing / new timing): %f percents" , original_time / new_time * 1_00 )
save_model(snake_case , args.output_dir )
def _A ( ) -> int:
_lowercase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir" , default=snake_case , type=snake_case , required=snake_case , help="The input data dir. Should contain the .tsv files (or other data files) for the task." , )
parser.add_argument(
"--model_name_or_path" , default=snake_case , type=snake_case , required=snake_case , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--output_dir" , default=snake_case , type=snake_case , required=snake_case , help="The output directory where the model predictions and checkpoints will be written." , )
# Other parameters
parser.add_argument(
"--config_name" , default="" , type=snake_case , help="Pretrained config name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--tokenizer_name" , default="" , type=snake_case , help="Pretrained tokenizer name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--cache_dir" , default=snake_case , type=snake_case , help="Where do you want to store the pre-trained models downloaded from s3" , )
parser.add_argument(
"--data_subset" , type=snake_case , default=-1 , help="If > 0: limit the data to a subset of data_subset instances." )
parser.add_argument(
"--overwrite_output_dir" , action="store_true" , help="Whether to overwrite data in output directory" )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" )
parser.add_argument(
"--dont_normalize_importance_by_layer" , action="store_true" , help="Don't normalize importance score by layers" )
parser.add_argument(
"--dont_normalize_global_importance" , action="store_true" , help="Don't normalize all importance scores between 0 and 1" , )
parser.add_argument(
"--try_masking" , action="store_true" , help="Whether to try to mask head until a threshold of accuracy." )
parser.add_argument(
"--masking_threshold" , default=0.9 , type=snake_case , help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)." , )
parser.add_argument(
"--masking_amount" , default=0.1 , type=snake_case , help="Amount to heads to masking at each masking step." )
parser.add_argument("--metric_name" , default="acc" , type=snake_case , help="Metric to use for head masking." )
parser.add_argument(
"--max_seq_length" , default=1_28 , type=snake_case , help=(
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, sequences shorter padded."
) , )
parser.add_argument("--batch_size" , default=1 , type=snake_case , help="Batch size." )
parser.add_argument("--seed" , type=snake_case , default=42 )
parser.add_argument("--local_rank" , type=snake_case , default=-1 , help="local_rank for distributed training on gpus" )
parser.add_argument("--no_cuda" , action="store_true" , help="Whether not to use CUDA when available" )
parser.add_argument("--server_ip" , type=snake_case , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=snake_case , default="" , help="Can be used for distant debugging." )
_lowercase : Optional[int] = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=snake_case )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
_lowercase : Any = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
_lowercase : Optional[int] = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
_lowercase : List[Any] = torch.device("cuda" , args.local_rank )
_lowercase : Dict = 1
torch.distributed.init_process_group(backend="nccl" ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
_lowercase : List[Any] = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
_lowercase : str = nn.parallel.DistributedDataParallel(
snake_case , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=snake_case )
elif args.n_gpu > 1:
_lowercase : Dict = nn.DataParallel(snake_case )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=snake_case )
torch.save(snake_case , os.path.join(args.output_dir , "run_args.bin" ) )
logger.info("Training/evaluation parameters %s" , snake_case )
# Prepare dataset
_lowercase : Optional[Any] = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
_lowercase : List[str] = (torch.from_numpy(snake_case ),)
_lowercase : Dict = TensorDataset(*snake_case )
_lowercase : List[Any] = RandomSampler(snake_case )
_lowercase : str = DataLoader(snake_case , sampler=snake_case , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(snake_case , snake_case , snake_case )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
_lowercase : int = mask_heads(snake_case , snake_case , snake_case )
prune_heads(snake_case , snake_case , snake_case , snake_case )
if __name__ == "__main__":
main()
| 250 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowercase__ = logging.get_logger(__name__)
lowercase__ = Dict[str, Any]
lowercase__ = List[Prediction]
@add_end_docstrings(__SCREAMING_SNAKE_CASE )
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self : int , *UpperCamelCase__ : Optional[Any] , **UpperCamelCase__ : List[Any] ) -> Any:
"""simple docstring"""
super().__init__(*UpperCamelCase__ , **UpperCamelCase__ )
if self.framework == "tf":
raise ValueError(f'The {self.__class__} is only available in PyTorch.' )
requires_backends(self , '''vision''' )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def lowerCAmelCase ( self : int , **UpperCamelCase__ : str ) -> List[Any]:
"""simple docstring"""
snake_case : Any = {}
if "threshold" in kwargs:
snake_case : str = kwargs['''threshold''']
return {}, {}, postprocess_kwargs
def __call__( self : str , *UpperCamelCase__ : Tuple , **UpperCamelCase__ : Tuple ) -> Union[Predictions, List[Prediction]]:
"""simple docstring"""
return super().__call__(*UpperCamelCase__ , **UpperCamelCase__ )
def lowerCAmelCase ( self : List[str] , UpperCamelCase__ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
snake_case : Dict = load_image(UpperCamelCase__ )
snake_case : Dict = torch.IntTensor([[image.height, image.width]] )
snake_case : Any = self.image_processor(images=[image] , return_tensors='''pt''' )
if self.tokenizer is not None:
snake_case : int = self.tokenizer(text=inputs['''words'''] , boxes=inputs['''boxes'''] , return_tensors='''pt''' )
snake_case : Optional[int] = target_size
return inputs
def lowerCAmelCase ( self : List[str] , UpperCamelCase__ : List[Any] ) -> int:
"""simple docstring"""
snake_case : Optional[Any] = model_inputs.pop('''target_size''' )
snake_case : Dict = self.model(**UpperCamelCase__ )
snake_case : Union[str, Any] = outputs.__class__({'''target_size''': target_size, **outputs} )
if self.tokenizer is not None:
snake_case : List[Any] = model_inputs['''bbox''']
return model_outputs
def lowerCAmelCase ( self : Any , UpperCamelCase__ : Dict , UpperCamelCase__ : int=0.9 ) -> List[str]:
"""simple docstring"""
snake_case : Dict = model_outputs['''target_size''']
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
snake_case : Any = target_size[0].tolist()
def unnormalize(UpperCamelCase__ : Optional[Any] ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1000),
(height * bbox[1] / 1000),
(width * bbox[2] / 1000),
(height * bbox[3] / 1000),
] ) )
snake_case : List[Any] = model_outputs['''logits'''].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
snake_case : Any = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
snake_case : Tuple = [unnormalize(UpperCamelCase__ ) for bbox in model_outputs['''bbox'''].squeeze(0 )]
snake_case : Optional[int] = ['''score''', '''label''', '''box''']
snake_case : List[str] = [dict(zip(UpperCamelCase__ , UpperCamelCase__ ) ) for vals in zip(scores.tolist() , UpperCamelCase__ , UpperCamelCase__ ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
snake_case : Optional[Any] = self.image_processor.post_process_object_detection(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
snake_case : Dict = raw_annotations[0]
snake_case : Dict = raw_annotation['''scores''']
snake_case : Dict = raw_annotation['''labels''']
snake_case : List[Any] = raw_annotation['''boxes''']
snake_case : List[str] = scores.tolist()
snake_case : Optional[int] = [self.model.config.idalabel[label.item()] for label in labels]
snake_case : Union[str, Any] = [self._get_bounding_box(UpperCamelCase__ ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
snake_case : Dict = ['''score''', '''label''', '''box''']
snake_case : List[str] = [
dict(zip(UpperCamelCase__ , UpperCamelCase__ ) )
for vals in zip(raw_annotation['''scores'''] , raw_annotation['''labels'''] , raw_annotation['''boxes'''] )
]
return annotation
def lowerCAmelCase ( self : Optional[Any] , UpperCamelCase__ : "torch.Tensor" ) -> Dict[str, int]:
"""simple docstring"""
if self.framework != "pt":
raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.''' )
snake_case : Optional[Any] = box.int().tolist()
snake_case : Dict = {
'''xmin''': xmin,
'''ymin''': ymin,
'''xmax''': xmax,
'''ymax''': ymax,
}
return bbox
| 354 |
'''simple docstring'''
import requests
lowercase__ = "" # <-- Put your OpenWeatherMap appid here!
lowercase__ = "https://api.openweathermap.org/data/2.5/"
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = "Chicago" , SCREAMING_SNAKE_CASE__ = APPID ) -> dict:
'''simple docstring'''
return requests.get(URL_BASE + '''weather''' , params=locals() ).json()
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = "Kolkata, India" , SCREAMING_SNAKE_CASE__ = APPID ) -> dict:
'''simple docstring'''
return requests.get(URL_BASE + '''forecast''' , params=locals() ).json()
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 55.68 , SCREAMING_SNAKE_CASE__ = 12.57 , SCREAMING_SNAKE_CASE__ = APPID ) -> dict:
'''simple docstring'''
return requests.get(URL_BASE + '''onecall''' , params=locals() ).json()
if __name__ == "__main__":
from pprint import pprint
while True:
lowercase__ = input("Enter a location:").strip()
if location:
pprint(current_weather(location))
else:
break
| 83 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
A__ : Tuple = (7_20, 12_80) # Height, Width
A__ : int = (0.4, 0.6) # if height or width lower than this scale, drop it.
A__ : int = 1 / 1_00
A__ : Any = """"""
A__ : List[str] = """"""
A__ : List[Any] = """"""
A__ : Tuple = 2_50
def a ( ):
'''simple docstring'''
lowercase__ = get_dataset(_lowerCAmelCase , _lowerCAmelCase )
for index in range(_lowerCAmelCase ):
lowercase__ = random.sample(range(len(_lowerCAmelCase ) ) , 4 )
lowercase__ = update_image_and_anno(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , filter_scale=_lowerCAmelCase , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
lowercase__ = random_chars(32 )
lowercase__ = path.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
lowercase__ = F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
cva.imwrite(F"""{file_root}.jpg""" , _lowerCAmelCase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
lowercase__ = []
for anno in new_annos:
lowercase__ = anno[3] - anno[1]
lowercase__ = anno[4] - anno[2]
lowercase__ = anno[1] + width / 2
lowercase__ = anno[2] + height / 2
lowercase__ = F"""{anno[0]} {x_center} {y_center} {width} {height}"""
annos_list.append(_lowerCAmelCase )
with open(F"""{file_root}.txt""" , '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = []
lowercase__ = []
for label_file in glob.glob(os.path.join(_lowerCAmelCase , '''*.txt''' ) ):
lowercase__ = label_file.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
with open(_lowerCAmelCase ) as in_file:
lowercase__ = in_file.readlines()
lowercase__ = os.path.join(_lowerCAmelCase , F"""{label_name}.jpg""" )
lowercase__ = []
for obj_list in obj_lists:
lowercase__ = obj_list.rstrip('''\n''' ).split(''' ''' )
lowercase__ = float(obj[1] ) - float(obj[3] ) / 2
lowercase__ = float(obj[2] ) - float(obj[4] ) / 2
lowercase__ = float(obj[1] ) + float(obj[3] ) / 2
lowercase__ = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(_lowerCAmelCase )
labels.append(_lowerCAmelCase )
return img_paths, labels
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 0.0 , ):
'''simple docstring'''
lowercase__ = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
lowercase__ = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
lowercase__ = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
lowercase__ = int(scale_x * output_size[1] )
lowercase__ = int(scale_y * output_size[0] )
lowercase__ = []
lowercase__ = []
for i, index in enumerate(_lowerCAmelCase ):
lowercase__ = all_img_list[index]
path_list.append(_lowerCAmelCase )
lowercase__ = all_annos[index]
lowercase__ = cva.imread(_lowerCAmelCase )
if i == 0: # top-left
lowercase__ = cva.resize(_lowerCAmelCase , (divid_point_x, divid_point_y) )
lowercase__ = img
for bbox in img_annos:
lowercase__ = bbox[1] * scale_x
lowercase__ = bbox[2] * scale_y
lowercase__ = bbox[3] * scale_x
lowercase__ = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
lowercase__ = cva.resize(_lowerCAmelCase , (output_size[1] - divid_point_x, divid_point_y) )
lowercase__ = img
for bbox in img_annos:
lowercase__ = scale_x + bbox[1] * (1 - scale_x)
lowercase__ = bbox[2] * scale_y
lowercase__ = scale_x + bbox[3] * (1 - scale_x)
lowercase__ = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
lowercase__ = cva.resize(_lowerCAmelCase , (divid_point_x, output_size[0] - divid_point_y) )
lowercase__ = img
for bbox in img_annos:
lowercase__ = bbox[1] * scale_x
lowercase__ = scale_y + bbox[2] * (1 - scale_y)
lowercase__ = bbox[3] * scale_x
lowercase__ = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
lowercase__ = cva.resize(
_lowerCAmelCase , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
lowercase__ = img
for bbox in img_annos:
lowercase__ = scale_x + bbox[1] * (1 - scale_x)
lowercase__ = scale_y + bbox[2] * (1 - scale_y)
lowercase__ = scale_x + bbox[3] * (1 - scale_x)
lowercase__ = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
lowercase__ = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def a ( lowerCamelCase_ ):
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
lowercase__ = ascii_lowercase + digits
return "".join(random.choice(_lowerCAmelCase ) for _ in range(_lowerCAmelCase ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 207 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class A__ :
def __init__( self , A_ , A_=13 , A_=7 , A_=True , A_=True , A_=True , A_=True , A_=99 , A_=24 , A_=2 , A_=6 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=16 , A_=2 , A_=0.02 , A_=3 , A_=None , A_=1000 , ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : List[Any] = batch_size
UpperCamelCase : Dict = seq_length
UpperCamelCase : Tuple = is_training
UpperCamelCase : Union[str, Any] = use_input_mask
UpperCamelCase : Tuple = use_token_type_ids
UpperCamelCase : Optional[Any] = use_labels
UpperCamelCase : str = vocab_size
UpperCamelCase : Optional[int] = hidden_size
UpperCamelCase : Any = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : Optional[Any] = intermediate_size
UpperCamelCase : Optional[Any] = hidden_act
UpperCamelCase : Union[str, Any] = hidden_dropout_prob
UpperCamelCase : Union[str, Any] = attention_probs_dropout_prob
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : str = type_vocab_size
UpperCamelCase : Optional[int] = type_sequence_label_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : int = num_labels
UpperCamelCase : Optional[int] = scope
UpperCamelCase : int = range_bbox
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Any = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
UpperCamelCase : Union[str, Any] = bbox[i, j, 3]
UpperCamelCase : int = bbox[i, j, 1]
UpperCamelCase : int = t
if bbox[i, j, 2] < bbox[i, j, 0]:
UpperCamelCase : List[str] = bbox[i, j, 2]
UpperCamelCase : Optional[int] = bbox[i, j, 0]
UpperCamelCase : Optional[Any] = t
UpperCamelCase : Dict = None
if self.use_input_mask:
UpperCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
UpperCamelCase : str = None
if self.use_token_type_ids:
UpperCamelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase : Dict = None
UpperCamelCase : int = None
if self.use_labels:
UpperCamelCase : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : List[Any] = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def __UpperCamelCase( self ):
'''simple docstring'''
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ):
'''simple docstring'''
UpperCamelCase : Any = LiltModel(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase : str = model(A_ , bbox=A_ , attention_mask=A_ , token_type_ids=A_ )
UpperCamelCase : Optional[int] = model(A_ , bbox=A_ , token_type_ids=A_ )
UpperCamelCase : Any = model(A_ , bbox=A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ):
'''simple docstring'''
UpperCamelCase : Any = self.num_labels
UpperCamelCase : Dict = LiltForTokenClassification(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase : Dict = model(
A_ , bbox=A_ , attention_mask=A_ , token_type_ids=A_ , labels=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ):
'''simple docstring'''
UpperCamelCase : Dict = LiltForQuestionAnswering(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase : List[str] = model(
A_ , bbox=A_ , attention_mask=A_ , token_type_ids=A_ , start_positions=A_ , end_positions=A_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Any = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : Tuple = config_and_inputs
UpperCamelCase : Tuple = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class A__ ( __snake_case , __snake_case , __snake_case , unittest.TestCase ):
_UpperCAmelCase :Union[str, Any] = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
_UpperCAmelCase :Optional[Any] = (
{
'feature-extraction': LiltModel,
'question-answering': LiltForQuestionAnswering,
'text-classification': LiltForSequenceClassification,
'token-classification': LiltForTokenClassification,
'zero-shot': LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase :Dict = False
_UpperCAmelCase :Union[str, Any] = False
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
return True
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = LiltModelTester(self )
UpperCamelCase : Optional[int] = ConfigTester(self , config_class=A_ , hidden_size=37 )
def __UpperCamelCase( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase : Union[str, Any] = type
self.model_tester.create_and_check_model(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A_ )
@slow
def __UpperCamelCase( self ):
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Dict = LiltModel.from_pretrained(A_ )
self.assertIsNotNone(A_ )
@require_torch
@slow
class A__ ( unittest.TestCase ):
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base" ).to(A_ )
UpperCamelCase : Tuple = torch.tensor([[1, 2]] , device=A_ )
UpperCamelCase : List[str] = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=A_ )
# forward pass
with torch.no_grad():
UpperCamelCase : Optional[int] = model(input_ids=A_ , bbox=A_ )
UpperCamelCase : List[str] = torch.Size([1, 2, 768] )
UpperCamelCase : Any = torch.tensor(
[[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]] , device=A_ , )
self.assertTrue(outputs.last_hidden_state.shape , A_ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , A_ , atol=1e-3 ) )
| 52 | 0 |
'''simple docstring'''
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _A ( ):
"""simple docstring"""
__lowercase = argparse.ArgumentParser()
parser.add_argument(
'''-m''' , '''--pretrained_model_name_or_path''' , type=A__ , default=A__ , required=A__ , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , )
parser.add_argument(
'''-c''' , '''--caption''' , type=A__ , default='''robotic cat with wings''' , help='''Text used to generate images.''' , )
parser.add_argument(
'''-n''' , '''--images_num''' , type=A__ , default=4 , help='''How much images to generate.''' , )
parser.add_argument(
'''-s''' , '''--seed''' , type=A__ , default=42 , help='''Seed for random process.''' , )
parser.add_argument(
'''-ci''' , '''--cuda_id''' , type=A__ , default=0 , help='''cuda_id.''' , )
__lowercase = parser.parse_args()
return args
def _A ( A__ , A__ , A__ ):
"""simple docstring"""
if not len(A__ ) == rows * cols:
raise ValueError('''The specified number of rows and columns are not correct.''' )
__lowercase , __lowercase = imgs[0].size
__lowercase = Image.new('''RGB''' , size=(cols * w, rows * h) )
__lowercase , __lowercase = grid.size
for i, img in enumerate(A__ ):
grid.paste(A__ , box=(i % cols * w, i // cols * h) )
return grid
def _A ( A__ , A__="robotic cat with wings" , A__=7.5 , A__=50 , A__=1 , A__=42 , ):
"""simple docstring"""
__lowercase = torch.Generator(pipeline.device ).manual_seed(A__ )
__lowercase = pipeline(
A__ , guidance_scale=A__ , num_inference_steps=A__ , generator=A__ , num_images_per_prompt=A__ , ).images
__lowercase = int(math.sqrt(A__ ) )
__lowercase = image_grid(A__ , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
lowerCAmelCase__ = parse_args()
# Load models and create wrapper for stable diffusion
lowerCAmelCase__ = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
lowerCAmelCase__ = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
lowerCAmelCase__ = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
lowerCAmelCase__ = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')
lowerCAmelCase__ = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase__ = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
lowerCAmelCase__ = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, '''unet''', unet)
else:
lowerCAmelCase__ = unet.to(torch.device('''cuda''', args.cuda_id))
lowerCAmelCase__ = pipeline.to(unet.device)
lowerCAmelCase__ , lowerCAmelCase__ = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
lowerCAmelCase__ = os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))
| 351 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = 'gpt_neo'
SCREAMING_SNAKE_CASE : Any = ['past_key_values']
SCREAMING_SNAKE_CASE : Union[str, Any] = {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
def __init__( self : Any ,lowercase__ : Tuple=5_0_2_5_7 ,lowercase__ : Union[str, Any]=2_0_4_8 ,lowercase__ : List[Any]=2_0_4_8 ,lowercase__ : Optional[Any]=2_4 ,lowercase__ : Union[str, Any]=[[["global", "local"], 1_2]] ,lowercase__ : List[Any]=1_6 ,lowercase__ : Optional[Any]=None ,lowercase__ : Optional[int]=2_5_6 ,lowercase__ : Union[str, Any]="gelu_new" ,lowercase__ : Tuple=0.0 ,lowercase__ : List[str]=0.0 ,lowercase__ : Dict=0.0 ,lowercase__ : Union[str, Any]=0.1 ,lowercase__ : List[str]=1e-5 ,lowercase__ : Dict=0.0_2 ,lowercase__ : str=True ,lowercase__ : int=5_0_2_5_6 ,lowercase__ : Any=5_0_2_5_6 ,**lowercase__ : Optional[Any] ,):
__lowercase = vocab_size
__lowercase = max_position_embeddings
__lowercase = hidden_size
__lowercase = num_layers
__lowercase = num_heads
__lowercase = intermediate_size
__lowercase = window_size
__lowercase = activation_function
__lowercase = resid_dropout
__lowercase = embed_dropout
__lowercase = attention_dropout
__lowercase = classifier_dropout
__lowercase = layer_norm_epsilon
__lowercase = initializer_range
__lowercase = use_cache
__lowercase = bos_token_id
__lowercase = eos_token_id
__lowercase = attention_types
__lowercase = self.expand_attention_types_params(lowercase__ )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
'''Configuration for convolutional module is incorrect. '''
'''It is required that `len(config.attention_layers)` == `config.num_layers` '''
F"but is `len(config.attention_layers) = {len(self.attention_layers )}`, "
F"`config.num_layers = {self.num_layers}`. "
'''`config.attention_layers` is prepared using `config.attention_types`. '''
'''Please verify the value of `config.attention_types` argument.''' )
super().__init__(bos_token_id=lowercase__ ,eos_token_id=lowercase__ ,**lowercase__ )
@staticmethod
def SCREAMING_SNAKE_CASE ( lowercase__ : Tuple ):
__lowercase = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def _A ( A__ , A__ , A__ , A__ ):
"""simple docstring"""
import torch
__lowercase = input.size()
__lowercase = len(A__ )
__lowercase = shape[dimension]
__lowercase = torch.arange(0 , A__ , A__ )
__lowercase = torch.div(sizedim - size , A__ , rounding_mode='''floor''' ) + 1
__lowercase = torch.arange(A__ ) + low_indices[:min_length][:, None]
__lowercase = [slice(A__ )] * rank
__lowercase = indices
__lowercase = input[s]
__lowercase = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(A__ )
def _A ( A__ , A__ ):
"""simple docstring"""
import torch
__lowercase = torch.arange(1 , A__ )
__lowercase = torch.remainder(A__ , A__ )
__lowercase = remainders == 0
__lowercase = candidates[divisor_indices]
__lowercase = torch.max(A__ )
return largest_divisor, torch.div(A__ , A__ , rounding_mode='''floor''' )
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
@property
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
__lowercase = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(lowercase__ ,direction='''inputs''' )
__lowercase = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
__lowercase = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def SCREAMING_SNAKE_CASE ( self : List[str] ):
return self._config.num_heads
def SCREAMING_SNAKE_CASE ( self : Optional[int] ,lowercase__ : PreTrainedTokenizer ,lowercase__ : int = -1 ,lowercase__ : int = -1 ,lowercase__ : bool = False ,lowercase__ : Optional[TensorType] = None ,):
__lowercase = super(lowercase__ ,self ).generate_dummy_inputs(
lowercase__ ,batch_size=lowercase__ ,seq_length=lowercase__ ,is_pair=lowercase__ ,framework=lowercase__ )
# We need to order the input in the way they appears in the forward()
__lowercase = 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
__lowercase , __lowercase = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
__lowercase = seqlen + 2
__lowercase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__lowercase = [
(torch.zeros(lowercase__ ), torch.zeros(lowercase__ )) for _ in range(self.num_layers )
]
__lowercase = common_inputs['''attention_mask''']
if self.use_past:
__lowercase = ordered_inputs['''attention_mask'''].dtype
__lowercase = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(lowercase__ ,lowercase__ ,dtype=lowercase__ )] ,dim=1 )
return ordered_inputs
@property
def SCREAMING_SNAKE_CASE ( self : Any ):
return 1_3
| 52 | 0 |
'''simple docstring'''
import string
def UpperCAmelCase_ ( __lowerCamelCase : str ):
for key in range(len(string.ascii_uppercase ) ):
lowercase_ :Any = ""
for symbol in message:
if symbol in string.ascii_uppercase:
lowercase_ :Tuple = string.ascii_uppercase.find(__lowerCamelCase )
lowercase_ :Optional[int] = num - key
if num < 0:
lowercase_ :str = num + len(string.ascii_uppercase )
lowercase_ :Any = translated + string.ascii_uppercase[num]
else:
lowercase_ :Any = translated + symbol
print(F'Decryption using Key #{key}: {translated}' )
def UpperCAmelCase_ ( ):
lowercase_ :Optional[Any] = input("Encrypted message: " )
lowercase_ :Union[str, Any] = message.upper()
decrypt(__lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 223 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a_ ( _lowerCAmelCase , unittest.TestCase ):
__A = RobertaTokenizer
__A = RobertaTokenizerFast
__A = True
__A = {"cls_token": "<s>"}
def lowercase__ ( self : Dict ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase_ :List[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
lowercase_ :List[Any] = dict(zip(lowercase , range(len(lowercase ) ) ) )
lowercase_ :Optional[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowercase_ :Union[str, Any] = {"unk_token": "<unk>"}
lowercase_ :Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
lowercase_ :Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(lowercase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowercase ) )
def lowercase__ ( self : str , **lowercase : List[str] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowercase )
def lowercase__ ( self : int , **lowercase : int ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **lowercase )
def lowercase__ ( self : Optional[int] , lowercase : List[Any] ):
"""simple docstring"""
lowercase_ :List[str] = "lower newer"
lowercase_ :Any = "lower newer"
return input_text, output_text
def lowercase__ ( self : int ):
"""simple docstring"""
lowercase_ :List[Any] = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowercase_ :Dict = "lower newer"
lowercase_ :Dict = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
lowercase_ :int = tokenizer.tokenize(lowercase ) # , add_prefix_space=True)
self.assertListEqual(lowercase , lowercase )
lowercase_ :Optional[Any] = tokens + [tokenizer.unk_token]
lowercase_ :Any = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase ) , lowercase )
def lowercase__ ( self : Dict ):
"""simple docstring"""
lowercase_ :Dict = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" , add_special_tokens=lowercase ) , [0, 31_414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" , add_special_tokens=lowercase ) , [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2] , )
@slow
def lowercase__ ( self : List[str] ):
"""simple docstring"""
lowercase_ :Optional[Any] = self.tokenizer_class.from_pretrained("roberta-base" )
lowercase_ :Any = tokenizer.encode("sequence builders" , add_special_tokens=lowercase )
lowercase_ :str = tokenizer.encode("multi-sequence build" , add_special_tokens=lowercase )
lowercase_ :int = tokenizer.encode(
"sequence builders" , add_special_tokens=lowercase , add_prefix_space=lowercase )
lowercase_ :Optional[int] = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=lowercase , add_prefix_space=lowercase )
lowercase_ :str = tokenizer.build_inputs_with_special_tokens(lowercase )
lowercase_ :Optional[int] = tokenizer.build_inputs_with_special_tokens(lowercase , lowercase )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def lowercase__ ( self : Optional[int] ):
"""simple docstring"""
lowercase_ :Optional[int] = self.get_tokenizer()
lowercase_ :str = "Encode this sequence."
lowercase_ :Tuple = tokenizer.byte_encoder[" ".encode("utf-8" )[0]]
# Testing encoder arguments
lowercase_ :List[str] = tokenizer.encode(lowercase , add_special_tokens=lowercase , add_prefix_space=lowercase )
lowercase_ :List[Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(lowercase , lowercase )
lowercase_ :List[str] = tokenizer.encode(lowercase , add_special_tokens=lowercase , add_prefix_space=lowercase )
lowercase_ :List[Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(lowercase , lowercase )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
lowercase_ :List[str] = tokenizer.encode(lowercase , add_special_tokens=lowercase )
lowercase_ :Optional[int] = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(lowercase , lowercase )
# Testing spaces after special tokens
lowercase_ :Union[str, Any] = "<mask>"
tokenizer.add_special_tokens(
{"mask_token": AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase )} ) # mask token has a left space
lowercase_ :Any = tokenizer.convert_tokens_to_ids(lowercase )
lowercase_ :Tuple = "Encode <mask> sequence"
lowercase_ :int = "Encode <mask>sequence"
lowercase_ :str = tokenizer.encode(lowercase )
lowercase_ :Any = encoded.index(lowercase )
lowercase_ :Dict = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(lowercase , lowercase )
lowercase_ :str = tokenizer.encode(lowercase )
lowercase_ :int = encoded.index(lowercase )
lowercase_ :Optional[int] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(lowercase , lowercase )
def lowercase__ ( self : Optional[Any] ):
"""simple docstring"""
pass
def lowercase__ ( self : Dict ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
lowercase_ :List[Any] = self.rust_tokenizer_class.from_pretrained(lowercase , **lowercase )
lowercase_ :Dict = self.tokenizer_class.from_pretrained(lowercase , **lowercase )
lowercase_ :str = "A, <mask> AllenNLP sentence."
lowercase_ :Tuple = tokenizer_r.encode_plus(lowercase , add_special_tokens=lowercase , return_token_type_ids=lowercase )
lowercase_ :str = tokenizer_p.encode_plus(lowercase , add_special_tokens=lowercase , return_token_type_ids=lowercase )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
lowercase_ :Any = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
lowercase_ :Union[str, Any] = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
lowercase , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
lowercase , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def lowercase__ ( self : Dict ):
"""simple docstring"""
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
lowercase_ :List[Any] = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=lowercase , add_prefix_space=lowercase , trim_offsets=lowercase )
lowercase_ :int = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
lowercase_ :Any = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] , lowercase )
self.assertEqual(post_processor_state["add_prefix_space"] , lowercase )
self.assertEqual(post_processor_state["trim_offsets"] , lowercase )
def lowercase__ ( self : Optional[Any] ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
lowercase_ :Tuple = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
lowercase_ :Optional[Any] = F'{text_of_1_token} {text_of_1_token}'
lowercase_ :int = self.rust_tokenizer_class.from_pretrained(
lowercase , use_fast=lowercase , add_prefix_space=lowercase , trim_offsets=lowercase )
lowercase_ :Optional[int] = tokenizer_r(lowercase , return_offsets_mapping=lowercase , add_special_tokens=lowercase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase ) + 1, len(lowercase ) + 1 + len(lowercase )) , )
lowercase_ :Dict = self.rust_tokenizer_class.from_pretrained(
lowercase , use_fast=lowercase , add_prefix_space=lowercase , trim_offsets=lowercase )
lowercase_ :List[Any] = tokenizer_r(lowercase , return_offsets_mapping=lowercase , add_special_tokens=lowercase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase ) + 1, len(lowercase ) + 1 + len(lowercase )) , )
lowercase_ :Any = self.rust_tokenizer_class.from_pretrained(
lowercase , use_fast=lowercase , add_prefix_space=lowercase , trim_offsets=lowercase )
lowercase_ :Optional[Any] = tokenizer_r(lowercase , return_offsets_mapping=lowercase , add_special_tokens=lowercase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase ), len(lowercase ) + 1 + len(lowercase )) , )
lowercase_ :List[str] = self.rust_tokenizer_class.from_pretrained(
lowercase , use_fast=lowercase , add_prefix_space=lowercase , trim_offsets=lowercase )
lowercase_ :List[Any] = tokenizer_r(lowercase , return_offsets_mapping=lowercase , add_special_tokens=lowercase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase ), len(lowercase ) + 1 + len(lowercase )) , )
lowercase_ :Dict = F' {text}'
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
lowercase_ :Optional[Any] = self.rust_tokenizer_class.from_pretrained(
lowercase , use_fast=lowercase , add_prefix_space=lowercase , trim_offsets=lowercase )
lowercase_ :Optional[Any] = tokenizer_r(lowercase , return_offsets_mapping=lowercase , add_special_tokens=lowercase )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowercase ) + 1, 1 + len(lowercase ) + 1 + len(lowercase )) , )
lowercase_ :Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
lowercase , use_fast=lowercase , add_prefix_space=lowercase , trim_offsets=lowercase )
lowercase_ :Dict = tokenizer_r(lowercase , return_offsets_mapping=lowercase , add_special_tokens=lowercase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowercase ), 1 + len(lowercase ) + 1 + len(lowercase )) , )
lowercase_ :Optional[int] = self.rust_tokenizer_class.from_pretrained(
lowercase , use_fast=lowercase , add_prefix_space=lowercase , trim_offsets=lowercase )
lowercase_ :Dict = tokenizer_r(lowercase , return_offsets_mapping=lowercase , add_special_tokens=lowercase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowercase ), 1 + len(lowercase ) + 1 + len(lowercase )) , )
| 223 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = ['torch', 'transformers', 'onnx']
def __init__(self , *lowercase , **lowercase ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def _a (cls , *lowercase , **lowercase ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def _a (cls , *lowercase , **lowercase ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _lowerCAmelCase ( metaclass=__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Tuple = ['torch', 'transformers', 'onnx']
def __init__(self , *lowercase , **lowercase ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def _a (cls , *lowercase , **lowercase ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def _a (cls , *lowercase , **lowercase ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _lowerCAmelCase ( metaclass=__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Tuple = ['torch', 'transformers', 'onnx']
def __init__(self , *lowercase , **lowercase ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def _a (cls , *lowercase , **lowercase ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def _a (cls , *lowercase , **lowercase ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _lowerCAmelCase ( metaclass=__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Any = ['torch', 'transformers', 'onnx']
def __init__(self , *lowercase , **lowercase ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def _a (cls , *lowercase , **lowercase ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def _a (cls , *lowercase , **lowercase ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _lowerCAmelCase ( metaclass=__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Tuple = ['torch', 'transformers', 'onnx']
def __init__(self , *lowercase , **lowercase ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def _a (cls , *lowercase , **lowercase ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def _a (cls , *lowercase , **lowercase ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _lowerCAmelCase ( metaclass=__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : List[str] = ['torch', 'transformers', 'onnx']
def __init__(self , *lowercase , **lowercase ):
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def _a (cls , *lowercase , **lowercase ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def _a (cls , *lowercase , **lowercase ):
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] ) | 369 |
'''simple docstring'''
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() | 135 | 0 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=lowercase_ ):
"""simple docstring"""
__magic_name__ = ['torch', 'torchsde']
def __init__( self , *__snake_case , **__snake_case ):
requires_backends(self , ['''torch''', '''torchsde'''] )
@classmethod
def a_ ( cls , *__snake_case , **__snake_case ):
requires_backends(cls , ['''torch''', '''torchsde'''] )
@classmethod
def a_ ( cls , *__snake_case , **__snake_case ):
requires_backends(cls , ['''torch''', '''torchsde'''] )
| 127 |
"""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 | 0 |
"""simple docstring"""
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_A = logging.get_logger(__name__)
def UpperCAmelCase ( a_ ):
'''simple docstring'''
lowerCamelCase : List[str] = r'\w+[.]\d+'
lowerCamelCase : str = re.findall(a_, a_ )
for pat in pats:
lowerCamelCase : List[Any] = key.replace(a_, '_'.join(pat.split('.' ) ) )
return key
def UpperCAmelCase ( a_, a_, a_ ):
'''simple docstring'''
lowerCamelCase : str = pt_tuple_key[:-1] + ('scale',)
if (
any('norm' in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
lowerCamelCase : Optional[Any] = pt_tuple_key[:-1] + ('scale',)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
lowerCamelCase : List[str] = pt_tuple_key[:-1] + ('scale',)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
lowerCamelCase : Optional[Any] = pt_tuple_key[:-1] + ('embedding',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowerCamelCase : Tuple = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
lowerCamelCase : Any = pt_tensor.transpose(2, 3, 1, 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowerCamelCase : Dict = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight":
lowerCamelCase : List[Any] = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowerCamelCase : Any = pt_tuple_key[:-1] + ('weight',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowerCamelCase : int = pt_tuple_key[:-1] + ('bias',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def UpperCAmelCase ( a_, a_, a_=42 ):
'''simple docstring'''
lowerCamelCase : Dict = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
lowerCamelCase : str = flax_model.init_weights(PRNGKey(a_ ) )
lowerCamelCase : Optional[Any] = flatten_dict(a_ )
lowerCamelCase : int = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowerCamelCase : Optional[Any] = rename_key(a_ )
lowerCamelCase : Any = tuple(renamed_pt_key.split('.' ) )
# Correctly rename weight parameters
lowerCamelCase , lowerCamelCase : Optional[Any] = rename_key_and_reshape_tensor(a_, a_, a_ )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# also add unexpected weight so that warning is thrown
lowerCamelCase : str = jnp.asarray(a_ )
return unflatten_dict(a_ )
| 205 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_A = logging.get_logger('transformers.models.speecht5')
def UpperCAmelCase ( a_, a_, a_ ):
'''simple docstring'''
hf_model.apply_weight_norm()
lowerCamelCase : str = checkpoint['input_conv.weight_g']
lowerCamelCase : int = checkpoint['input_conv.weight_v']
lowerCamelCase : Optional[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 : List[str] = 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 : Optional[Any] = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
lowerCamelCase : Tuple = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
lowerCamelCase : Union[str, Any] = checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]
lowerCamelCase : Dict = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
lowerCamelCase : str = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
lowerCamelCase : Optional[Any] = checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]
lowerCamelCase : Any = checkpoint['output_conv.1.weight_g']
lowerCamelCase : Tuple = checkpoint['output_conv.1.weight_v']
lowerCamelCase : int = checkpoint['output_conv.1.bias']
hf_model.remove_weight_norm()
@torch.no_grad()
def UpperCAmelCase ( a_, a_, a_, a_=None, a_=None, ):
'''simple docstring'''
if config_path is not None:
lowerCamelCase : str = SpeechTaHifiGanConfig.from_pretrained(a_ )
else:
lowerCamelCase : Dict = SpeechTaHifiGanConfig()
lowerCamelCase : int = SpeechTaHifiGan(a_ )
lowerCamelCase : Optional[Any] = torch.load(a_ )
load_weights(orig_checkpoint['model']['generator'], a_, a_ )
lowerCamelCase : Tuple = np.load(a_ )
lowerCamelCase : str = stats[0].reshape(-1 )
lowerCamelCase : Optional[int] = stats[1].reshape(-1 )
lowerCamelCase : Dict = torch.from_numpy(a_ ).float()
lowerCamelCase : Optional[int] = torch.from_numpy(a_ ).float()
model.save_pretrained(a_ )
if repo_id:
print('Pushing to the hub...' )
model.push_to_hub(a_ )
if __name__ == "__main__":
_A = 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.'
)
_A = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 205 | 1 |
'''simple docstring'''
import math
def snake_case_ (_a : list , _a : int ):
UpperCAmelCase = len(_a )
UpperCAmelCase = int(math.floor(math.sqrt(_a ) ) )
UpperCAmelCase = 0
while arr[min(_a , _a ) - 1] < x:
UpperCAmelCase = step
step += int(math.floor(math.sqrt(_a ) ) )
if prev >= n:
return -1
while arr[prev] < x:
UpperCAmelCase = prev + 1
if prev == min(_a , _a ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
A =input('Enter numbers separated by a comma:\n').strip()
A =[int(item) for item in user_input.split(',')]
A =int(input('Enter the number to be searched:\n'))
A =jump_search(arr, x)
if res == -1:
print('Number not found!')
else:
print(f"""Number {x} is at index {res}""")
| 34 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all LED models at https://huggingface.co/models?filter=LED
UpperCamelCase_ = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
UpperCamelCase_ = {
'''allenai/led-base-16384''': 16384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCAmelCase_ : int = (
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
UpperCAmelCase_ : Dict = bs[:]
UpperCAmelCase_ : Any = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_a )
cs.append(2**8 + n )
n += 1
UpperCAmelCase_ : Any = [chr(_a ) for n in cs]
return dict(zip(_a , _a ) )
def lowerCamelCase_ ( _a : List[str] ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = set()
UpperCAmelCase_ : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
UpperCAmelCase_ : Optional[int] = char
return pairs
class _snake_case ( __snake_case ):
'''simple docstring'''
A__ : str = VOCAB_FILES_NAMES
A__ : List[str] = PRETRAINED_VOCAB_FILES_MAP
A__ : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : Optional[int] = ["input_ids", "attention_mask"]
def __init__( self: Union[str, Any] ,lowerCamelCase_: Tuple ,lowerCamelCase_: Any ,lowerCamelCase_: Union[str, Any]="replace" ,lowerCamelCase_: Optional[Any]="<s>" ,lowerCamelCase_: List[Any]="</s>" ,lowerCamelCase_: List[str]="</s>" ,lowerCamelCase_: int="<s>" ,lowerCamelCase_: int="<unk>" ,lowerCamelCase_: str="<pad>" ,lowerCamelCase_: Optional[Any]="<mask>" ,lowerCamelCase_: List[str]=False ,**lowerCamelCase_: Tuple ,) -> Any:
UpperCAmelCase_ : Union[str, Any] = AddedToken(lowerCamelCase_ ,lstrip=lowerCamelCase_ ,rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ ,lowerCamelCase_ ) else bos_token
UpperCAmelCase_ : int = AddedToken(lowerCamelCase_ ,lstrip=lowerCamelCase_ ,rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ ,lowerCamelCase_ ) else eos_token
UpperCAmelCase_ : List[str] = AddedToken(lowerCamelCase_ ,lstrip=lowerCamelCase_ ,rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ ,lowerCamelCase_ ) else sep_token
UpperCAmelCase_ : List[str] = AddedToken(lowerCamelCase_ ,lstrip=lowerCamelCase_ ,rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ ,lowerCamelCase_ ) else cls_token
UpperCAmelCase_ : Optional[Any] = AddedToken(lowerCamelCase_ ,lstrip=lowerCamelCase_ ,rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ ,lowerCamelCase_ ) else unk_token
UpperCAmelCase_ : List[str] = AddedToken(lowerCamelCase_ ,lstrip=lowerCamelCase_ ,rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ ,lowerCamelCase_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase_ : str = AddedToken(lowerCamelCase_ ,lstrip=lowerCamelCase_ ,rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ ,lowerCamelCase_ ) else mask_token
super().__init__(
errors=lowerCamelCase_ ,bos_token=lowerCamelCase_ ,eos_token=lowerCamelCase_ ,unk_token=lowerCamelCase_ ,sep_token=lowerCamelCase_ ,cls_token=lowerCamelCase_ ,pad_token=lowerCamelCase_ ,mask_token=lowerCamelCase_ ,add_prefix_space=lowerCamelCase_ ,**lowerCamelCase_ ,)
with open(lowerCamelCase_ ,encoding="""utf-8""" ) as vocab_handle:
UpperCAmelCase_ : Union[str, Any] = json.load(lowerCamelCase_ )
UpperCAmelCase_ : Optional[int] = {v: k for k, v in self.encoder.items()}
UpperCAmelCase_ : Any = errors # how to handle errors in decoding
UpperCAmelCase_ : int = bytes_to_unicode()
UpperCAmelCase_ : Dict = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCamelCase_ ,encoding="""utf-8""" ) as merges_handle:
UpperCAmelCase_ : Any = merges_handle.read().split("""\n""" )[1:-1]
UpperCAmelCase_ : int = [tuple(merge.split() ) for merge in bpe_merges]
UpperCAmelCase_ : Union[str, Any] = dict(zip(lowerCamelCase_ ,range(len(lowerCamelCase_ ) ) ) )
UpperCAmelCase_ : Tuple = {}
UpperCAmelCase_ : Optional[int] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
UpperCAmelCase_ : int = re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def A__ ( self: List[str] ) -> List[str]:
return len(self.encoder )
def A__ ( self: Any ) -> Union[str, Any]:
return dict(self.encoder ,**self.added_tokens_encoder )
def A__ ( self: Tuple ,lowerCamelCase_: Dict ) -> Optional[Any]:
if token in self.cache:
return self.cache[token]
UpperCAmelCase_ : Union[str, Any] = tuple(lowerCamelCase_ )
UpperCAmelCase_ : Union[str, Any] = get_pairs(lowerCamelCase_ )
if not pairs:
return token
while True:
UpperCAmelCase_ : Union[str, Any] = min(lowerCamelCase_ ,key=lambda lowerCamelCase_ : self.bpe_ranks.get(lowerCamelCase_ ,float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
UpperCAmelCase_ , UpperCAmelCase_ : Any = bigram
UpperCAmelCase_ : Optional[Any] = []
UpperCAmelCase_ : List[str] = 0
while i < len(lowerCamelCase_ ):
try:
UpperCAmelCase_ : str = word.index(lowerCamelCase_ ,lowerCamelCase_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
UpperCAmelCase_ : Union[str, Any] = j
if word[i] == first and i < len(lowerCamelCase_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
UpperCAmelCase_ : List[str] = tuple(lowerCamelCase_ )
UpperCAmelCase_ : List[Any] = new_word
if len(lowerCamelCase_ ) == 1:
break
else:
UpperCAmelCase_ : List[str] = get_pairs(lowerCamelCase_ )
UpperCAmelCase_ : int = """ """.join(lowerCamelCase_ )
UpperCAmelCase_ : Optional[Any] = word
return word
def A__ ( self: Union[str, Any] ,lowerCamelCase_: Tuple ) -> List[str]:
UpperCAmelCase_ : str = []
for token in re.findall(self.pat ,lowerCamelCase_ ):
UpperCAmelCase_ : List[Any] = """""".join(
self.byte_encoder[b] for b in token.encode("""utf-8""" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase_ ).split(""" """ ) )
return bpe_tokens
def A__ ( self: List[Any] ,lowerCamelCase_: Optional[Any] ) -> Optional[int]:
return self.encoder.get(lowerCamelCase_ ,self.encoder.get(self.unk_token ) )
def A__ ( self: List[str] ,lowerCamelCase_: str ) -> Optional[Any]:
return self.decoder.get(lowerCamelCase_ )
def A__ ( self: List[str] ,lowerCamelCase_: List[str] ) -> List[Any]:
UpperCAmelCase_ : str = """""".join(lowerCamelCase_ )
UpperCAmelCase_ : int = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" ,errors=self.errors )
return text
def A__ ( self: Optional[Any] ,lowerCamelCase_: str ,lowerCamelCase_: Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(lowerCamelCase_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase_ : List[Any] = os.path.join(
lowerCamelCase_ ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase_ : List[str] = os.path.join(
lowerCamelCase_ ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(lowerCamelCase_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowerCamelCase_ ,ensure_ascii=lowerCamelCase_ ) + """\n""" )
UpperCAmelCase_ : str = 0
with open(lowerCamelCase_ ,"""w""" ,encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda lowerCamelCase_ : kv[1] ):
if index != token_index:
logger.warning(
F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
""" Please check that the tokenizer is not corrupted!""" )
UpperCAmelCase_ : Tuple = token_index
writer.write(""" """.join(lowerCamelCase_ ) + """\n""" )
index += 1
return vocab_file, merge_file
def A__ ( self: str ,lowerCamelCase_: List[int] ,lowerCamelCase_: Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase_ : int = [self.cls_token_id]
UpperCAmelCase_ : Optional[int] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def A__ ( self: Union[str, Any] ,lowerCamelCase_: List[int] ,lowerCamelCase_: Optional[List[int]] = None ,lowerCamelCase_: bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase_ ,token_ids_a=lowerCamelCase_ ,already_has_special_tokens=lowerCamelCase_ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase_ )) + [1]
return [1] + ([0] * len(lowerCamelCase_ )) + [1, 1] + ([0] * len(lowerCamelCase_ )) + [1]
def A__ ( self: str ,lowerCamelCase_: List[int] ,lowerCamelCase_: Optional[List[int]] = None ) -> List[int]:
UpperCAmelCase_ : Optional[Any] = [self.sep_token_id]
UpperCAmelCase_ : 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 + sep + token_ids_a + sep ) * [0]
def A__ ( self: Optional[Any] ,lowerCamelCase_: Optional[Any] ,lowerCamelCase_: str=False ,**lowerCamelCase_: List[str] ) -> Optional[int]:
UpperCAmelCase_ : Optional[int] = kwargs.pop("""add_prefix_space""" ,self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase_ ) > 0 and not text[0].isspace()):
UpperCAmelCase_ : Dict = """ """ + text
return (text, kwargs)
def A__ ( self: List[str] ,lowerCamelCase_: Union[Dict[str, EncodedInput], BatchEncoding] ,lowerCamelCase_: Optional[int] = None ,lowerCamelCase_: PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,lowerCamelCase_: Optional[int] = None ,lowerCamelCase_: Optional[bool] = None ,) -> dict:
UpperCAmelCase_ : Optional[int] = super()._pad(
encoded_inputs=lowerCamelCase_ ,max_length=lowerCamelCase_ ,padding_strategy=lowerCamelCase_ ,pad_to_multiple_of=lowerCamelCase_ ,return_attention_mask=lowerCamelCase_ ,)
# Load from model defaults
if return_attention_mask is None:
UpperCAmelCase_ : str = """attention_mask""" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
UpperCAmelCase_ : str = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
UpperCAmelCase_ : List[Any] = len(encoded_inputs["""global_attention_mask"""] ) != len(lowerCamelCase_ )
if needs_to_be_padded:
UpperCAmelCase_ : Dict = len(lowerCamelCase_ ) - len(encoded_inputs["""global_attention_mask"""] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
UpperCAmelCase_ : str = (
encoded_inputs["""global_attention_mask"""] + [-1] * difference
)
elif self.padding_side == "left":
UpperCAmelCase_ : List[str] = [-1] * difference + encoded_inputs[
"""global_attention_mask"""
]
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return encoded_inputs
| 345 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __lowercase ( a__ , a__ , a__ , a__ , ) -> list[float]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = coefficient_matrix.shape
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = constant_matrix.shape
if rowsa != colsa:
__SCREAMING_SNAKE_CASE = f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
raise ValueError(a__ )
if colsa != 1:
__SCREAMING_SNAKE_CASE = f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
raise ValueError(a__ )
if rowsa != rowsa:
__SCREAMING_SNAKE_CASE = (
'Coefficient and constant matrices dimensions must be nxn and nx1 but '
f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
)
raise ValueError(a__ )
if len(a__ ) != rowsa:
__SCREAMING_SNAKE_CASE = (
'Number of initial values must be equal to number of rows in coefficient '
f"""matrix but received {len(a__ )} and {rowsa}"""
)
raise ValueError(a__ )
if iterations <= 0:
raise ValueError('Iterations must be at least 1' )
__SCREAMING_SNAKE_CASE = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = table.shape
strictly_diagonally_dominant(a__ )
# Iterates the whole matrix for given number of times
for _ in range(a__ ):
__SCREAMING_SNAKE_CASE = []
for row in range(a__ ):
__SCREAMING_SNAKE_CASE = 0
for col in range(a__ ):
if col == row:
__SCREAMING_SNAKE_CASE = table[row][col]
elif col == cols - 1:
__SCREAMING_SNAKE_CASE = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
__SCREAMING_SNAKE_CASE = (temp + val) / denom
new_val.append(a__ )
__SCREAMING_SNAKE_CASE = new_val
return [float(a__ ) for i in new_val]
def __lowercase ( a__ ) -> bool:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = table.shape
__SCREAMING_SNAKE_CASE = True
for i in range(0 , a__ ):
__SCREAMING_SNAKE_CASE = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError('Coefficient matrix is not strictly diagonally dominant' )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 363 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = '''Wav2Vec2FeatureExtractor'''
UpperCamelCase__ : Union[str, Any] = '''AutoTokenizer'''
def __init__( self , _A , _A ):
'''simple docstring'''
super().__init__(_A , _A )
__SCREAMING_SNAKE_CASE = self.feature_extractor
__SCREAMING_SNAKE_CASE = False
@classmethod
def _A ( cls , _A , **_A ):
'''simple docstring'''
try:
return super().from_pretrained(_A , **_A )
except OSError:
warnings.warn(
f"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
' include a `tokenizer_class` attribute is deprecated and will be '
'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
' attribute to either your `config.json` or `tokenizer_config.json` '
'file to suppress this warning: ' , _A , )
__SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor.from_pretrained(_A , **_A )
__SCREAMING_SNAKE_CASE = WavaVecaCTCTokenizer.from_pretrained(_A , **_A )
return cls(feature_extractor=_A , tokenizer=_A )
def __call__( self , *_A , **_A ):
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*_A , **_A )
if "raw_speech" in kwargs:
warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
__SCREAMING_SNAKE_CASE = kwargs.pop('raw_speech' )
else:
__SCREAMING_SNAKE_CASE = kwargs.pop('audio' , _A )
__SCREAMING_SNAKE_CASE = kwargs.pop('sampling_rate' , _A )
__SCREAMING_SNAKE_CASE = kwargs.pop('text' , _A )
if len(_A ) > 0:
__SCREAMING_SNAKE_CASE = args[0]
__SCREAMING_SNAKE_CASE = 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:
__SCREAMING_SNAKE_CASE = self.feature_extractor(_A , *_A , sampling_rate=_A , **_A )
if text is not None:
__SCREAMING_SNAKE_CASE = self.tokenizer(_A , **_A )
if text is None:
return inputs
elif audio is None:
return encodings
else:
__SCREAMING_SNAKE_CASE = encodings['input_ids']
return inputs
def _A ( self , *_A , **_A ):
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor.pad(*_A , **_A )
__SCREAMING_SNAKE_CASE = kwargs.pop('input_features' , _A )
__SCREAMING_SNAKE_CASE = kwargs.pop('labels' , _A )
if len(_A ) > 0:
__SCREAMING_SNAKE_CASE = args[0]
__SCREAMING_SNAKE_CASE = args[1:]
if input_features is not None:
__SCREAMING_SNAKE_CASE = self.feature_extractor.pad(_A , *_A , **_A )
if labels is not None:
__SCREAMING_SNAKE_CASE = self.tokenizer.pad(_A , **_A )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
__SCREAMING_SNAKE_CASE = labels['input_ids']
return input_features
def _A ( self , *_A , **_A ):
'''simple docstring'''
return self.tokenizer.batch_decode(*_A , **_A )
def _A ( self , *_A , **_A ):
'''simple docstring'''
return self.tokenizer.decode(*_A , **_A )
@contextmanager
def _A ( self ):
'''simple docstring'''
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.' )
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = self.tokenizer
yield
__SCREAMING_SNAKE_CASE = self.feature_extractor
__SCREAMING_SNAKE_CASE = False
| 118 | 0 |
"""simple docstring"""
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 SCREAMING_SNAKE_CASE ( a_ , unittest.TestCase ):
"""simple docstring"""
lowercase__ = FlaxAutoencoderKL
@property
def __lowerCAmelCase ( self : int ):
lowerCAmelCase__ : Optional[int] = 4
lowerCAmelCase__ : Dict = 3
lowerCAmelCase__ : str = (3_2, 3_2)
lowerCAmelCase__ : str = jax.random.PRNGKey(0 )
lowerCAmelCase__ : str = jax.random.uniform(lowercase_ ,((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ : Tuple = {
'''block_out_channels''': [3_2, 6_4],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 4,
}
lowerCAmelCase__ : Optional[Any] = self.dummy_input
return init_dict, inputs_dict
| 106 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__UpperCamelCase : int = logging.getLogger(__name__)
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
# save results
if os.path.exists(A_ ):
if os.path.exists(os.path.join(A_ , '''config.json''' ) ) and os.path.isfile(
os.path.join(A_ , '''config.json''' ) ):
os.remove(os.path.join(A_ , '''config.json''' ) )
if os.path.exists(os.path.join(A_ , '''pytorch_model.bin''' ) ) and os.path.isfile(
os.path.join(A_ , '''pytorch_model.bin''' ) ):
os.remove(os.path.join(A_ , '''pytorch_model.bin''' ) )
else:
os.makedirs(A_ )
model.save_pretrained(A_ )
def __SCREAMING_SNAKE_CASE ( A_ , A_=False ):
lowerCAmelCase__ : Optional[Any] = 2
if unlogit:
lowerCAmelCase__ : Union[str, Any] = torch.pow(A_ , A_ )
lowerCAmelCase__ : Optional[Any] = p * torch.log(A_ )
lowerCAmelCase__ : List[Any] = 0
return -plogp.sum(dim=-1 )
def __SCREAMING_SNAKE_CASE ( A_ ):
logger.info('''lv, h >\t''' + '''\t'''.join(f'{x + 1}' for x in range(len(A_ ) ) ) )
for row in range(len(A_ ) ):
if tensor.dtype != torch.long:
logger.info(f'layer {row + 1}:\t' + '''\t'''.join(f'{x:.5f}' for x in tensor[row].cpu().data ) )
else:
logger.info(f'layer {row + 1}:\t' + '''\t'''.join(f'{x:d}' for x in tensor[row].cpu().data ) )
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_=True , A_=True , A_=None , A_=False ):
lowerCAmelCase__ ,lowerCAmelCase__ : List[Any] = model.config.num_hidden_layers, model.config.num_attention_heads
lowerCAmelCase__ : Dict = torch.zeros(A_ , A_ ).to(args.device )
lowerCAmelCase__ : int = torch.zeros(A_ , A_ ).to(args.device )
if head_mask is None:
lowerCAmelCase__ : Union[str, Any] = torch.ones(A_ , A_ ).to(args.device )
head_mask.requires_grad_(requires_grad=A_ )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
lowerCAmelCase__ : Union[str, Any] = None
lowerCAmelCase__ : Optional[int] = 0.0
lowerCAmelCase__ : Optional[int] = 0.0
for step, inputs in enumerate(tqdm(A_ , desc='''Iteration''' , disable=args.local_rank not in [-1, 0] ) ):
lowerCAmelCase__ : Any = tuple(t.to(args.device ) for t in inputs )
((lowerCAmelCase__) ,) : List[Any] = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
lowerCAmelCase__ : Any = model(A_ , labels=A_ , head_mask=A_ )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : Dict = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(A_ ):
lowerCAmelCase__ : Dict = entropy(attn.detach() , A_ )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(A_ ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
lowerCAmelCase__ : Any = 2
lowerCAmelCase__ : Dict = torch.pow(torch.pow(A_ , A_ ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-2_0
if not args.dont_normalize_global_importance:
lowerCAmelCase__ : List[Any] = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('''Attention entropies''' )
print_ad_tensor(A_ )
if compute_importance:
logger.info('''Head importance scores''' )
print_ad_tensor(A_ )
logger.info('''Head ranked by importance scores''' )
lowerCAmelCase__ : str = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
lowerCAmelCase__ : Optional[int] = torch.arange(
head_importance.numel() , device=args.device )
lowerCAmelCase__ : int = head_ranks.view_as(A_ )
print_ad_tensor(A_ )
return attn_entropy, head_importance, total_loss
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ ):
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : List[str] = compute_heads_importance(A_ , A_ , A_ , compute_entropy=A_ )
lowerCAmelCase__ : Union[str, Any] = 1 / loss # instead of downsteam score use the LM loss
logger.info('''Pruning: original score: %f, threshold: %f''' , A_ , original_score * args.masking_threshold )
lowerCAmelCase__ : Union[str, Any] = torch.ones_like(A_ )
lowerCAmelCase__ : List[str] = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
lowerCAmelCase__ : int = original_score
while current_score >= original_score * args.masking_threshold:
lowerCAmelCase__ : Union[str, Any] = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
lowerCAmelCase__ : str = float('''Inf''' )
lowerCAmelCase__ : List[Any] = head_importance.view(-1 ).sort()[1]
if len(A_ ) <= num_to_mask:
print('''BREAK BY num_to_mask''' )
break
# mask heads
lowerCAmelCase__ : List[Any] = current_heads_to_mask[:num_to_mask]
logger.info('''Heads to mask: %s''' , str(current_heads_to_mask.tolist() ) )
lowerCAmelCase__ : int = new_head_mask.view(-1 )
lowerCAmelCase__ : Optional[int] = 0.0
lowerCAmelCase__ : Union[str, Any] = new_head_mask.view_as(A_ )
lowerCAmelCase__ : Tuple = new_head_mask.clone().detach()
print_ad_tensor(A_ )
# Compute metric and head importance again
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : List[Any] = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , head_mask=A_ )
lowerCAmelCase__ : Tuple = 1 / loss
logger.info(
'''Masking: current score: %f, remaining heads %d (%.1f percents)''' , A_ , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 1_00 , )
logger.info('''Final head mask''' )
print_ad_tensor(A_ )
np.save(os.path.join(args.output_dir , '''head_mask.npy''' ) , head_mask.detach().cpu().numpy() )
return head_mask
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_ ):
lowerCAmelCase__ : Optional[Any] = datetime.now()
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : Optional[Any] = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ )
lowerCAmelCase__ : Optional[Any] = 1 / loss
lowerCAmelCase__ : Tuple = datetime.now() - before_time
lowerCAmelCase__ : int = sum(p.numel() for p in model.parameters() )
lowerCAmelCase__ : List[Any] = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(A_ ) )
}
for k, v in heads_to_prune.items():
if isinstance(A_ , A_ ):
lowerCAmelCase__ : int = [
v,
]
assert sum(len(A_ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(A_ )
lowerCAmelCase__ : List[Any] = sum(p.numel() for p in model.parameters() )
lowerCAmelCase__ : Any = datetime.now()
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : int = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ , actually_pruned=A_ , )
lowerCAmelCase__ : int = 1 / loss
lowerCAmelCase__ : Dict = datetime.now() - before_time
logger.info(
'''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''' , A_ , A_ , pruned_num_params / original_num_params * 1_00 , )
logger.info('''Pruning: score with masking: %f score with pruning: %f''' , A_ , A_ )
logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''' , original_time / new_time * 1_00 )
save_model(A_ , args.output_dir )
def __SCREAMING_SNAKE_CASE ( ):
lowerCAmelCase__ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--data_dir''' , default=A_ , type=A_ , required=A_ , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=A_ , type=A_ , required=A_ , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--output_dir''' , default=A_ , type=A_ , required=A_ , help='''The output directory where the model predictions and checkpoints will be written.''' , )
# Other parameters
parser.add_argument(
'''--config_name''' , default='''''' , type=A_ , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--tokenizer_name''' , default='''''' , type=A_ , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--cache_dir''' , default=A_ , type=A_ , help='''Where do you want to store the pre-trained models downloaded from s3''' , )
parser.add_argument(
'''--data_subset''' , type=A_ , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.''' )
parser.add_argument(
'''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory''' )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
parser.add_argument(
'''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers''' )
parser.add_argument(
'''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )
parser.add_argument(
'''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.''' )
parser.add_argument(
'''--masking_threshold''' , default=0.9 , type=A_ , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
parser.add_argument(
'''--masking_amount''' , default=0.1 , type=A_ , help='''Amount to heads to masking at each masking step.''' )
parser.add_argument('''--metric_name''' , default='''acc''' , type=A_ , help='''Metric to use for head masking.''' )
parser.add_argument(
'''--max_seq_length''' , default=1_28 , type=A_ , help=(
'''The maximum total input sequence length after WordPiece tokenization. \n'''
'''Sequences longer than this will be truncated, sequences shorter padded.'''
) , )
parser.add_argument('''--batch_size''' , default=1 , type=A_ , help='''Batch size.''' )
parser.add_argument('''--seed''' , type=A_ , default=42 )
parser.add_argument('''--local_rank''' , type=A_ , default=-1 , help='''local_rank for distributed training on gpus''' )
parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available''' )
parser.add_argument('''--server_ip''' , type=A_ , default='''''' , help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' , type=A_ , default='''''' , help='''Can be used for distant debugging.''' )
lowerCAmelCase__ : Optional[Any] = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=A_ )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
lowerCAmelCase__ : Union[str, Any] = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )
lowerCAmelCase__ : str = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
lowerCAmelCase__ : Dict = torch.device('''cuda''' , args.local_rank )
lowerCAmelCase__ : Union[str, Any] = 1
torch.distributed.init_process_group(backend='''nccl''' ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
lowerCAmelCase__ : List[str] = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
lowerCAmelCase__ : Dict = nn.parallel.DistributedDataParallel(
A_ , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=A_ )
elif args.n_gpu > 1:
lowerCAmelCase__ : List[Any] = nn.DataParallel(A_ )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=A_ )
torch.save(A_ , os.path.join(args.output_dir , '''run_args.bin''' ) )
logger.info('''Training/evaluation parameters %s''' , A_ )
# Prepare dataset
lowerCAmelCase__ : str = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
lowerCAmelCase__ : Union[str, Any] = (torch.from_numpy(A_ ),)
lowerCAmelCase__ : Tuple = TensorDataset(*A_ )
lowerCAmelCase__ : Optional[int] = RandomSampler(A_ )
lowerCAmelCase__ : Dict = DataLoader(A_ , sampler=A_ , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(A_ , A_ , A_ )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
lowerCAmelCase__ : Tuple = mask_heads(A_ , A_ , A_ )
prune_heads(A_ , A_ , A_ , A_ )
if __name__ == "__main__":
main()
| 106 | 1 |
'''simple docstring'''
from math import pow, sqrt
def lowerCAmelCase__ ( *lowerCamelCase : float ):
_A : Optional[Any] = len(lowerCamelCase ) > 0 and all(value > 0.0 for value in values )
return result
def lowerCAmelCase__ ( lowerCamelCase : float ,lowerCamelCase : float ):
return (
round(sqrt(molar_mass_a / molar_mass_a ) ,6 )
if validate(lowerCamelCase ,lowerCamelCase )
else ValueError('Input Error: Molar mass values must greater than 0.' )
)
def lowerCAmelCase__ ( lowerCamelCase : float ,lowerCamelCase : float ,lowerCamelCase : float ):
return (
round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) ,6 )
if validate(lowerCamelCase ,lowerCamelCase ,lowerCamelCase )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def lowerCAmelCase__ ( lowerCamelCase : float ,lowerCamelCase : float ,lowerCamelCase : float ):
return (
round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) ,6 )
if validate(lowerCamelCase ,lowerCamelCase ,lowerCamelCase )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def lowerCAmelCase__ ( lowerCamelCase : float ,lowerCamelCase : float ,lowerCamelCase : float ):
return (
round(molar_mass / pow(effusion_rate_a / effusion_rate_a ,2 ) ,6 )
if validate(lowerCamelCase ,lowerCamelCase ,lowerCamelCase )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def lowerCAmelCase__ ( lowerCamelCase : float ,lowerCamelCase : float ,lowerCamelCase : float ):
return (
round(pow(effusion_rate_a / effusion_rate_a ,2 ) / molar_mass ,6 )
if validate(lowerCamelCase ,lowerCamelCase ,lowerCamelCase )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
| 227 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
A : int = 2
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : List[str] , *, # begin keyword-only arguments
SCREAMING_SNAKE_CASE : Optional[Any]="<s>" , SCREAMING_SNAKE_CASE : int="<pad>" , SCREAMING_SNAKE_CASE : Optional[Any]="</s>" , SCREAMING_SNAKE_CASE : Tuple="<unk>" , SCREAMING_SNAKE_CASE : List[Any]=None , ):
_A , _A , _A , _A : Any = bos, unk, pad, eos
_A : Optional[Any] = []
_A : Optional[Any] = []
_A : Optional[int] = {}
_A : Dict = self.add_symbol(SCREAMING_SNAKE_CASE)
_A : List[str] = self.add_symbol(SCREAMING_SNAKE_CASE)
_A : str = self.add_symbol(SCREAMING_SNAKE_CASE)
_A : Any = self.add_symbol(SCREAMING_SNAKE_CASE)
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(SCREAMING_SNAKE_CASE)
_A : List[str] = len(self.symbols)
def __eq__( self : int , SCREAMING_SNAKE_CASE : Optional[Any]):
return self.indices == other.indices
def __getitem__( self : List[Any] , SCREAMING_SNAKE_CASE : Tuple):
if idx < len(self.symbols):
return self.symbols[idx]
return self.unk_word
def __len__( self : Union[str, Any]):
return len(self.symbols)
def __contains__( self : Optional[Any] , SCREAMING_SNAKE_CASE : List[str]):
return sym in self.indices
@classmethod
def A ( cls : Dict , SCREAMING_SNAKE_CASE : Optional[Any]):
_A : Any = cls()
d.add_from_file(SCREAMING_SNAKE_CASE)
return d
def A ( self : Dict , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Optional[int]=1 , SCREAMING_SNAKE_CASE : int=False):
if word in self.indices and not overwrite:
_A : str = self.indices[word]
_A : List[str] = self.count[idx] + n
return idx
else:
_A : Optional[Any] = len(self.symbols)
_A : Union[str, Any] = idx
self.symbols.append(SCREAMING_SNAKE_CASE)
self.count.append(SCREAMING_SNAKE_CASE)
return idx
def A ( self : Dict , SCREAMING_SNAKE_CASE : Optional[int]):
return 0
def A ( self : Optional[Any] , SCREAMING_SNAKE_CASE : List[Any]):
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE):
try:
with open(SCREAMING_SNAKE_CASE , 'r' , encoding='utf-8') as fd:
self.add_from_file(SCREAMING_SNAKE_CASE)
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(SCREAMING_SNAKE_CASE))
return
_A : Union[str, Any] = f.readlines()
_A : Any = self._load_meta(SCREAMING_SNAKE_CASE)
for line in lines[indices_start_line:]:
try:
_A , _A : List[str] = line.rstrip().rsplit(' ' , 1)
if field == "#fairseq:overwrite":
_A : int = True
_A , _A : List[str] = line.rsplit(' ' , 1)
else:
_A : Union[str, Any] = False
_A : List[str] = int(SCREAMING_SNAKE_CASE)
_A : Optional[Any] = line
if word in self and not overwrite:
raise RuntimeError(
'Duplicate word found when loading Dictionary: \'{}\'. '
'Duplicate words can overwrite earlier ones by adding the '
'#fairseq:overwrite flag at the end of the corresponding row '
'in the dictionary file. If using the Camembert model, please '
'download an updated copy of the model file.'.format(SCREAMING_SNAKE_CASE))
self.add_symbol(SCREAMING_SNAKE_CASE , n=SCREAMING_SNAKE_CASE , overwrite=SCREAMING_SNAKE_CASE)
except ValueError:
raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\'')
def lowerCAmelCase__ ( lowerCamelCase : Optional[int] ):
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
_A : Union[str, Any] = dict((re.sub(R'@@$' ,'' ,lowerCamelCase ), v) if k.endswith('@@' ) else (re.sub(R'$' ,'</w>' ,lowerCamelCase ), v) for k, v in d.items() )
_A : Optional[Any] = '<s> <pad> </s> <unk>'.split()
# restore the special tokens
for k in keep_keys:
del da[F'{k}</w>']
_A : str = d[k] # restore
return da
def lowerCAmelCase__ ( lowerCamelCase : List[str] ,lowerCamelCase : List[str] ):
# prep
if not os.path.exists(lowerCamelCase ):
raise ValueError(F'path {biogpt_checkpoint_path} does not exist!' )
os.makedirs(lowerCamelCase ,exist_ok=lowerCamelCase )
print(F'Writing results to {pytorch_dump_folder_path}' )
# handle various types of models
_A : Dict = os.path.join(lowerCamelCase ,'checkpoint.pt' )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {checkpoint_file} does not exist!' )
_A : int = torch.load(lowerCamelCase ,map_location='cpu' )
_A : Dict = chkpt['cfg']['model']
# dicts
_A : Any = os.path.join(lowerCamelCase ,'dict.txt' )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {dict_file} does not exist!' )
_A : Any = Dictionary.load(lowerCamelCase )
_A : Optional[int] = rewrite_dict_keys(src_dict.indices )
_A : List[Any] = len(lowerCamelCase )
_A : str = os.path.join(lowerCamelCase ,VOCAB_FILES_NAMES['vocab_file'] )
print(F'Generating {src_vocab_file} of {src_vocab_size} records' )
with open(lowerCamelCase ,'w' ,encoding='utf-8' ) as f:
f.write(json.dumps(lowerCamelCase ,ensure_ascii=lowerCamelCase ,indent=lowerCamelCase ) )
# merges_file (bpecodes)
_A : Optional[int] = os.path.join(lowerCamelCase ,'bpecodes' )
if not os.path.isfile(lowerCamelCase ):
raise ValueError(F'path to the file {bpecodes_file} does not exist!' )
_A : Dict = os.path.join(lowerCamelCase ,VOCAB_FILES_NAMES['merges_file'] )
shutil.copyfile(lowerCamelCase ,lowerCamelCase )
# model config
_A : str = os.path.join(lowerCamelCase ,'config.json' )
_A : int = {
'activation_dropout': args['activation_dropout'],
'architectures': ['BioGptForCausalLM'],
'attention_probs_dropout_prob': args['attention_dropout'],
'bos_token_id': 0,
'eos_token_id': 2,
'hidden_act': args['activation_fn'],
'hidden_dropout_prob': args['dropout'],
'hidden_size': args['decoder_embed_dim'],
'initializer_range': 0.02,
'intermediate_size': args['decoder_ffn_embed_dim'],
'layer_norm_eps': 1E-12,
'layerdrop': args['decoder_layerdrop'],
'max_position_embeddings': args['max_target_positions'],
'model_type': 'biogpt',
'num_attention_heads': args['decoder_attention_heads'],
'num_hidden_layers': args['decoder_layers'],
'pad_token_id': 1,
'scale_embedding': not args['no_scale_embedding'],
'tie_word_embeddings': args['share_decoder_input_output_embed'],
'vocab_size': src_vocab_size,
}
# good hparam defaults to start with
print(F'Generating {biogpt_model_config_file}' )
with open(lowerCamelCase ,'w' ,encoding='utf-8' ) as f:
f.write(json.dumps(lowerCamelCase ,ensure_ascii=lowerCamelCase ,indent=lowerCamelCase ) )
# tokenizer config
_A : Union[str, Any] = os.path.join(lowerCamelCase ,lowerCamelCase )
_A : Any = {
'bos_token': '<s>',
'eos_token': '</s>',
'model_max_length': 1024,
'pad_token': '<pad>',
'special_tokens_map_file': None,
'tokenizer_class': 'BioGptTokenizer',
'unk_token': '<unk>',
}
print(F'Generating {biogpt_tokenizer_config_file}' )
with open(lowerCamelCase ,'w' ,encoding='utf-8' ) as f:
f.write(json.dumps(lowerCamelCase ,ensure_ascii=lowerCamelCase ,indent=lowerCamelCase ) )
# model
_A : List[Any] = chkpt['model']
# remove unneeded keys
_A : int = [
'decoder.version',
]
for k in ignore_keys:
model_state_dict.pop(lowerCamelCase ,lowerCamelCase )
_A : Any = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith('output_projection.weight' ):
_A : str = model_state_dict.pop(lowerCamelCase )
else:
_A : Dict = model_state_dict.pop(lowerCamelCase )
_A : Any = BioGptConfig.from_pretrained(lowerCamelCase )
_A : Union[str, Any] = BioGptForCausalLM(lowerCamelCase )
# check that it loads ok
model_new.load_state_dict(lowerCamelCase )
# save
_A : Union[str, Any] = os.path.join(lowerCamelCase ,lowerCamelCase )
print(F'Generating {pytorch_weights_dump_path}' )
torch.save(lowerCamelCase ,lowerCamelCase )
print('Conversion is done!' )
if __name__ == "__main__":
A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--biogpt_checkpoint_path''',
default=None,
type=str,
required=True,
help=(
'''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
''' bpecodes, etc.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A : int = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 227 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = DDIMPipeline
lowerCAmelCase__ = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
lowerCAmelCase__ = PipelineTesterMixin.required_optional_params - {
'num_images_per_prompt',
'latents',
'callback',
'callback_steps',
}
lowerCAmelCase__ = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
lowerCAmelCase__ = False
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
lowerCamelCase_ = DDIMScheduler()
lowerCamelCase_ = {"unet": unet, "scheduler": scheduler}
return components
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase=0 ) -> Any:
if str(lowercase ).startswith("mps" ):
lowerCamelCase_ = torch.manual_seed(lowercase )
else:
lowerCamelCase_ = torch.Generator(device=lowercase ).manual_seed(lowercase )
lowerCamelCase_ = {
"batch_size": 1,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def SCREAMING_SNAKE_CASE_( self ) -> int:
lowerCamelCase_ = "cpu"
lowerCamelCase_ = self.get_dummy_components()
lowerCamelCase_ = self.pipeline_class(**lowercase )
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
lowerCamelCase_ = self.get_dummy_inputs(lowercase )
lowerCamelCase_ = pipe(**lowercase ).images
lowerCamelCase_ = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
lowerCamelCase_ = np.array(
[1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04] )
lowerCamelCase_ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase , 1e-3 )
def SCREAMING_SNAKE_CASE_( self ) -> int:
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def SCREAMING_SNAKE_CASE_( self ) -> Union[str, Any]:
super().test_save_load_local(expected_max_difference=3e-3 )
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def SCREAMING_SNAKE_CASE_( self ) -> Any:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
lowerCamelCase_ = "google/ddpm-cifar10-32"
lowerCamelCase_ = UNetaDModel.from_pretrained(lowercase )
lowerCamelCase_ = DDIMScheduler()
lowerCamelCase_ = DDIMPipeline(unet=lowercase , scheduler=lowercase )
ddim.to(lowercase )
ddim.set_progress_bar_config(disable=lowercase )
lowerCamelCase_ = torch.manual_seed(0 )
lowerCamelCase_ = ddim(generator=lowercase , eta=0.0 , output_type="numpy" ).images
lowerCamelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ = np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
lowerCamelCase_ = "google/ddpm-ema-bedroom-256"
lowerCamelCase_ = UNetaDModel.from_pretrained(lowercase )
lowerCamelCase_ = DDIMScheduler.from_pretrained(lowercase )
lowerCamelCase_ = DDIMPipeline(unet=lowercase , scheduler=lowercase )
ddpm.to(lowercase )
ddpm.set_progress_bar_config(disable=lowercase )
lowerCamelCase_ = torch.manual_seed(0 )
lowerCamelCase_ = ddpm(generator=lowercase , output_type="numpy" ).images
lowerCamelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowerCamelCase_ = np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 19 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 284 | 0 |
'''simple docstring'''
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__a: int = logging.get_logger(__name__)
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ["input_ids", "attention_mask"]
def __init__( self , __lowerCAmelCase="</s>" , __lowerCAmelCase="<unk>" , __lowerCAmelCase="<pad>" , __lowerCAmelCase=125 , __lowerCAmelCase=None , **__lowerCAmelCase , ) -> None:
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
lowercase__ : Tuple = [F"""<extra_id_{i}>""" for i in range(__lowerCAmelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
lowercase__ : Dict = len(set(filter(lambda __lowerCAmelCase : bool('''extra_id''' in str(__lowerCAmelCase ) ) , __lowerCAmelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'''
''' extra_ids tokens''' )
lowercase__ : List[str] = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else pad_token
lowercase__ : Any = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else eos_token
lowercase__ : List[str] = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else unk_token
super().__init__(
eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , extra_ids=__lowerCAmelCase , additional_special_tokens=__lowerCAmelCase , **__lowerCAmelCase , )
lowercase__ : Any = extra_ids
lowercase__ : Any = 2**8 # utf is 8 bits
# define special tokens dict
lowercase__ : Dict[int, str] = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
lowercase__ : Tuple = len(self.special_tokens_encoder )
lowercase__ : Tuple = len(__lowerCAmelCase )
for i, token in enumerate(__lowerCAmelCase ):
lowercase__ : Optional[Any] = self.vocab_size + i - n
lowercase__ : Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def _lowerCAmelCase( self ) -> List[str]:
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCAmelCase , token_ids_a=__lowerCAmelCase , already_has_special_tokens=__lowerCAmelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(__lowerCAmelCase )) + [1]
return ([0] * len(__lowerCAmelCase )) + [1] + ([0] * len(__lowerCAmelCase )) + [1]
def _lowerCAmelCase( self , __lowerCAmelCase ) -> List[int]:
if len(__lowerCAmelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase = None ) -> List[int]:
lowercase__ : Optional[Any] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase = None ) -> List[int]:
lowercase__ : Optional[Any] = self._add_eos_if_not_present(__lowerCAmelCase )
if token_ids_a is None:
return token_ids_a
else:
lowercase__ : Dict = self._add_eos_if_not_present(__lowerCAmelCase )
return token_ids_a + token_ids_a
def _lowerCAmelCase( self , __lowerCAmelCase ) -> List[str]:
lowercase__ : Tuple = [chr(__lowerCAmelCase ) for i in text.encode('''utf-8''' )]
return tokens
def _lowerCAmelCase( self , __lowerCAmelCase ) -> Any:
if token in self.special_tokens_encoder:
lowercase__ : Optional[int] = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
lowercase__ : Dict = self.added_tokens_encoder[token]
elif len(__lowerCAmelCase ) != 1:
lowercase__ : Optional[int] = self.unk_token_id
else:
lowercase__ : Tuple = ord(__lowerCAmelCase ) + self._num_special_tokens
return token_id
def _lowerCAmelCase( self , __lowerCAmelCase ) -> Any:
if index in self.special_tokens_decoder:
lowercase__ : Any = self.special_tokens_decoder[index]
else:
lowercase__ : List[str] = chr(index - self._num_special_tokens )
return token
def _lowerCAmelCase( self , __lowerCAmelCase ) -> List[Any]:
lowercase__ : Optional[Any] = b''''''
for token in tokens:
if token in self.special_tokens_decoder:
lowercase__ : Union[str, Any] = self.special_tokens_decoder[token].encode('''utf-8''' )
elif token in self.added_tokens_decoder:
lowercase__ : List[Any] = self.special_tokens_decoder[token].encode('''utf-8''' )
elif token in self.special_tokens_encoder:
lowercase__ : Dict = token.encode('''utf-8''' )
elif token in self.added_tokens_encoder:
lowercase__ : List[str] = token.encode('''utf-8''' )
else:
lowercase__ : Dict = bytes([ord(__lowerCAmelCase )] )
bstring += tok_string
lowercase__ : Optional[Any] = bstring.decode('''utf-8''' , errors='''ignore''' )
return string
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase = None ) -> Tuple[str]:
return ()
| 214 | '''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
__a: Optional[Any] = logging.get_logger(__name__)
@add_end_docstrings(a__ )
class UpperCAmelCase ( a__ ):
'''simple docstring'''
def __init__( self , **__lowerCAmelCase ) -> int:
super().__init__(**__lowerCAmelCase )
requires_backends(self , '''vision''' )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self , __lowerCAmelCase , **__lowerCAmelCase ) -> List[str]:
return super().__call__(__lowerCAmelCase , **__lowerCAmelCase )
def _lowerCAmelCase( self , **__lowerCAmelCase ) -> Optional[Any]:
lowercase__ : str = {}
if "candidate_labels" in kwargs:
lowercase__ : str = kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
lowercase__ : str = kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase="This is a photo of {}." ) -> Any:
lowercase__ : Union[str, Any] = load_image(__lowerCAmelCase )
lowercase__ : List[str] = self.image_processor(images=[image] , return_tensors=self.framework )
lowercase__ : Union[str, Any] = candidate_labels
lowercase__ : int = [hypothesis_template.format(__lowerCAmelCase ) for x in candidate_labels]
lowercase__ : Any = self.tokenizer(__lowerCAmelCase , return_tensors=self.framework , padding=__lowerCAmelCase )
lowercase__ : Any = [text_inputs]
return inputs
def _lowerCAmelCase( self , __lowerCAmelCase ) -> Optional[Any]:
lowercase__ : Any = model_inputs.pop('''candidate_labels''' )
lowercase__ : int = model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0] , __lowerCAmelCase ):
lowercase__ : Union[str, Any] = text_inputs[0]
else:
# Batching case.
lowercase__ : Optional[Any] = text_inputs[0][0]
lowercase__ : Any = self.model(**__lowerCAmelCase , **__lowerCAmelCase )
lowercase__ : Any = {
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_image,
}
return model_outputs
def _lowerCAmelCase( self , __lowerCAmelCase ) -> List[str]:
lowercase__ : Union[str, Any] = model_outputs.pop('''candidate_labels''' )
lowercase__ : Optional[int] = model_outputs['''logits'''][0]
if self.framework == "pt":
lowercase__ : Optional[int] = logits.softmax(dim=-1 ).squeeze(-1 )
lowercase__ : Any = probs.tolist()
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowercase__ : Dict = [scores]
elif self.framework == "tf":
lowercase__ : List[Any] = stable_softmax(__lowerCAmelCase , axis=-1 )
lowercase__ : str = probs.numpy().tolist()
else:
raise ValueError(F"""Unsupported framework: {self.framework}""" )
lowercase__ : Optional[int] = [
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(__lowerCAmelCase , __lowerCAmelCase ) , key=lambda __lowerCAmelCase : -x[0] )
]
return result
| 214 | 1 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def _A ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
raise ValueError("Undefined for non-natural numbers" )
a__ : Tuple =precision
a__ : List[Any] =ceil(precision / 14 )
a__ : Tuple =426_880 * Decimal(10_005 ).sqrt()
a__ : List[Any] =1
a__ : str =13_591_409
a__ : Any =Decimal(SCREAMING_SNAKE_CASE )
for k in range(1 , SCREAMING_SNAKE_CASE ):
a__ : Any =factorial(6 * k ) // (factorial(3 * k ) * factorial(SCREAMING_SNAKE_CASE ) ** 3)
linear_term += 545_140_134
exponential_term *= -262_537_412_640_768_000
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
UpperCAmelCase : Optional[int] = 50
print(F"""The first {n} digits of pi is: {pi(n)}""")
| 95 |
from math import pi
def _A ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 95 | 1 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCamelCase__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class A__ ( datasets.BuilderConfig):
A_ : Optional[datasets.Features] = None
A_ : str = "utf-8"
A_ : Optional[str] = None
A_ : Optional[str] = None
A_ : bool = True # deprecated
A_ : Optional[int] = None # deprecated
A_ : int = 1_0 << 2_0 # 10MB
A_ : Optional[bool] = None
class A__ ( datasets.ArrowBasedBuilder):
A_ : int = JsonConfig
def __lowerCamelCase ( self ):
if self.config.block_size is not None:
logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead' )
__lowerCAmelCase : List[str] = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
'The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.' )
if self.config.newlines_in_values is not None:
raise ValueError('The JSON loader parameter `newlines_in_values` is no longer supported' )
return datasets.DatasetInfo(features=self.config.features )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
if not self.config.data_files:
raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}" )
__lowerCAmelCase : Union[str, Any] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(lowerCAmelCase__ , (str, list, tuple) ):
__lowerCAmelCase : Dict = data_files
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__lowerCAmelCase : Dict = [files]
__lowerCAmelCase : List[str] = [dl_manager.iter_files(lowerCAmelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'files': files} )]
__lowerCAmelCase : Any = []
for split_name, files in data_files.items():
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__lowerCAmelCase : Optional[Any] = [files]
__lowerCAmelCase : Optional[int] = [dl_manager.iter_files(lowerCAmelCase__ ) for file in files]
splits.append(datasets.SplitGenerator(name=lowerCAmelCase__ , gen_kwargs={'files': files} ) )
return splits
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
__lowerCAmelCase : List[str] = self.config.features.arrow_schema.field(lowerCAmelCase__ ).type
__lowerCAmelCase : List[Any] = pa_table.append_column(lowerCAmelCase__ , pa.array([None] * len(lowerCAmelCase__ ) , type=lowerCAmelCase__ ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
__lowerCAmelCase : str = table_cast(lowerCAmelCase__ , self.config.features.arrow_schema )
return pa_table
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__ ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(lowerCAmelCase__ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
__lowerCAmelCase : int = json.load(lowerCAmelCase__ )
# We keep only the field we are interested in
__lowerCAmelCase : int = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(lowerCAmelCase__ , (list, tuple) ):
__lowerCAmelCase : List[Any] = set().union(*[row.keys() for row in dataset] )
__lowerCAmelCase : Optional[Any] = {col: [row.get(lowerCAmelCase__ ) for row in dataset] for col in keys}
else:
__lowerCAmelCase : Union[str, Any] = dataset
__lowerCAmelCase : List[Any] = pa.Table.from_pydict(lowerCAmelCase__ )
yield file_idx, self._cast_table(lowerCAmelCase__ )
# If the file has one json object per line
else:
with open(lowerCAmelCase__ , 'rb' ) as f:
__lowerCAmelCase : Optional[Any] = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
__lowerCAmelCase : Optional[int] = max(self.config.chunksize // 32 , 16 << 10 )
__lowerCAmelCase : Optional[Any] = (
self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
)
while True:
__lowerCAmelCase : int = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(lowerCAmelCase__ )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
__lowerCAmelCase : int = batch.decode(self.config.encoding , errors=lowerCAmelCase__ ).encode('utf-8' )
try:
while True:
try:
__lowerCAmelCase : Optional[int] = paj.read_json(
io.BytesIO(lowerCAmelCase__ ) , read_options=paj.ReadOptions(block_size=lowerCAmelCase__ ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(lowerCAmelCase__ , pa.ArrowInvalid )
and "straddling" not in str(lowerCAmelCase__ )
or block_size > len(lowerCAmelCase__ )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f"Batch of {len(lowerCAmelCase__ )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}." )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
lowerCAmelCase__ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
__lowerCAmelCase : str = json.load(lowerCAmelCase__ )
except json.JSONDecodeError:
logger.error(f"Failed to read file \'{file}\' with error {type(lowerCAmelCase__ )}: {e}" )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ): # list is the only sequence type supported in JSON
try:
__lowerCAmelCase : Optional[Any] = set().union(*[row.keys() for row in dataset] )
__lowerCAmelCase : Dict = {col: [row.get(lowerCAmelCase__ ) for row in dataset] for col in keys}
__lowerCAmelCase : Dict = pa.Table.from_pydict(lowerCAmelCase__ )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f"Failed to read file \'{file}\' with error {type(lowerCAmelCase__ )}: {e}" )
raise ValueError(f"Not able to read records in the JSON file at {file}." ) from None
yield file_idx, self._cast_table(lowerCAmelCase__ )
break
else:
logger.error(f"Failed to read file \'{file}\' with error {type(lowerCAmelCase__ )}: {e}" )
raise ValueError(
f"Not able to read records in the JSON file at {file}. "
f"You should probably indicate the field of the JSON file containing your records. "
f"This JSON file contain the following fields: {str(list(dataset.keys() ) )}. "
f"Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. " ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__ )
batch_idx += 1 | 366 |
"""simple docstring"""
import argparse
import datetime
def __lowerCAmelCase (_UpperCamelCase ):
__lowerCAmelCase : Optional[Any] = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Saturday',
}
__lowerCAmelCase : Optional[Any] = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(_UpperCamelCase ) < 11:
raise ValueError('Must be 10 characters long' )
# Get month
__lowerCAmelCase : int = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError('Month must be between 1 - 12' )
__lowerCAmelCase : str = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('Date separator must be \'-\' or \'/\'' )
# Get day
__lowerCAmelCase : int = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError('Date must be between 1 - 31' )
# Get second separator
__lowerCAmelCase : str = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('Date separator must be \'-\' or \'/\'' )
# Get year
__lowerCAmelCase : int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 8500:
raise ValueError(
'Year out of range. There has to be some sort of limit...right?' )
# Get datetime obj for validation
__lowerCAmelCase : Tuple = datetime.date(int(_UpperCamelCase ) , int(_UpperCamelCase ) , int(_UpperCamelCase ) )
# Start math
if m <= 2:
__lowerCAmelCase : int = y - 1
__lowerCAmelCase : Tuple = m + 12
# maths var
__lowerCAmelCase : int = int(str(_UpperCamelCase )[:2] )
__lowerCAmelCase : int = int(str(_UpperCamelCase )[2:] )
__lowerCAmelCase : int = int(2.6 * m - 5.39 )
__lowerCAmelCase : int = int(c / 4 )
__lowerCAmelCase : int = int(k / 4 )
__lowerCAmelCase : int = int(d + k )
__lowerCAmelCase : int = int(t + u + v + x )
__lowerCAmelCase : int = int(z - (2 * c) )
__lowerCAmelCase : int = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError('The date was evaluated incorrectly. Contact developer.' )
# Response
__lowerCAmelCase : str = F"Your date {date_input}, is a {days[str(_UpperCamelCase )]}!"
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase__ = argparse.ArgumentParser(
description=(
"""Find out what day of the week nearly any date is or was. Enter """
"""date as a string in the mm-dd-yyyy or mm/dd/yyyy format"""
)
)
parser.add_argument(
"""date_input""", type=str, help="""Date as a string (mm-dd-yyyy or mm/dd/yyyy)"""
)
lowerCamelCase__ = parser.parse_args()
zeller(args.date_input) | 182 | 0 |
def __lowercase ( lowerCamelCase : list ):
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowerCamelCase ) == 0:
raise ValueError('Input list must be a non empty list' )
if len(lowerCamelCase ) == 1:
return True
UpperCamelCase_ : Optional[Any] = series[1] - series[0]
for index in range(len(lowerCamelCase ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def __lowercase ( lowerCamelCase : list ):
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowerCamelCase ) == 0:
raise ValueError('Input list must be a non empty list' )
UpperCamelCase_ : Union[str, Any] = 0
for val in series:
answer += val
return answer / len(lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 175 | def __lowercase ( lowerCamelCase : str , lowerCamelCase : str ):
def get_matched_characters(lowerCamelCase : str , lowerCamelCase : str ) -> str:
UpperCamelCase_ : Tuple = []
UpperCamelCase_ : List[Any] = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
UpperCamelCase_ : int = int(max(0 , i - limit ) )
UpperCamelCase_ : Dict = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowerCamelCase )
UpperCamelCase_ : Dict = F"{_stra[0:_stra.index(lowerCamelCase )]} {_stra[_stra.index(lowerCamelCase ) + 1:]}"
return "".join(lowerCamelCase )
# matching characters
UpperCamelCase_ : str = get_matched_characters(lowerCamelCase , lowerCamelCase )
UpperCamelCase_ : str = get_matched_characters(lowerCamelCase , lowerCamelCase )
UpperCamelCase_ : Union[str, Any] = len(lowerCamelCase )
# transposition
UpperCamelCase_ : int = (
len([(ca, ca) for ca, ca in zip(lowerCamelCase , lowerCamelCase ) if ca != ca] ) // 2
)
if not match_count:
UpperCamelCase_ : Union[str, Any] = 0.0
else:
UpperCamelCase_ : str = (
1
/ 3
* (
match_count / len(lowerCamelCase )
+ match_count / len(lowerCamelCase )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
UpperCamelCase_ : Dict = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world'))
| 175 | 1 |
from bisect import bisect
from itertools import accumulate
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Tuple:
"""simple docstring"""
A__ = sorted(zip(_UpperCAmelCase , _UpperCAmelCase ) , key=lambda lowercase_ : x[0] / x[1] , reverse=_UpperCAmelCase )
A__ = [i[0] for i in r], [i[1] for i in r]
A__ = list(accumulate(_UpperCAmelCase ) )
A__ = bisect(_UpperCAmelCase , _UpperCAmelCase )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 351 |
from __future__ import annotations
from typing import Any
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : str , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : float = 0) ->None:
'''simple docstring'''
A__ , A__ = row, column
A__ = [[default_value for c in range(UpperCAmelCase__)] for r in range(UpperCAmelCase__)]
def __str__( self : List[str]) ->str:
'''simple docstring'''
A__ = f"""Matrix consist of {self.row} rows and {self.column} columns\n"""
# Make string identifier
A__ = 0
for row_vector in self.array:
for obj in row_vector:
A__ = max(UpperCAmelCase__ , len(str(UpperCAmelCase__)))
A__ = f"""%{max_element_length}s"""
# Make string and return
def single_line(UpperCAmelCase__ : list[float]) -> str:
nonlocal string_format_identifier
A__ = '''['''
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
line += "]"
return line
s += "\n".join(single_line(UpperCAmelCase__) for row_vector in self.array)
return s
def __repr__( self : Tuple) ->str:
'''simple docstring'''
return str(self)
def SCREAMING_SNAKE_CASE ( self : List[str] , UpperCAmelCase__ : tuple[int, int]) ->bool:
'''simple docstring'''
if not (isinstance(UpperCAmelCase__ , (list, tuple)) and len(UpperCAmelCase__) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__( self : List[Any] , UpperCAmelCase__ : tuple[int, int]) ->Any:
'''simple docstring'''
assert self.validate_indicies(UpperCAmelCase__)
return self.array[loc[0]][loc[1]]
def __setitem__( self : List[Any] , UpperCAmelCase__ : tuple[int, int] , UpperCAmelCase__ : float) ->None:
'''simple docstring'''
assert self.validate_indicies(UpperCAmelCase__)
A__ = value
def __add__( self : Optional[int] , UpperCAmelCase__ : Matrix) ->Matrix:
'''simple docstring'''
assert isinstance(UpperCAmelCase__ , UpperCAmelCase__)
assert self.row == another.row and self.column == another.column
# Add
A__ = Matrix(self.row , self.column)
for r in range(self.row):
for c in range(self.column):
A__ = self[r, c] + another[r, c]
return result
def __neg__( self : str) ->Matrix:
'''simple docstring'''
A__ = Matrix(self.row , self.column)
for r in range(self.row):
for c in range(self.column):
A__ = -self[r, c]
return result
def __sub__( self : str , UpperCAmelCase__ : Matrix) ->Matrix:
'''simple docstring'''
return self + (-another)
def __mul__( self : Union[str, Any] , UpperCAmelCase__ : int | float | Matrix) ->Matrix:
'''simple docstring'''
if isinstance(UpperCAmelCase__ , (int, float)): # Scalar multiplication
A__ = Matrix(self.row , self.column)
for r in range(self.row):
for c in range(self.column):
A__ = self[r, c] * another
return result
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__): # Matrix multiplication
assert self.column == another.row
A__ = Matrix(self.row , another.column)
for r in range(self.row):
for c in range(another.column):
for i in range(self.column):
result[r, c] += self[r, i] * another[i, c]
return result
else:
A__ = f"""Unsupported type given for another ({type(UpperCAmelCase__)})"""
raise TypeError(UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : List[str]) ->Matrix:
'''simple docstring'''
A__ = Matrix(self.column , self.row)
for r in range(self.row):
for c in range(self.column):
A__ = self[r, c]
return result
def SCREAMING_SNAKE_CASE ( self : Tuple , UpperCAmelCase__ : Matrix , UpperCAmelCase__ : Matrix) ->Any:
'''simple docstring'''
assert isinstance(UpperCAmelCase__ , UpperCAmelCase__) and isinstance(UpperCAmelCase__ , UpperCAmelCase__)
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
A__ = v.transpose()
A__ = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
A__ = Matrix(3 , 3 , 0 )
for i in range(3 ):
A__ = 1
print(f"""a^(-1) is {ainv}""" )
# u, v
A__ = Matrix(3 , 1 , 0 )
A__ , A__ , A__ = 1, 2, -3
A__ = Matrix(3 , 1 , 0 )
A__ , A__ , A__ = 4, -2, 5
print(f"""u is {u}""" )
print(f"""v is {v}""" )
print(f"""uv^T is {u * v.transpose()}""" )
# Sherman Morrison
print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(lowercase_ , lowercase_ )}""" )
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
import doctest
doctest.testmod()
testa()
| 231 | 0 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowercase__ :
'''simple docstring'''
def __init__( self , __snake_case , __snake_case=13 , __snake_case=7 , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=False , __snake_case=False , __snake_case=False , __snake_case=2 , __snake_case=99 , __snake_case=0 , __snake_case=32 , __snake_case=5 , __snake_case=4 , __snake_case=0.1 , __snake_case=0.1 , __snake_case=512 , __snake_case=2 , __snake_case=0.02 , __snake_case=2 , __snake_case=4 , __snake_case="last" , __snake_case=True , __snake_case=None , __snake_case=0 , ):
_SCREAMING_SNAKE_CASE : Optional[int] = parent
_SCREAMING_SNAKE_CASE : List[Any] = batch_size
_SCREAMING_SNAKE_CASE : Dict = seq_length
_SCREAMING_SNAKE_CASE : Union[str, Any] = is_training
_SCREAMING_SNAKE_CASE : List[Any] = use_input_lengths
_SCREAMING_SNAKE_CASE : Union[str, Any] = use_token_type_ids
_SCREAMING_SNAKE_CASE : Dict = use_labels
_SCREAMING_SNAKE_CASE : Tuple = gelu_activation
_SCREAMING_SNAKE_CASE : Union[str, Any] = sinusoidal_embeddings
_SCREAMING_SNAKE_CASE : Tuple = causal
_SCREAMING_SNAKE_CASE : Any = asm
_SCREAMING_SNAKE_CASE : List[str] = n_langs
_SCREAMING_SNAKE_CASE : Union[str, Any] = vocab_size
_SCREAMING_SNAKE_CASE : Any = n_special
_SCREAMING_SNAKE_CASE : Optional[Any] = hidden_size
_SCREAMING_SNAKE_CASE : str = num_hidden_layers
_SCREAMING_SNAKE_CASE : str = num_attention_heads
_SCREAMING_SNAKE_CASE : Any = hidden_dropout_prob
_SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
_SCREAMING_SNAKE_CASE : Any = type_sequence_label_size
_SCREAMING_SNAKE_CASE : int = initializer_range
_SCREAMING_SNAKE_CASE : Dict = num_labels
_SCREAMING_SNAKE_CASE : int = num_choices
_SCREAMING_SNAKE_CASE : int = summary_type
_SCREAMING_SNAKE_CASE : Optional[Any] = use_proj
_SCREAMING_SNAKE_CASE : Optional[Any] = scope
_SCREAMING_SNAKE_CASE : Dict = bos_token_id
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
_SCREAMING_SNAKE_CASE : int = None
if self.use_input_lengths:
_SCREAMING_SNAKE_CASE : str = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_SCREAMING_SNAKE_CASE : Union[str, Any] = None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_SCREAMING_SNAKE_CASE : Union[str, Any] = None
_SCREAMING_SNAKE_CASE : Tuple = None
_SCREAMING_SNAKE_CASE : Optional[Any] = None
if self.use_labels:
_SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size] , 2 ).float()
_SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE : List[Any] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCAmelCase_ ( self ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def UpperCAmelCase_ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ):
_SCREAMING_SNAKE_CASE : Dict = XLMModel(config=__snake_case )
model.to(__snake_case )
model.eval()
_SCREAMING_SNAKE_CASE : Union[str, Any] = model(__snake_case , lengths=__snake_case , langs=__snake_case )
_SCREAMING_SNAKE_CASE : Optional[int] = model(__snake_case , langs=__snake_case )
_SCREAMING_SNAKE_CASE : str = model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ):
_SCREAMING_SNAKE_CASE : Optional[Any] = XLMWithLMHeadModel(__snake_case )
model.to(__snake_case )
model.eval()
_SCREAMING_SNAKE_CASE : Union[str, Any] = model(__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = XLMForQuestionAnsweringSimple(__snake_case )
model.to(__snake_case )
model.eval()
_SCREAMING_SNAKE_CASE : List[Any] = model(__snake_case )
_SCREAMING_SNAKE_CASE : int = model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
_SCREAMING_SNAKE_CASE : str = outputs
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 , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ):
_SCREAMING_SNAKE_CASE : int = XLMForQuestionAnswering(__snake_case )
model.to(__snake_case )
model.eval()
_SCREAMING_SNAKE_CASE : int = model(__snake_case )
_SCREAMING_SNAKE_CASE : int = model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , p_mask=__snake_case , )
_SCREAMING_SNAKE_CASE : List[Any] = model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , )
((_SCREAMING_SNAKE_CASE) , ) : List[str] = result_with_labels.to_tuple()
_SCREAMING_SNAKE_CASE : Any = model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
((_SCREAMING_SNAKE_CASE) , ) : int = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCAmelCase_ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ):
_SCREAMING_SNAKE_CASE : Dict = XLMForSequenceClassification(__snake_case )
model.to(__snake_case )
model.eval()
_SCREAMING_SNAKE_CASE : Tuple = model(__snake_case )
_SCREAMING_SNAKE_CASE : Tuple = model(__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase_ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ):
_SCREAMING_SNAKE_CASE : Dict = self.num_labels
_SCREAMING_SNAKE_CASE : Union[str, Any] = XLMForTokenClassification(__snake_case )
model.to(__snake_case )
model.eval()
_SCREAMING_SNAKE_CASE : int = model(__snake_case , attention_mask=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase_ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ):
_SCREAMING_SNAKE_CASE : Any = self.num_choices
_SCREAMING_SNAKE_CASE : List[Any] = XLMForMultipleChoice(config=__snake_case )
model.to(__snake_case )
model.eval()
_SCREAMING_SNAKE_CASE : Tuple = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_SCREAMING_SNAKE_CASE : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_SCREAMING_SNAKE_CASE : int = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_SCREAMING_SNAKE_CASE : List[Any] = model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Optional[Any] = 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
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) : Tuple = config_and_inputs
_SCREAMING_SNAKE_CASE : str = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class lowercase__ ( _snake_case , _snake_case , _snake_case , unittest.TestCase ):
'''simple docstring'''
A_ : str = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
A_ : Any = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
A_ : Optional[Any] = (
{
"""feature-extraction""": XLMModel,
"""fill-mask""": XLMWithLMHeadModel,
"""question-answering""": XLMForQuestionAnsweringSimple,
"""text-classification""": XLMForSequenceClassification,
"""text-generation""": XLMWithLMHeadModel,
"""token-classification""": XLMForTokenClassification,
"""zero-shot""": XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCAmelCase_ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCAmelCase_ ( self , __snake_case , __snake_case , __snake_case=False ):
_SCREAMING_SNAKE_CASE : List[str] = super()._prepare_for_class(__snake_case , __snake_case , return_labels=__snake_case )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
_SCREAMING_SNAKE_CASE : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
_SCREAMING_SNAKE_CASE : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
return inputs_dict
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Optional[int] = XLMModelTester(self )
_SCREAMING_SNAKE_CASE : Dict = ConfigTester(self , config_class=__snake_case , emb_dim=37 )
def UpperCAmelCase_ ( self ):
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*__snake_case )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*__snake_case )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*__snake_case )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*__snake_case )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*__snake_case )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*__snake_case )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*__snake_case )
def UpperCAmelCase_ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ):
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_attentions in attentions] , [True] * len(__snake_case ) )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(__snake_case ):
# adds PAD dummy token
_SCREAMING_SNAKE_CASE : Any = min_length + idx + 1
_SCREAMING_SNAKE_CASE : List[str] = min_length + idx + 1
_SCREAMING_SNAKE_CASE : Optional[Any] = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(__snake_case ) )
def UpperCAmelCase_ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ):
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_hidden_states in hidden_states] , [True] * len(__snake_case ) , )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(__snake_case ):
# adds PAD dummy token
_SCREAMING_SNAKE_CASE : int = min_length + idx + 1
_SCREAMING_SNAKE_CASE : Any = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(__snake_case ) , )
pass
@slow
def UpperCAmelCase_ ( self ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE : Any = XLMModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
@require_torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Tuple = XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
model.to(__snake_case )
_SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([[14, 447]] , dtype=torch.long , device=__snake_case ) # the president
_SCREAMING_SNAKE_CASE : Union[str, Any] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
_SCREAMING_SNAKE_CASE : Optional[Any] = model.generate(__snake_case , do_sample=__snake_case )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , __snake_case )
| 200 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
UpperCAmelCase_ : Union[str, Any] = pytest.mark.integration
UpperCAmelCase_ : List[Any] = {'comet'}
UpperCAmelCase_ : int = importlib.util.find_spec('fairseq') is not None
UpperCAmelCase_ : Optional[Any] = {'code_eval'}
UpperCAmelCase_ : Optional[int] = os.name == 'nt'
UpperCAmelCase_ : Dict = {'bertscore', 'frugalscore', 'perplexity'}
UpperCAmelCase_ : Dict = importlib.util.find_spec('transformers') is not None
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@wraps(SCREAMING_SNAKE_CASE__ )
def wrapper(self , SCREAMING_SNAKE_CASE__ ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest("""\"test requires Fairseq\"""" )
else:
test_case(self , SCREAMING_SNAKE_CASE__ )
return wrapper
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@wraps(SCREAMING_SNAKE_CASE__ )
def wrapper(self , SCREAMING_SNAKE_CASE__ ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest("""\"test requires transformers\"""" )
else:
test_case(self , SCREAMING_SNAKE_CASE__ )
return wrapper
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@wraps(SCREAMING_SNAKE_CASE__ )
def wrapper(self , SCREAMING_SNAKE_CASE__ ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest("""\"test not supported on Windows\"""" )
else:
test_case(self , SCREAMING_SNAKE_CASE__ )
return wrapper
def snake_case_ ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Dict = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("""./metrics/*/""" )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
_snake_case , _snake_case , _snake_case )
@local
class lowercase__ ( parameterized.TestCase ):
'''simple docstring'''
A_ : Optional[int] = {}
A_ : Union[str, Any] = None
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning""" )
def UpperCAmelCase_ ( self , __snake_case ):
_SCREAMING_SNAKE_CASE : str = """[...]"""
_SCREAMING_SNAKE_CASE : Any = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("""metrics""" , __snake_case ) ).module_path )
_SCREAMING_SNAKE_CASE : Optional[int] = datasets.load.import_main_class(metric_module.__name__ , dataset=__snake_case )
# check parameters
_SCREAMING_SNAKE_CASE : Tuple = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(__snake_case , metric_module.__name__ ):
with self.use_local_metrics():
try:
_SCREAMING_SNAKE_CASE : int = doctest.testmod(__snake_case , verbose=__snake_case , raise_on_error=__snake_case )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def UpperCAmelCase_ ( self , __snake_case ):
_SCREAMING_SNAKE_CASE : List[Any] = """[...]"""
_SCREAMING_SNAKE_CASE : Optional[Any] = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("""metrics""" , __snake_case ) ).module_path )
# run doctest
with self.use_local_metrics():
_SCREAMING_SNAKE_CASE : List[str] = doctest.testmod(__snake_case , verbose=__snake_case , raise_on_error=__snake_case )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def UpperCAmelCase_ ( self , __snake_case , __snake_case ):
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](__snake_case ):
yield
else:
yield
@contextmanager
def UpperCAmelCase_ ( self ):
def load_local_metric(__snake_case , *__snake_case , **__snake_case ):
return load_metric(os.path.join("""metrics""" , __snake_case ) , *__snake_case , **__snake_case )
with patch("""datasets.load_metric""" ) as mock_load_metric:
_SCREAMING_SNAKE_CASE : Union[str, Any] = load_local_metric
yield
@classmethod
def UpperCAmelCase_ ( cls , __snake_case ):
def wrapper(__snake_case ):
_SCREAMING_SNAKE_CASE : Any = contextmanager(__snake_case )
_SCREAMING_SNAKE_CASE : int = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher("""bleurt""" )
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string("""sv""" , """""" , """""" ) # handle pytest cli flags
class lowercase__ ( _snake_case ):
'''simple docstring'''
def UpperCAmelCase_ ( self , __snake_case ):
assert len(input_dict["""input_ids"""] ) == 2
return np.array([1.03, 1.04] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch("""bleurt.score._create_predictor""" ) as mock_create_predictor:
_SCREAMING_SNAKE_CASE : Any = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher("""bertscore""" )
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
import torch
def bert_cos_score_idf(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(SCREAMING_SNAKE_CASE__ ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch("""bert_score.scorer.get_model""" ), patch(
"""bert_score.scorer.bert_cos_score_idf""" ) as mock_bert_cos_score_idf:
_SCREAMING_SNAKE_CASE : Any = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher("""comet""" )
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def load_from_checkpoint(SCREAMING_SNAKE_CASE__ ):
class lowercase__ :
'''simple docstring'''
def UpperCAmelCase_ ( self , __snake_case , *__snake_case , **__snake_case ):
assert len(__snake_case ) == 2
_SCREAMING_SNAKE_CASE : Dict = [0.19, 0.92]
return scores, sum(__snake_case ) / len(__snake_case )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch("""comet.download_model""" ) as mock_download_model:
_SCREAMING_SNAKE_CASE : Any = None
with patch("""comet.load_from_checkpoint""" ) as mock_load_from_checkpoint:
_SCREAMING_SNAKE_CASE : List[str] = load_from_checkpoint
yield
def snake_case_ ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Union[str, Any] = load_metric(os.path.join("""metrics""" , """seqeval""" ) )
_SCREAMING_SNAKE_CASE : List[str] = """ERROR"""
_SCREAMING_SNAKE_CASE : Tuple = f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
with pytest.raises(SCREAMING_SNAKE_CASE__ , match=re.escape(SCREAMING_SNAKE_CASE__ ) ):
metric.compute(predictions=[] , references=[] , scheme=SCREAMING_SNAKE_CASE__ )
| 200 | 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
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : int = {
'''sail/poolformer_s12''': '''https://huggingface.co/sail/poolformer_s12/resolve/main/config.json''',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class A_ ( _a ):
lowerCAmelCase__ = 'poolformer'
def __init__( self: Any ,__lowerCAmelCase: Union[str, Any]=3 ,__lowerCAmelCase: Optional[int]=16 ,__lowerCAmelCase: Union[str, Any]=16 ,__lowerCAmelCase: Optional[Any]=3 ,__lowerCAmelCase: Tuple=4.0 ,__lowerCAmelCase: Optional[Any]=[2, 2, 6, 2] ,__lowerCAmelCase: Optional[Any]=[64, 128, 320, 512] ,__lowerCAmelCase: Dict=[7, 3, 3, 3] ,__lowerCAmelCase: Dict=[4, 2, 2, 2] ,__lowerCAmelCase: Optional[int]=[2, 1, 1, 1] ,__lowerCAmelCase: Optional[int]=4 ,__lowerCAmelCase: Union[str, Any]=0.0 ,__lowerCAmelCase: Optional[Any]="gelu" ,__lowerCAmelCase: Optional[Any]=True ,__lowerCAmelCase: Optional[int]=1e-5 ,__lowerCAmelCase: Dict=0.02 ,**__lowerCAmelCase: Optional[int] ,):
'''simple docstring'''
_lowerCamelCase : int = num_channels
_lowerCamelCase : Optional[int] = patch_size
_lowerCamelCase : Union[str, Any] = stride
_lowerCamelCase : Optional[int] = padding
_lowerCamelCase : int = pool_size
_lowerCamelCase : Optional[Any] = hidden_sizes
_lowerCamelCase : Optional[Any] = mlp_ratio
_lowerCamelCase : str = depths
_lowerCamelCase : List[str] = patch_sizes
_lowerCamelCase : str = strides
_lowerCamelCase : str = num_encoder_blocks
_lowerCamelCase : Tuple = drop_path_rate
_lowerCamelCase : List[str] = hidden_act
_lowerCamelCase : str = use_layer_scale
_lowerCamelCase : List[Any] = layer_scale_init_value
_lowerCamelCase : Optional[int] = initializer_range
super().__init__(**__lowerCAmelCase )
class A_ ( _a ):
lowerCAmelCase__ = version.parse('1.11' )
@property
def _lowercase ( self: List[Any] ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _lowercase ( self: Optional[int] ):
'''simple docstring'''
return 2e-3 | 340 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase : int = str(_lowerCamelCase )
return len(_lowerCamelCase ) == 9 and set(_lowerCamelCase ) == set("123456789" )
def lowerCamelCase_( ) -> int | None:
'''simple docstring'''
for base_num in range(9999 , 4999 , -1 ):
_lowerCamelCase : Union[str, Any] = 100002 * base_num
if is_9_pandigital(_lowerCamelCase ):
return candidate
for base_num in range(333 , 99 , -1 ):
_lowerCamelCase : Tuple = 1002003 * base_num
if is_9_pandigital(_lowerCamelCase ):
return candidate
return None
if __name__ == "__main__":
print(f'''{solution() = }''') | 340 | 1 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = XLMTokenizer
__lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_lowerCAmelCase = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
_lowerCAmelCase = dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
_lowerCAmelCase = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']
_lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
_lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) )
with open(self.merges_file , """w""" ) as fp:
fp.write("""\n""".join(lowerCamelCase__ ) )
def snake_case ( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = 'lower newer'
_lowerCAmelCase = 'lower newer'
return input_text, output_text
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = XLMTokenizer(self.vocab_file , self.merges_file )
_lowerCAmelCase = 'lower'
_lowerCAmelCase = ['low', 'er</w>']
_lowerCAmelCase = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_lowerCAmelCase = tokens + ['<unk>']
_lowerCAmelCase = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , lowerCamelCase__ )
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = XLMTokenizer.from_pretrained("""xlm-mlm-en-2048""" )
_lowerCAmelCase = tokenizer.encode("""sequence builders""" , add_special_tokens=lowerCamelCase__ )
_lowerCAmelCase = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowerCamelCase__ )
_lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ )
_lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ , lowerCamelCase__ )
assert encoded_sentence == [0] + text + [1]
assert encoded_pair == [0] + text + [1] + text_a + [1]
| 82 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
snake_case_ : List[Any] = logging.get_logger(__name__)
class lowercase__ ( lowercase ):
lowercase__ = """AutoTokenizer"""
lowercase__ = ["""tokenizer"""]
lowercase__ = {
"""semantic_prompt""": 1,
"""coarse_prompt""": 2,
"""fine_prompt""": 2,
}
def __init__( self : List[str] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Tuple=None ):
'''simple docstring'''
super().__init__(lowerCamelCase__ )
_UpperCamelCase : Dict = speaker_embeddings
@classmethod
def UpperCamelCase_ ( cls : Union[str, Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : str="speaker_embeddings_path.json" ,**lowerCamelCase__ : Optional[Any] ):
'''simple docstring'''
if speaker_embeddings_dict_path is not None:
_UpperCamelCase : Optional[Any] = get_file_from_repo(
lowerCamelCase__ ,lowerCamelCase__ ,subfolder=kwargs.pop('subfolder' ,lowerCamelCase__ ) ,cache_dir=kwargs.pop('cache_dir' ,lowerCamelCase__ ) ,force_download=kwargs.pop('force_download' ,lowerCamelCase__ ) ,proxies=kwargs.pop('proxies' ,lowerCamelCase__ ) ,resume_download=kwargs.pop('resume_download' ,lowerCamelCase__ ) ,local_files_only=kwargs.pop('local_files_only' ,lowerCamelCase__ ) ,use_auth_token=kwargs.pop('use_auth_token' ,lowerCamelCase__ ) ,revision=kwargs.pop('revision' ,lowerCamelCase__ ) ,)
if speaker_embeddings_path is None:
logger.warning(
F'`{os.path.join(lowerCamelCase__ ,lowerCamelCase__ )}` does not exists\n , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.' )
_UpperCamelCase : Union[str, Any] = None
else:
with open(lowerCamelCase__ ) as speaker_embeddings_json:
_UpperCamelCase : Optional[int] = json.load(lowerCamelCase__ )
else:
_UpperCamelCase : Tuple = None
_UpperCamelCase : Tuple = AutoTokenizer.from_pretrained(lowerCamelCase__ ,**lowerCamelCase__ )
return cls(tokenizer=lowerCamelCase__ ,speaker_embeddings=lowerCamelCase__ )
def UpperCamelCase_ ( self : Tuple ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : int="speaker_embeddings_path.json" ,lowerCamelCase__ : Dict="speaker_embeddings" ,lowerCamelCase__ : bool = False ,**lowerCamelCase__ : Tuple ,):
'''simple docstring'''
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(lowerCamelCase__ ,lowerCamelCase__ ,'v2' ) ,exist_ok=lowerCamelCase__ )
_UpperCamelCase : Tuple = {}
_UpperCamelCase : Optional[Any] = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
_UpperCamelCase : Any = self._load_voice_preset(lowerCamelCase__ )
_UpperCamelCase : Union[str, Any] = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict['repo_or_path'] ,lowerCamelCase__ ,F'{prompt_key}_{key}' ) ,voice_preset[key] ,allow_pickle=lowerCamelCase__ ,)
_UpperCamelCase : List[str] = os.path.join(lowerCamelCase__ ,F'{prompt_key}_{key}.npy' )
_UpperCamelCase : str = tmp_dict
with open(os.path.join(lowerCamelCase__ ,lowerCamelCase__ ) ,'w' ) as fp:
json.dump(lowerCamelCase__ ,lowerCamelCase__ )
super().save_pretrained(lowerCamelCase__ ,lowerCamelCase__ ,**lowerCamelCase__ )
def UpperCamelCase_ ( self : Union[str, Any] ,lowerCamelCase__ : str = None ,**lowerCamelCase__ : Dict ):
'''simple docstring'''
_UpperCamelCase : Tuple = self.speaker_embeddings[voice_preset]
_UpperCamelCase : Union[str, Any] = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
F'Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].' )
_UpperCamelCase : Dict = get_file_from_repo(
self.speaker_embeddings.get('repo_or_path' ,'/' ) ,voice_preset_paths[key] ,subfolder=kwargs.pop('subfolder' ,lowerCamelCase__ ) ,cache_dir=kwargs.pop('cache_dir' ,lowerCamelCase__ ) ,force_download=kwargs.pop('force_download' ,lowerCamelCase__ ) ,proxies=kwargs.pop('proxies' ,lowerCamelCase__ ) ,resume_download=kwargs.pop('resume_download' ,lowerCamelCase__ ) ,local_files_only=kwargs.pop('local_files_only' ,lowerCamelCase__ ) ,use_auth_token=kwargs.pop('use_auth_token' ,lowerCamelCase__ ) ,revision=kwargs.pop('revision' ,lowerCamelCase__ ) ,)
if path is None:
raise ValueError(
F'`{os.path.join(self.speaker_embeddings.get("repo_or_path" ,"/" ) ,voice_preset_paths[key] )}` does not exists\n , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n embeddings.' )
_UpperCamelCase : List[str] = np.load(lowerCamelCase__ )
return voice_preset_dict
def UpperCamelCase_ ( self : Any ,lowerCamelCase__ : Optional[dict] = None ):
'''simple docstring'''
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(F'Voice preset unrecognized, missing {key} as a key.' )
if not isinstance(voice_preset[key] ,np.ndarray ):
raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.' )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.' )
def __call__( self : Any ,lowerCamelCase__ : Optional[Any]=None ,lowerCamelCase__ : Union[str, Any]=None ,lowerCamelCase__ : Any="pt" ,lowerCamelCase__ : Dict=256 ,lowerCamelCase__ : int=False ,lowerCamelCase__ : int=True ,lowerCamelCase__ : List[str]=False ,**lowerCamelCase__ : Union[str, Any] ,):
'''simple docstring'''
if voice_preset is not None and not isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
if (
isinstance(lowerCamelCase__ ,lowerCamelCase__ )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
_UpperCamelCase : Optional[int] = self._load_voice_preset(lowerCamelCase__ )
else:
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) and not voice_preset.endswith('.npz' ):
_UpperCamelCase : Tuple = voice_preset + '.npz'
_UpperCamelCase : str = np.load(lowerCamelCase__ )
if voice_preset is not None:
self._validate_voice_preset_dict(lowerCamelCase__ ,**lowerCamelCase__ )
_UpperCamelCase : Union[str, Any] = BatchFeature(data=lowerCamelCase__ ,tensor_type=lowerCamelCase__ )
_UpperCamelCase : Union[str, Any] = self.tokenizer(
lowerCamelCase__ ,return_tensors=lowerCamelCase__ ,padding='max_length' ,max_length=lowerCamelCase__ ,return_attention_mask=lowerCamelCase__ ,return_token_type_ids=lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ ,**lowerCamelCase__ ,)
if voice_preset is not None:
_UpperCamelCase : Optional[Any] = voice_preset
return encoded_text
| 83 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use CLIPImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class a ( __snake_case ):
# to overwrite at feature extractactor specific tests
_snake_case : Any = None
_snake_case : Optional[Any] = None
@property
def lowerCAmelCase_ ( self : Union[str, Any] ):
return self.feat_extract_tester.prepare_feat_extract_dict()
def lowerCAmelCase_ ( self : List[Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(A_ , """feature_size""" ) )
self.assertTrue(hasattr(A_ , """sampling_rate""" ) )
self.assertTrue(hasattr(A_ , """padding_value""" ) )
def lowerCAmelCase_ ( self : Dict ):
_UpperCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_UpperCAmelCase = feat_extract.model_input_names[0]
_UpperCAmelCase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(A_ ) == len(A_ ) for x, y in zip(A_ , processed_features[input_name] ) ) )
_UpperCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_ )
_UpperCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
_UpperCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_UpperCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def lowerCAmelCase_ ( self : Dict ):
_UpperCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_ )
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_UpperCAmelCase = feat_extract.model_input_names[0]
_UpperCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
_UpperCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_UpperCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def lowerCAmelCase_ ( self : Optional[int] ):
_UpperCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_ )
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_UpperCAmelCase = feat_extract.model_input_names[0]
_UpperCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
_UpperCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_UpperCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def lowerCAmelCase_ ( self : Dict , __lowerCAmelCase : List[str]=False ):
def _inputs_have_equal_length(__lowerCAmelCase : int ):
_UpperCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(A_ ) != length:
return False
return True
def _inputs_are_equal(__lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[int] ):
if len(A_ ) != len(A_ ):
return False
for input_slice_a, input_slice_a in zip(A_ , A_ ):
if not np.allclose(np.asarray(A_ ) , np.asarray(A_ ) , atol=1e-3 ):
return False
return True
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_UpperCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=A_ )
_UpperCAmelCase = feat_extract.model_input_names[0]
_UpperCAmelCase = BatchFeature({input_name: speech_inputs} )
_UpperCAmelCase = self.feat_extract_tester.seq_length_diff
_UpperCAmelCase = self.feat_extract_tester.max_seq_length + pad_diff
_UpperCAmelCase = self.feat_extract_tester.min_seq_length
_UpperCAmelCase = self.feat_extract_tester.batch_size
_UpperCAmelCase = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
_UpperCAmelCase = feat_extract.pad(A_ , padding=A_ )
_UpperCAmelCase = input_a[input_name]
_UpperCAmelCase = feat_extract.pad(A_ , padding="""longest""" )
_UpperCAmelCase = input_a[input_name]
_UpperCAmelCase = feat_extract.pad(A_ , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
_UpperCAmelCase = input_a[input_name]
_UpperCAmelCase = feat_extract.pad(A_ , padding="""longest""" , return_tensors="""np""" )
_UpperCAmelCase = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(A_ ):
feat_extract.pad(A_ , padding="""max_length""" )[input_name]
_UpperCAmelCase = feat_extract.pad(
A_ , padding="""max_length""" , max_length=A_ , return_tensors="""np""" )
_UpperCAmelCase = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(A_ ) )
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertTrue(_inputs_are_equal(A_ , A_ ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
_UpperCAmelCase = feat_extract.pad(A_ , pad_to_multiple_of=10 )
_UpperCAmelCase = input_a[input_name]
_UpperCAmelCase = feat_extract.pad(A_ , padding="""longest""" , pad_to_multiple_of=10 )
_UpperCAmelCase = input_a[input_name]
_UpperCAmelCase = feat_extract.pad(
A_ , padding="""max_length""" , pad_to_multiple_of=10 , max_length=A_ )
_UpperCAmelCase = input_a[input_name]
_UpperCAmelCase = feat_extract.pad(
A_ , padding="""max_length""" , pad_to_multiple_of=10 , max_length=A_ , return_tensors="""np""" , )
_UpperCAmelCase = input_a[input_name]
self.assertTrue(all(len(A_ ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(A_ , A_ ) )
_UpperCAmelCase = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(A_ ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
_UpperCAmelCase = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def lowerCAmelCase_ ( self : Union[str, Any] , __lowerCAmelCase : Tuple=False ):
def _inputs_have_equal_length(__lowerCAmelCase : Dict ):
_UpperCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(A_ ) != length:
return False
return True
def _inputs_are_equal(__lowerCAmelCase : Tuple , __lowerCAmelCase : int ):
if len(A_ ) != len(A_ ):
return False
for input_slice_a, input_slice_a in zip(A_ , A_ ):
if not np.allclose(np.asarray(A_ ) , np.asarray(A_ ) , atol=1e-3 ):
return False
return True
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_UpperCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=A_ )
_UpperCAmelCase = feat_extract.model_input_names[0]
_UpperCAmelCase = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
_UpperCAmelCase = feat_extract.pad(
A_ , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=A_ )
_UpperCAmelCase = input_a[input_name]
_UpperCAmelCase = feat_extract.pad(A_ , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
_UpperCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertFalse(_inputs_have_equal_length(A_ ) )
# truncate to smallest with np
_UpperCAmelCase = feat_extract.pad(
A_ , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=A_ , )
_UpperCAmelCase = input_a[input_name]
_UpperCAmelCase = feat_extract.pad(
A_ , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
_UpperCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(A_ ) )
# truncate to middle
_UpperCAmelCase = feat_extract.pad(
A_ , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=A_ , return_tensors="""np""" , )
_UpperCAmelCase = input_a[input_name]
_UpperCAmelCase = feat_extract.pad(
A_ , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=A_ )
_UpperCAmelCase = input_a[input_name]
_UpperCAmelCase = feat_extract.pad(
A_ , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
_UpperCAmelCase = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertTrue(_inputs_are_equal(A_ , A_ ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(A_ ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A_ ):
feat_extract.pad(A_ , truncation=A_ )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A_ ):
feat_extract.pad(A_ , padding="""longest""" , truncation=A_ )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A_ ):
feat_extract.pad(A_ , padding="""longest""" , truncation=A_ )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(A_ ):
feat_extract.pad(A_ , padding="""max_length""" , truncation=A_ )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
_UpperCAmelCase = 12
_UpperCAmelCase = feat_extract.pad(
A_ , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=A_ , truncation=A_ , )
_UpperCAmelCase = input_a[input_name]
_UpperCAmelCase = feat_extract.pad(
A_ , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=A_ , )
_UpperCAmelCase = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
_UpperCAmelCase = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
_UpperCAmelCase = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertFalse(_inputs_have_equal_length(A_ ) )
def lowerCAmelCase_ ( self : List[Any] ):
self._check_padding(numpify=A_ )
def lowerCAmelCase_ ( self : Dict ):
self._check_padding(numpify=A_ )
def lowerCAmelCase_ ( self : str ):
self._check_truncation(numpify=A_ )
def lowerCAmelCase_ ( self : List[Any] ):
self._check_truncation(numpify=A_ )
@require_torch
def lowerCAmelCase_ ( self : Dict ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_UpperCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_UpperCAmelCase = feat_extract.model_input_names[0]
_UpperCAmelCase = BatchFeature({input_name: speech_inputs} )
_UpperCAmelCase = feat_extract.pad(A_ , padding="""longest""" , return_tensors="""np""" )[input_name]
_UpperCAmelCase = feat_extract.pad(A_ , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def lowerCAmelCase_ ( self : Optional[int] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_UpperCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_UpperCAmelCase = feat_extract.model_input_names[0]
_UpperCAmelCase = BatchFeature({input_name: speech_inputs} )
_UpperCAmelCase = feat_extract.pad(A_ , padding="""longest""" , return_tensors="""np""" )[input_name]
_UpperCAmelCase = feat_extract.pad(A_ , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def lowerCAmelCase_ ( self : str ):
_UpperCAmelCase = self.feat_extract_dict
_UpperCAmelCase = True
_UpperCAmelCase = self.feature_extraction_class(**A_ )
_UpperCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_UpperCAmelCase = [len(A_ ) for x in speech_inputs]
_UpperCAmelCase = feat_extract.model_input_names[0]
_UpperCAmelCase = BatchFeature({input_name: speech_inputs} )
_UpperCAmelCase = feat_extract.pad(A_ , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , A_ )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , A_ )
def lowerCAmelCase_ ( self : Optional[Any] ):
_UpperCAmelCase = self.feat_extract_dict
_UpperCAmelCase = True
_UpperCAmelCase = self.feature_extraction_class(**A_ )
_UpperCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_UpperCAmelCase = [len(A_ ) for x in speech_inputs]
_UpperCAmelCase = feat_extract.model_input_names[0]
_UpperCAmelCase = BatchFeature({input_name: speech_inputs} )
_UpperCAmelCase = min(A_ )
_UpperCAmelCase = feat_extract.pad(
A_ , padding="""max_length""" , max_length=A_ , truncation=A_ , return_tensors="""np""" )
self.assertIn("""attention_mask""" , A_ )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 289 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
__lowerCamelCase : Union[str, Any] = pytest.mark.integration
@require_faiss
class A__ ( __snake_case ):
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = Dataset.from_dict({"filename": ["my_name-train" + "_" + str(A_ ) for x in np.arange(30 ).tolist()]} )
return dset
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : Dataset = self._create_dummy_dataset()
UpperCamelCase : List[Any] = dset.map(
lambda A_ , A_ : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=A_ , keep_in_memory=A_ )
UpperCamelCase : List[str] = dset.add_faiss_index("vecs" , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
UpperCamelCase , UpperCamelCase : Tuple = dset.get_nearest_examples("vecs" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
dset.drop_index("vecs" )
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
UpperCamelCase , UpperCamelCase : int = dset.get_nearest_examples("vecs" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=A_ ) as tmp_file:
dset.save_faiss_index("vecs" , tmp_file.name )
dset.load_faiss_index("vecs2" , tmp_file.name )
os.unlink(tmp_file.name )
UpperCamelCase , UpperCamelCase : List[str] = dset.get_nearest_examples("vecs2" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" )
dset.drop_index("vecs" )
self.assertRaises(A_ , partial(dset.get_nearest_examples , "vecs2" , np.ones(5 , dtype=np.floataa ) ) )
def __UpperCamelCase( self ):
'''simple docstring'''
from elasticsearch import Elasticsearch
UpperCamelCase : Dataset = self._create_dummy_dataset()
with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
"elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
UpperCamelCase : List[str] = {"acknowledged": True}
mocked_bulk.return_value([(True, None)] * 30 )
UpperCamelCase : List[Any] = {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
UpperCamelCase : Optional[Any] = Elasticsearch()
dset.add_elasticsearch_index("filename" , es_client=A_ )
UpperCamelCase , UpperCamelCase : List[str] = dset.get_nearest_examples("filename" , "my_name-train_29" )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
@require_faiss
class A__ ( __snake_case ):
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : Optional[int] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
UpperCamelCase : Any = np.zeros(5 , dtype=np.floataa )
UpperCamelCase : Optional[Any] = 1
UpperCamelCase , UpperCamelCase : Optional[Any] = index.search(A_ )
self.assertRaises(A_ , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
UpperCamelCase : Optional[int] = np.eye(5 , dtype=np.floataa )[::-1]
UpperCamelCase , UpperCamelCase : Tuple = index.search_batch(A_ )
self.assertRaises(A_ , index.search_batch , queries[0] )
UpperCamelCase : Optional[int] = [scores[0] for scores in total_scores]
UpperCamelCase : Tuple = [indices[0] for indices in total_indices]
self.assertGreater(np.min(A_ ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : List[str] = FaissIndex(string_factory="Flat" )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
UpperCamelCase : List[str] = FaissIndex(string_factory="LSH" )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(A_ ):
UpperCamelCase : List[str] = FaissIndex(string_factory="Flat" , custom_index=faiss.IndexFlat(5 ) )
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : Dict = faiss.IndexFlat(5 )
UpperCamelCase : Union[str, Any] = FaissIndex(custom_index=A_ )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : str = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=A_ ) as tmp_file:
index.save(tmp_file.name )
UpperCamelCase : int = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
UpperCamelCase : str = np.zeros(5 , dtype=np.floataa )
UpperCamelCase : int = 1
UpperCamelCase , UpperCamelCase : Dict = index.search(A_ )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def A_ ( _lowerCAmelCase ) -> Optional[int]:
import faiss
UpperCamelCase : Union[str, Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
UpperCamelCase : List[Any] = "index.faiss"
UpperCamelCase : List[str] = F"""mock://{index_name}"""
index.save(_lowerCAmelCase , storage_options=mockfs.storage_options )
UpperCamelCase : List[str] = FaissIndex.load(_lowerCAmelCase , storage_options=mockfs.storage_options )
UpperCamelCase : List[str] = np.zeros(5 , dtype=np.floataa )
UpperCamelCase : Optional[int] = 1
UpperCamelCase , UpperCamelCase : List[str] = index.search(_lowerCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class A__ ( __snake_case ):
def __UpperCamelCase( self ):
'''simple docstring'''
from elasticsearch import Elasticsearch
with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
"elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
UpperCamelCase : List[str] = Elasticsearch()
UpperCamelCase : Union[str, Any] = {"acknowledged": True}
UpperCamelCase : Union[str, Any] = ElasticSearchIndex(es_client=A_ )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(["foo", "bar", "foobar"] )
# single query
UpperCamelCase : str = "foo"
UpperCamelCase : Dict = {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
UpperCamelCase , UpperCamelCase : Tuple = index.search(A_ )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
UpperCamelCase : Dict = "foo"
UpperCamelCase : Optional[Any] = {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
UpperCamelCase , UpperCamelCase : str = index.search(A_ , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
UpperCamelCase : Dict = ["foo", "bar", "foobar"]
UpperCamelCase : List[Any] = {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
UpperCamelCase , UpperCamelCase : Optional[int] = index.search_batch(A_ )
UpperCamelCase : str = [scores[0] for scores in total_scores]
UpperCamelCase : Optional[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(A_ ) , 0 )
self.assertListEqual([1, 1, 1] , A_ )
# batched queries with timeout
UpperCamelCase : int = ["foo", "bar", "foobar"]
UpperCamelCase : List[Any] = {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
UpperCamelCase , UpperCamelCase : Union[str, Any] = index.search_batch(A_ , request_timeout=30 )
UpperCamelCase : Union[str, Any] = [scores[0] for scores in total_scores]
UpperCamelCase : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(A_ ) , 0 )
self.assertListEqual([1, 1, 1] , A_ )
| 52 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class a_ ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
lowercase = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
lowercase = (
{
'''feature-extraction''': TFMobileBertModel,
'''fill-mask''': TFMobileBertForMaskedLM,
'''question-answering''': TFMobileBertForQuestionAnswering,
'''text-classification''': TFMobileBertForSequenceClassification,
'''token-classification''': TFMobileBertForTokenClassification,
'''zero-shot''': TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
lowercase = False
lowercase = False
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> int:
"""simple docstring"""
UpperCamelCase = super()._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ , return_labels=lowerCAmelCase_ )
if return_labels:
if model_class in get_values(lowerCAmelCase_ ):
UpperCamelCase = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class a_ ( __lowerCAmelCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=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=512 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=None , ) -> List[str]:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_input_mask
UpperCamelCase = use_token_type_ids
UpperCamelCase = use_labels
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = num_labels
UpperCamelCase = num_choices
UpperCamelCase = scope
UpperCamelCase = embedding_size
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_input_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase = None
if self.use_token_type_ids:
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase = MobileBertConfig(
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 , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = TFMobileBertModel(config=lowerCAmelCase_ )
UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCamelCase = model(lowerCAmelCase_ )
UpperCamelCase = [input_ids, input_mask]
UpperCamelCase = model(lowerCAmelCase_ )
UpperCamelCase = model(lowerCAmelCase_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
UpperCamelCase = TFMobileBertForMaskedLM(config=lowerCAmelCase_ )
UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCamelCase = model(lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = TFMobileBertForNextSentencePrediction(config=lowerCAmelCase_ )
UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCamelCase = model(lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
UpperCamelCase = TFMobileBertForPreTraining(config=lowerCAmelCase_ )
UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCamelCase = model(lowerCAmelCase_ )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = TFMobileBertForSequenceClassification(config=lowerCAmelCase_ )
UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCamelCase = model(lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
UpperCamelCase = self.num_choices
UpperCamelCase = TFMobileBertForMultipleChoice(config=lowerCAmelCase_ )
UpperCamelCase = tf.tile(tf.expand_dims(lowerCAmelCase_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase = tf.tile(tf.expand_dims(lowerCAmelCase_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase = tf.tile(tf.expand_dims(lowerCAmelCase_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
UpperCamelCase = model(lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = TFMobileBertForTokenClassification(config=lowerCAmelCase_ )
UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCamelCase = model(lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
UpperCamelCase = TFMobileBertForQuestionAnswering(config=lowerCAmelCase_ )
UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCamelCase = model(lowerCAmelCase_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = TFMobileBertModelTest.TFMobileBertModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=lowerCAmelCase_ , hidden_size=37 )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*lowerCAmelCase_ )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCAmelCase_ )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCAmelCase_ )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCAmelCase_ )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCAmelCase_ )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCAmelCase_ )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCAmelCase_ )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCAmelCase_ )
@slow
def A__ ( self ) -> List[Any]:
"""simple docstring"""
for model_name in ["google/mobilebert-uncased"]:
UpperCamelCase = TFMobileBertModel.from_pretrained(lowerCAmelCase_ )
self.assertIsNotNone(lowerCAmelCase_ )
@require_tf
class a_ ( unittest.TestCase ):
@slow
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = TFMobileBertForPreTraining.from_pretrained("""google/mobilebert-uncased""" )
UpperCamelCase = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCamelCase = model(lowerCAmelCase_ )[0]
UpperCamelCase = [1, 6, 30522]
self.assertEqual(output.shape , lowerCAmelCase_ )
UpperCamelCase = tf.constant(
[
[
[-4.5_9_1_9_5_4_7, -9.2_4_8_2_9_5, -9.6_4_5_2_5_6],
[-6.7_3_0_6_1_7_5, -6.4_4_0_2_8_4, -6.6_0_5_2_8_3_7],
[-7.2_7_4_3_5_0_6, -6.7_8_4_7_9_1_5, -6.0_2_4_6_7_3],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , lowerCAmelCase_ , atol=1e-4 )
| 365 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
SCREAMING_SNAKE_CASE__ = threading.Lock()
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = {
'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL,
}
SCREAMING_SNAKE_CASE__ = logging.WARNING
SCREAMING_SNAKE_CASE__ = True
def lowercase__ ( )-> Optional[int]:
UpperCamelCase = os.getenv("""TRANSFORMERS_VERBOSITY""" , __UpperCamelCase )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F"Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, "
F"has to be one of: { ', '.join(log_levels.keys() ) }" )
return _default_log_level
def lowercase__ ( )-> str:
return __name__.split(""".""" )[0]
def lowercase__ ( )-> logging.Logger:
return logging.getLogger(_get_library_name() )
def lowercase__ ( )-> None:
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
UpperCamelCase = logging.StreamHandler() # Set sys.stderr as stream.
UpperCamelCase = sys.stderr.flush
# Apply our default configuration to the library root logger.
UpperCamelCase = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
UpperCamelCase = False
def lowercase__ ( )-> None:
global _default_handler
with _lock:
if not _default_handler:
return
UpperCamelCase = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
UpperCamelCase = None
def lowercase__ ( )-> Tuple:
return log_levels
def lowercase__ ( __UpperCamelCase = None )-> logging.Logger:
if name is None:
UpperCamelCase = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(__UpperCamelCase )
def lowercase__ ( )-> int:
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def lowercase__ ( __UpperCamelCase )-> None:
_configure_library_root_logger()
_get_library_root_logger().setLevel(__UpperCamelCase )
def lowercase__ ( )-> Tuple:
return set_verbosity(__UpperCamelCase )
def lowercase__ ( )-> Union[str, Any]:
return set_verbosity(__UpperCamelCase )
def lowercase__ ( )-> Optional[int]:
return set_verbosity(__UpperCamelCase )
def lowercase__ ( )-> Tuple:
return set_verbosity(__UpperCamelCase )
def lowercase__ ( )-> None:
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def lowercase__ ( )-> None:
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def lowercase__ ( __UpperCamelCase )-> None:
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(__UpperCamelCase )
def lowercase__ ( __UpperCamelCase )-> None:
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(__UpperCamelCase )
def lowercase__ ( )-> None:
_configure_library_root_logger()
UpperCamelCase = False
def lowercase__ ( )-> None:
_configure_library_root_logger()
UpperCamelCase = True
def lowercase__ ( )-> None:
UpperCamelCase = _get_library_root_logger().handlers
for handler in handlers:
UpperCamelCase = logging.Formatter("""[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s""" )
handler.setFormatter(__UpperCamelCase )
def lowercase__ ( )-> None:
UpperCamelCase = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(__UpperCamelCase )
def lowercase__ ( self , *__UpperCamelCase , **__UpperCamelCase )-> Tuple:
UpperCamelCase = os.getenv("""TRANSFORMERS_NO_ADVISORY_WARNINGS""" , __UpperCamelCase )
if no_advisory_warnings:
return
self.warning(*__UpperCamelCase , **__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = warning_advice
@functools.lru_cache(__UpperCamelCase )
def lowercase__ ( self , *__UpperCamelCase , **__UpperCamelCase )-> Optional[Any]:
self.warning(*__UpperCamelCase , **__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = warning_once
class a_ :
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Optional[int]: # pylint: disable=unused-argument
"""simple docstring"""
UpperCamelCase = args[0] if args else None
def __iter__( self ) -> List[Any]:
"""simple docstring"""
return iter(self._iterator )
def __getattr__( self , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
def empty_fn(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ) -> Dict:
"""simple docstring"""
return self
def __exit__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
return
class a_ :
def __call__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
else:
return EmptyTqdm(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def A__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
SCREAMING_SNAKE_CASE__ = _tqdm_cls()
def lowercase__ ( )-> bool:
global _tqdm_active
return bool(_tqdm_active )
def lowercase__ ( )-> Optional[Any]:
global _tqdm_active
UpperCamelCase = True
hf_hub_utils.enable_progress_bars()
def lowercase__ ( )-> str:
global _tqdm_active
UpperCamelCase = False
hf_hub_utils.disable_progress_bars()
| 183 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
UpperCamelCase__ = 1_0_0
UpperCamelCase__ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCamelCase__ = 4_2
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def lowerCAmelCase_ ( __A ) -> set[int]:
'''simple docstring'''
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
UpperCAmelCase__ = set()
UpperCAmelCase__ = 42
UpperCAmelCase__ = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def lowerCAmelCase_ ( __A = 5_000 ) -> int | None:
'''simple docstring'''
for number_to_partition in range(1, _lowerCamelCase ):
if len(partition(_lowerCamelCase ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(f'''{solution() = }''')
| 65 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A = logging.get_logger(__name__)
class _snake_case ( a__ ):
snake_case__ = ["input_features", "attention_mask"]
def __init__( self : Union[str, Any] , UpperCAmelCase : Tuple=80 , UpperCAmelCase : Tuple=16000 , UpperCAmelCase : Any=80 , UpperCAmelCase : Dict=0.0 , UpperCAmelCase : Optional[int]=True , UpperCAmelCase : Tuple=True , UpperCAmelCase : Tuple=True , **UpperCAmelCase : Optional[int] , ):
super().__init__(feature_size=UpperCAmelCase , sampling_rate=UpperCAmelCase , padding_value=UpperCAmelCase , **UpperCAmelCase )
__lowerCamelCase : str = num_mel_bins
__lowerCamelCase : Tuple = do_ceptral_normalize
__lowerCamelCase : Dict = normalize_means
__lowerCamelCase : str = normalize_vars
__lowerCamelCase : Optional[int] = True
def lowerCamelCase__ ( self : Optional[int] , UpperCAmelCase : np.ndarray , ):
__lowerCamelCase : Any = waveform * (2**15) # Kaldi compliance: 16-bit signed integers
__lowerCamelCase : Optional[int] = torch.from_numpy(UpperCAmelCase ).unsqueeze(0 )
__lowerCamelCase : str = ta_kaldi.fbank(UpperCAmelCase , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def lowerCamelCase__ ( UpperCAmelCase : np.ndarray , UpperCAmelCase : int , UpperCAmelCase : Optional[bool] = True , UpperCAmelCase : Optional[bool] = True , UpperCAmelCase : float = 0.0 , ):
# make sure we normalize float32 arrays
if normalize_means:
__lowerCamelCase : Any = x[:input_length].mean(axis=0 )
__lowerCamelCase : Optional[int] = np.subtract(UpperCAmelCase , UpperCAmelCase )
if normalize_vars:
__lowerCamelCase : int = x[:input_length].std(axis=0 )
__lowerCamelCase : Union[str, Any] = np.divide(UpperCAmelCase , UpperCAmelCase )
if input_length < x.shape[0]:
__lowerCamelCase : Any = padding_value
# make sure array is in float32
__lowerCamelCase : List[str] = x.astype(np.floataa )
return x
def lowerCamelCase__ ( self : Union[str, Any] , UpperCAmelCase : List[np.ndarray] , UpperCAmelCase : Optional[np.ndarray] = None ):
__lowerCamelCase : Any = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(UpperCAmelCase , UpperCAmelCase , self.normalize_means , self.normalize_vars , self.padding_value )
for x, n in zip(UpperCAmelCase , UpperCAmelCase )
]
def __call__( self : Optional[Any] , UpperCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : bool = False , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : Optional[Union[str, TensorType]] = None , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : Optional[bool] = None , **UpperCAmelCase : Dict , ):
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 `raw_speech` 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." )
__lowerCamelCase : Optional[int] = isinstance(UpperCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
__lowerCamelCase : Tuple = is_batched_numpy or (
isinstance(UpperCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__lowerCamelCase : Dict = [np.asarray(UpperCAmelCase , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(UpperCAmelCase , np.ndarray ):
__lowerCamelCase : Optional[int] = np.asarray(UpperCAmelCase , dtype=np.floataa )
elif isinstance(UpperCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__lowerCamelCase : Optional[Any] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__lowerCamelCase : Optional[int] = [raw_speech]
# extract fbank features
__lowerCamelCase : Optional[Any] = [self._extract_fbank_features(UpperCAmelCase ) for waveform in raw_speech]
# convert into correct format for padding
__lowerCamelCase : Dict = BatchFeature({"input_features": features} )
__lowerCamelCase : Optional[Any] = self.pad(
UpperCAmelCase , padding=UpperCAmelCase , max_length=UpperCAmelCase , truncation=UpperCAmelCase , pad_to_multiple_of=UpperCAmelCase , return_attention_mask=UpperCAmelCase , **UpperCAmelCase , )
# make sure list is in array format
__lowerCamelCase : Tuple = padded_inputs.get("input_features" )
if isinstance(input_features[0] , UpperCAmelCase ):
__lowerCamelCase : List[str] = [np.asarray(UpperCAmelCase , dtype=np.floataa ) for feature in input_features]
__lowerCamelCase : Optional[int] = padded_inputs.get("attention_mask" )
if attention_mask is not None:
__lowerCamelCase : Union[str, Any] = [np.asarray(UpperCAmelCase , dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
__lowerCamelCase : Optional[int] = (
np.array(UpperCAmelCase , dtype=np.intaa )
if self._get_padding_strategies(UpperCAmelCase , max_length=UpperCAmelCase ) is not PaddingStrategy.DO_NOT_PAD
else None
)
__lowerCamelCase : Optional[int] = self.normalize(
padded_inputs["input_features"] , attention_mask=UpperCAmelCase )
if return_tensors is not None:
__lowerCamelCase : Optional[Any] = padded_inputs.convert_to_tensors(UpperCAmelCase )
return padded_inputs | 135 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def _snake_case ( _SCREAMING_SNAKE_CASE : str = "AAPL" ) -> str:
lowerCAmelCase = f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
lowerCAmelCase = BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text , """html.parser""" )
lowerCAmelCase = """My(6px) Pos(r) smartphone_Mt(6px)"""
return soup.find("""div""" , class_=class_ ).find("""span""" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''') | 354 |
'''simple docstring'''
from __future__ import annotations
import pandas as pd
def _snake_case ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
lowerCAmelCase = [0] * no_of_processes
lowerCAmelCase = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase = burst_time[i]
lowerCAmelCase = 0
lowerCAmelCase = 0
lowerCAmelCase = 999_999_999
lowerCAmelCase = 0
lowerCAmelCase = False
# Process until all processes are completed
while complete != no_of_processes:
for j in range(_SCREAMING_SNAKE_CASE ):
if arrival_time[j] <= increment_time and remaining_time[j] > 0:
if remaining_time[j] < minm:
lowerCAmelCase = remaining_time[j]
lowerCAmelCase = j
lowerCAmelCase = True
if not check:
increment_time += 1
continue
remaining_time[short] -= 1
lowerCAmelCase = remaining_time[short]
if minm == 0:
lowerCAmelCase = 999_999_999
if remaining_time[short] == 0:
complete += 1
lowerCAmelCase = False
# Find finish time of current process
lowerCAmelCase = increment_time + 1
# Calculate waiting time
lowerCAmelCase = finish_time - arrival_time[short]
lowerCAmelCase = finar - burst_time[short]
if waiting_time[short] < 0:
lowerCAmelCase = 0
# Increment time
increment_time += 1
return waiting_time
def _snake_case ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[int] ) -> list[int]:
"""simple docstring"""
lowerCAmelCase = [0] * no_of_processes
for i in range(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase = burst_time[i] + waiting_time[i]
return turn_around_time
def _snake_case ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ) -> None:
"""simple docstring"""
lowerCAmelCase = 0
lowerCAmelCase = 0
for i in range(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase = total_waiting_time + waiting_time[i]
lowerCAmelCase = total_turn_around_time + turn_around_time[i]
print(f'Average waiting time = {total_waiting_time / no_of_processes:.5f}' )
print("""Average turn around time =""" , total_turn_around_time / no_of_processes )
if __name__ == "__main__":
print('Enter how many process you want to analyze')
UpperCAmelCase = int(input())
UpperCAmelCase = [0] * no_of_processes
UpperCAmelCase = [0] * no_of_processes
UpperCAmelCase = list(range(1, no_of_processes + 1))
for i in range(no_of_processes):
print('Enter the arrival time and burst time for process:--' + str(i + 1))
UpperCAmelCase , UpperCAmelCase = map(int, input().split())
UpperCAmelCase = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
UpperCAmelCase = burst_time
UpperCAmelCase = no_of_processes
UpperCAmelCase = waiting_time
UpperCAmelCase = calculate_turnaroundtime(bt, n, wt)
calculate_average_times(waiting_time, turn_around_time, no_of_processes)
UpperCAmelCase = pd.DataFrame(
list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
columns=[
'Process',
'BurstTime',
'ArrivalTime',
'WaitingTime',
'TurnAroundTime',
],
)
# Printing the dataFrame
pd.set_option('display.max_rows', fcfs.shape[0] + 1)
print(fcfs) | 187 | 0 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE ):
_a = 42
_a = 42
if is_transformers_available() and is_torch_available():
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 205 |
import argparse
import os
import re
lowercase_ = 'src/transformers'
# Pattern that looks at the indentation in a line.
lowercase_ = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase_ = re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowercase_ = re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
lowercase_ = re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowercase_ = re.compile(R'\[([^\]]+)\]')
def a ( A__ : Dict ) -> Optional[Any]:
"""simple docstring"""
_lowercase =_re_indent.search(A__ )
return "" if search is None else search.groups()[0]
def a ( A__ : Optional[Any] , A__ : Dict="" , A__ : Union[str, Any]=None , A__ : Tuple=None ) -> Dict:
"""simple docstring"""
_lowercase =0
_lowercase =code.split('\n' )
if start_prompt is not None:
while not lines[index].startswith(A__ ):
index += 1
_lowercase =['\n'.join(lines[:index] )]
else:
_lowercase =[]
# We split into blocks until we get to the `end_prompt` (or the end of the block).
_lowercase =[lines[index]]
index += 1
while index < len(A__ ) and (end_prompt is None or not lines[index].startswith(A__ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(A__ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + ' ' ):
current_block.append(lines[index] )
blocks.append('\n'.join(A__ ) )
if index < len(A__ ) - 1:
_lowercase =[lines[index + 1]]
index += 1
else:
_lowercase =[]
else:
blocks.append('\n'.join(A__ ) )
_lowercase =[lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(A__ ) > 0:
blocks.append('\n'.join(A__ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(A__ ):
blocks.append('\n'.join(lines[index:] ) )
return blocks
def a ( A__ : int ) -> Union[str, Any]:
"""simple docstring"""
def _inner(A__ : Any ):
return key(A__ ).lower().replace('_' , '' )
return _inner
def a ( A__ : Any , A__ : Union[str, Any]=None ) -> int:
"""simple docstring"""
def noop(A__ : Optional[int] ):
return x
if key is None:
_lowercase =noop
# Constants are all uppercase, they go first.
_lowercase =[obj for obj in objects if key(A__ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
_lowercase =[obj for obj in objects if key(A__ )[0].isupper() and not key(A__ ).isupper()]
# Functions begin with a lowercase, they go last.
_lowercase =[obj for obj in objects if not key(A__ )[0].isupper()]
_lowercase =ignore_underscore(A__ )
return sorted(A__ , key=A__ ) + sorted(A__ , key=A__ ) + sorted(A__ , key=A__ )
def a ( A__ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
def _replace(A__ : Optional[int] ):
_lowercase =match.groups()[0]
if "," not in imports:
return F'''[{imports}]'''
_lowercase =[part.strip().replace('"' , '' ) for part in imports.split(',' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
_lowercase =keys[:-1]
return "[" + ", ".join([F'''"{k}"''' for k in sort_objects(A__ )] ) + "]"
_lowercase =import_statement.split('\n' )
if len(A__ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
_lowercase =2 if lines[1].strip() == '[' else 1
_lowercase =[(i, _re_strip_line.search(A__ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
_lowercase =sort_objects(A__ , key=lambda A__ : x[1] )
_lowercase =[lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(A__ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
_lowercase =_re_bracket_content.sub(_replace , lines[1] )
else:
_lowercase =[part.strip().replace('"' , '' ) for part in lines[1].split(',' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
_lowercase =keys[:-1]
_lowercase =get_indent(lines[1] ) + ', '.join([F'''"{k}"''' for k in sort_objects(A__ )] )
return "\n".join(A__ )
else:
# Finally we have to deal with imports fitting on one line
_lowercase =_re_bracket_content.sub(_replace , A__ )
return import_statement
def a ( A__ : Dict , A__ : int=True ) -> Optional[Any]:
"""simple docstring"""
with open(A__ , encoding='utf-8' ) as f:
_lowercase =f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
_lowercase =split_code_in_indented_blocks(
A__ , start_prompt='_import_structure = {' , end_prompt='if TYPE_CHECKING:' )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(A__ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
_lowercase =main_blocks[block_idx]
_lowercase =block.split('\n' )
# Get to the start of the imports.
_lowercase =0
while line_idx < len(A__ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
_lowercase =len(A__ )
else:
line_idx += 1
if line_idx >= len(A__ ):
continue
# Ignore beginning and last line: they don't contain anything.
_lowercase ='\n'.join(block_lines[line_idx:-1] )
_lowercase =get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
_lowercase =split_code_in_indented_blocks(A__ , indent_level=A__ )
# We have two categories of import key: list or _import_structure[key].append/extend
_lowercase =_re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
_lowercase =[(pattern.search(A__ ).groups()[0] if pattern.search(A__ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
_lowercase =[(i, key) for i, key in enumerate(A__ ) if key is not None]
_lowercase =[x[0] for x in sorted(A__ , key=lambda A__ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
_lowercase =0
_lowercase =[]
for i in range(len(A__ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
_lowercase =sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(A__ )
count += 1
# And we put our main block back together with its first and last line.
_lowercase ='\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(A__ ):
if check_only:
return True
else:
print(F'''Overwriting {file}.''' )
with open(A__ , 'w' , encoding='utf-8' ) as f:
f.write('\n'.join(A__ ) )
def a ( A__ : List[Any]=True ) -> List[str]:
"""simple docstring"""
_lowercase =[]
for root, _, files in os.walk(A__ ):
if "__init__.py" in files:
_lowercase =sort_imports(os.path.join(A__ , '__init__.py' ) , check_only=A__ )
if result:
_lowercase =[os.path.join(A__ , '__init__.py' )]
if len(A__ ) > 0:
raise ValueError(F'''Would overwrite {len(A__ )} files, run `make style`.''' )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
lowercase_ = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 205 | 1 |
from __future__ import annotations
from typing import Any
class __a:
"""simple docstring"""
def __init__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = 0 ) -> None:
UpperCAmelCase_ : int = row, column
UpperCAmelCase_ : Optional[Any] = [[default_value for c in range(_SCREAMING_SNAKE_CASE )] for r in range(_SCREAMING_SNAKE_CASE )]
def __str__( self ) -> str:
UpperCAmelCase_ : str = f'''Matrix consist of {self.row} rows and {self.column} columns\n'''
# Make string identifier
UpperCAmelCase_ : Optional[Any] = 0
for row_vector in self.array:
for obj in row_vector:
UpperCAmelCase_ : Optional[int] = max(_SCREAMING_SNAKE_CASE ,len(str(_SCREAMING_SNAKE_CASE ) ) )
UpperCAmelCase_ : Dict = f'''%{max_element_length}s'''
# Make string and return
def single_line(_SCREAMING_SNAKE_CASE ) -> str:
nonlocal string_format_identifier
UpperCAmelCase_ : Optional[Any] = '''['''
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
line += "]"
return line
s += "\n".join(single_line(_SCREAMING_SNAKE_CASE ) for row_vector in self.array )
return s
def __repr__( self ) -> str:
return str(self )
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> bool:
if not (isinstance(_SCREAMING_SNAKE_CASE ,(list, tuple) ) and len(_SCREAMING_SNAKE_CASE ) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__( self ,_SCREAMING_SNAKE_CASE ) -> Any:
assert self.validate_indicies(_SCREAMING_SNAKE_CASE )
return self.array[loc[0]][loc[1]]
def __setitem__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> None:
assert self.validate_indicies(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : int = value
def __add__( self ,_SCREAMING_SNAKE_CASE ) -> Matrix:
assert isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
assert self.row == another.row and self.column == another.column
# Add
UpperCAmelCase_ : Tuple = Matrix(self.row ,self.column )
for r in range(self.row ):
for c in range(self.column ):
UpperCAmelCase_ : Optional[Any] = self[r, c] + another[r, c]
return result
def __neg__( self ) -> Matrix:
UpperCAmelCase_ : int = Matrix(self.row ,self.column )
for r in range(self.row ):
for c in range(self.column ):
UpperCAmelCase_ : Dict = -self[r, c]
return result
def __sub__( self ,_SCREAMING_SNAKE_CASE ) -> Matrix:
return self + (-another)
def __mul__( self ,_SCREAMING_SNAKE_CASE ) -> Matrix:
if isinstance(_SCREAMING_SNAKE_CASE ,(int, float) ): # Scalar multiplication
UpperCAmelCase_ : Tuple = Matrix(self.row ,self.column )
for r in range(self.row ):
for c in range(self.column ):
UpperCAmelCase_ : List[Any] = self[r, c] * another
return result
elif isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ): # Matrix multiplication
assert self.column == another.row
UpperCAmelCase_ : Union[str, Any] = Matrix(self.row ,another.column )
for r in range(self.row ):
for c in range(another.column ):
for i in range(self.column ):
result[r, c] += self[r, i] * another[i, c]
return result
else:
UpperCAmelCase_ : str = f'''Unsupported type given for another ({type(_SCREAMING_SNAKE_CASE )})'''
raise TypeError(_SCREAMING_SNAKE_CASE )
def a__ ( self ) -> Matrix:
UpperCAmelCase_ : List[Any] = Matrix(self.column ,self.row )
for r in range(self.row ):
for c in range(self.column ):
UpperCAmelCase_ : int = self[r, c]
return result
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Any:
assert isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) and isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
UpperCAmelCase_ : List[str] = v.transpose()
UpperCAmelCase_ : int = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = Matrix(3 , 3 , 0 )
for i in range(3 ):
UpperCAmelCase_ : Any = 1
print(f'''a^(-1) is {ainv}''' )
# u, v
UpperCAmelCase_ : List[str] = Matrix(3 , 1 , 0 )
UpperCAmelCase_ : List[Any] = 1, 2, -3
UpperCAmelCase_ : List[Any] = Matrix(3 , 1 , 0 )
UpperCAmelCase_ : str = 4, -2, 5
print(f'''u is {u}''' )
print(f'''v is {v}''' )
print(f'''uv^T is {u * v.transpose()}''' )
# Sherman Morrison
print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(_lowercase , _lowercase )}''' )
def lowerCamelCase__ ( ):
'''simple docstring'''
import doctest
doctest.testmod()
testa() | 360 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class __a( unittest.TestCase ):
"""simple docstring"""
def a__ ( self ) -> Optional[int]:
UpperCAmelCase_ : Optional[int] = tempfile.mkdtemp()
UpperCAmelCase_ : str = BlipImageProcessor()
UpperCAmelCase_ : Dict = GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''' )
UpperCAmelCase_ : Optional[Any] = BlipaProcessor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
def a__ ( self ,**_SCREAMING_SNAKE_CASE ) -> Optional[int]:
return AutoProcessor.from_pretrained(self.tmpdirname ,**_SCREAMING_SNAKE_CASE ).tokenizer
def a__ ( self ,**_SCREAMING_SNAKE_CASE ) -> Dict:
return AutoProcessor.from_pretrained(self.tmpdirname ,**_SCREAMING_SNAKE_CASE ).image_processor
def a__ ( self ) -> List[Any]:
shutil.rmtree(self.tmpdirname )
def a__ ( self ) -> Tuple:
UpperCAmelCase_ : Tuple = [np.random.randint(255 ,size=(3, 30, 400) ,dtype=np.uinta )]
UpperCAmelCase_ : Optional[Any] = [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def a__ ( self ) -> List[str]:
UpperCAmelCase_ : Dict = BlipaProcessor(tokenizer=self.get_tokenizer() ,image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase_ : str = self.get_tokenizer(bos_token='''(BOS)''' ,eos_token='''(EOS)''' )
UpperCAmelCase_ : int = self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE ,padding_value=1.0 )
UpperCAmelCase_ : Union[str, Any] = BlipaProcessor.from_pretrained(
self.tmpdirname ,bos_token='''(BOS)''' ,eos_token='''(EOS)''' ,do_normalize=_SCREAMING_SNAKE_CASE ,padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer ,_SCREAMING_SNAKE_CASE )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,_SCREAMING_SNAKE_CASE )
def a__ ( self ) -> Any:
UpperCAmelCase_ : Dict = self.get_image_processor()
UpperCAmelCase_ : Any = self.get_tokenizer()
UpperCAmelCase_ : str = BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,image_processor=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Union[str, Any] = self.prepare_image_inputs()
UpperCAmelCase_ : Optional[Any] = image_processor(_SCREAMING_SNAKE_CASE ,return_tensors='''np''' )
UpperCAmelCase_ : int = processor(images=_SCREAMING_SNAKE_CASE ,return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() ,input_processor[key].sum() ,delta=1e-2 )
def a__ ( self ) -> int:
UpperCAmelCase_ : str = self.get_image_processor()
UpperCAmelCase_ : List[Any] = self.get_tokenizer()
UpperCAmelCase_ : Any = BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,image_processor=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Tuple = '''lower newer'''
UpperCAmelCase_ : Optional[int] = processor(text=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : int = tokenizer(_SCREAMING_SNAKE_CASE ,return_token_type_ids=_SCREAMING_SNAKE_CASE )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def a__ ( self ) -> Optional[int]:
UpperCAmelCase_ : str = self.get_image_processor()
UpperCAmelCase_ : List[Any] = self.get_tokenizer()
UpperCAmelCase_ : Tuple = BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,image_processor=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : int = '''lower newer'''
UpperCAmelCase_ : int = self.prepare_image_inputs()
UpperCAmelCase_ : List[str] = processor(text=_SCREAMING_SNAKE_CASE ,images=_SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) ,['''pixel_values''', '''input_ids''', '''attention_mask'''] )
# test if it raises when no input is passed
with pytest.raises(_SCREAMING_SNAKE_CASE ):
processor()
def a__ ( self ) -> Optional[int]:
UpperCAmelCase_ : Tuple = self.get_image_processor()
UpperCAmelCase_ : Dict = self.get_tokenizer()
UpperCAmelCase_ : List[str] = BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,image_processor=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Optional[Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase_ : List[str] = processor.batch_decode(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Union[str, Any] = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
def a__ ( self ) -> str:
UpperCAmelCase_ : Union[str, Any] = self.get_image_processor()
UpperCAmelCase_ : int = self.get_tokenizer()
UpperCAmelCase_ : Any = BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,image_processor=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Dict = '''lower newer'''
UpperCAmelCase_ : Union[str, Any] = self.prepare_image_inputs()
UpperCAmelCase_ : Any = processor(text=_SCREAMING_SNAKE_CASE ,images=_SCREAMING_SNAKE_CASE )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) ,['''pixel_values''', '''input_ids''', '''attention_mask'''] ) | 235 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
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
lowercase__ = "platform"
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , ) -> Any:
"""simple docstring"""
if attention_mask is None:
lowerCAmelCase_ : int = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
lowerCAmelCase_ : Dict = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
lowerCAmelCase_ : Any = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowerCAmelCase_ : Tuple = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowerCAmelCase_ : Optional[int] = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class __lowerCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , a_ : List[str] , a_ : Optional[int]=13 , a_ : Tuple=7 , a_ : Tuple=True , a_ : int=False , a_ : str=99 , a_ : Union[str, Any]=16 , a_ : int=2 , a_ : Optional[int]=4 , a_ : List[str]=4 , a_ : Union[str, Any]="gelu" , a_ : Any=0.1 , a_ : List[Any]=0.1 , a_ : List[str]=32 , a_ : Optional[Any]=2 , a_ : Dict=1 , a_ : List[str]=0 , a_ : Optional[Any]=0.02 , ):
lowerCAmelCase_ : str = parent
lowerCAmelCase_ : List[str] = batch_size
lowerCAmelCase_ : List[str] = seq_length
lowerCAmelCase_ : Optional[Any] = is_training
lowerCAmelCase_ : Tuple = use_labels
lowerCAmelCase_ : Any = vocab_size
lowerCAmelCase_ : str = hidden_size
lowerCAmelCase_ : Tuple = num_hidden_layers
lowerCAmelCase_ : Optional[int] = num_attention_heads
lowerCAmelCase_ : Union[str, Any] = intermediate_size
lowerCAmelCase_ : Optional[int] = hidden_act
lowerCAmelCase_ : Optional[Any] = hidden_dropout_prob
lowerCAmelCase_ : Tuple = attention_probs_dropout_prob
lowerCAmelCase_ : Union[str, Any] = max_position_embeddings
lowerCAmelCase_ : List[Any] = eos_token_id
lowerCAmelCase_ : str = pad_token_id
lowerCAmelCase_ : str = bos_token_id
lowerCAmelCase_ : Tuple = initializer_range
def lowerCamelCase ( self : Optional[Any] ):
lowerCAmelCase_ : List[Any] = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
lowerCAmelCase_ : List[str] = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
lowerCAmelCase_ : Optional[Any] = shift_tokens_right(a_ , 1 , 2 )
lowerCAmelCase_ : Optional[Any] = BlenderbotConfig(
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_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=a_ , )
lowerCAmelCase_ : Optional[int] = prepare_blenderbot_inputs_dict(a_ , a_ , a_ )
return config, inputs_dict
def lowerCamelCase ( self : List[str] ):
lowerCAmelCase_ , lowerCAmelCase_ : Any = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCamelCase ( self : Optional[Any] , a_ : Optional[Any] , a_ : List[str] , a_ : Optional[int] ):
lowerCAmelCase_ : List[Any] = 20
lowerCAmelCase_ : Tuple = model_class_name(a_ )
lowerCAmelCase_ : List[Any] = model.encode(inputs_dict["input_ids"] )
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
lowerCAmelCase_ : int = model.init_cache(decoder_input_ids.shape[0] , a_ , a_ )
lowerCAmelCase_ : Tuple = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="i4" )
lowerCAmelCase_ : Union[str, 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[Any] = model.decode(
decoder_input_ids[:, :-1] , a_ , decoder_attention_mask=a_ , past_key_values=a_ , decoder_position_ids=a_ , )
lowerCAmelCase_ : Optional[Any] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
lowerCAmelCase_ : Dict = model.decode(
decoder_input_ids[:, -1:] , a_ , decoder_attention_mask=a_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=a_ , )
lowerCAmelCase_ : int = model.decode(a_ , a_ )
lowerCAmelCase_ : Optional[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 lowerCamelCase ( self : int , a_ : Any , a_ : List[Any] , a_ : Any ):
lowerCAmelCase_ : int = 20
lowerCAmelCase_ : Union[str, Any] = model_class_name(a_ )
lowerCAmelCase_ : Union[str, Any] = model.encode(inputs_dict["input_ids"] )
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
lowerCAmelCase_ : int = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
lowerCAmelCase_ : Any = model.init_cache(decoder_input_ids.shape[0] , a_ , a_ )
lowerCAmelCase_ : Union[str, 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] , a_ , decoder_attention_mask=a_ , past_key_values=a_ , decoder_position_ids=a_ , )
lowerCAmelCase_ : Optional[int] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
lowerCAmelCase_ : Optional[int] = model.decode(
decoder_input_ids[:, -1:] , a_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=a_ , decoder_position_ids=a_ , )
lowerCAmelCase_ : Tuple = model.decode(a_ , a_ , decoder_attention_mask=a_ )
lowerCAmelCase_ : str = 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 __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
a_ : Optional[int] = 99
def lowerCamelCase ( self : Dict ):
lowerCAmelCase_ : str = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
lowerCAmelCase_ : str = input_ids.shape[0]
lowerCAmelCase_ : List[Any] = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def lowerCamelCase ( self : Union[str, Any] ):
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = self._get_config_and_data()
lowerCAmelCase_ : int = FlaxBlenderbotForConditionalGeneration(a_ )
lowerCAmelCase_ : Any = lm_model(input_ids=a_ )
lowerCAmelCase_ : List[Any] = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["logits"].shape , a_ )
def lowerCamelCase ( self : Optional[Any] ):
lowerCAmelCase_ : Optional[int] = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
lowerCAmelCase_ : int = FlaxBlenderbotForConditionalGeneration(a_ )
lowerCAmelCase_ : Optional[int] = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
lowerCAmelCase_ : List[Any] = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
lowerCAmelCase_ : Dict = lm_model(input_ids=a_ , decoder_input_ids=a_ )
lowerCAmelCase_ : int = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["logits"].shape , a_ )
def lowerCamelCase ( self : List[Any] ):
lowerCAmelCase_ : Optional[int] = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
lowerCAmelCase_ : Tuple = shift_tokens_right(a_ , 1 , 2 )
lowerCAmelCase_ : str = np.equal(a_ , 1 ).astype(np.floataa ).sum()
lowerCAmelCase_ : str = np.equal(a_ , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(a_ , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class __lowerCamelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
a_ : List[str] = True
a_ : Optional[Any] = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
a_ : List[Any] = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def lowerCamelCase ( self : Union[str, Any] ):
lowerCAmelCase_ : Tuple = FlaxBlenderbotModelTester(self )
def lowerCamelCase ( self : Optional[int] ):
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(a_ , a_ , a_ )
def lowerCamelCase ( self : Optional[Any] ):
lowerCAmelCase_ , lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(a_ , a_ , a_ )
def lowerCamelCase ( self : int ):
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_ : Optional[Any] = self._prepare_for_class(a_ , a_ )
lowerCAmelCase_ : str = model_class(a_ )
@jax.jit
def encode_jitted(a_ : List[Any] , a_ : Dict=None , **a_ : List[str] ):
return model.encode(input_ids=a_ , attention_mask=a_ )
with self.subTest("JIT Enabled" ):
lowerCAmelCase_ : int = encode_jitted(**a_ ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
lowerCAmelCase_ : List[str] = encode_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 lowerCamelCase ( self : int ):
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = 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 = model_class(a_ )
lowerCAmelCase_ : int = model.encode(inputs_dict["input_ids"] , inputs_dict["attention_mask"] )
lowerCAmelCase_ : Union[str, Any] = {
"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(a_ : Optional[int] , a_ : Dict , a_ : Any ):
return model.decode(
decoder_input_ids=a_ , decoder_attention_mask=a_ , encoder_outputs=a_ , )
with self.subTest("JIT Enabled" ):
lowerCAmelCase_ : Union[str, Any] = decode_jitted(**a_ ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
lowerCAmelCase_ : Optional[Any] = decode_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 )
@slow
def lowerCamelCase ( self : Union[str, Any] ):
for model_class_name in self.all_model_classes:
lowerCAmelCase_ : Dict = model_class_name.from_pretrained("facebook/blenderbot-400M-distill" )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
lowerCAmelCase_ : Dict = np.ones((1, 1) ) * model.config.eos_token_id
lowerCAmelCase_ : Any = model(a_ )
self.assertIsNotNone(a_ )
@unittest.skipUnless(jax_device != "cpu" , "3B test too slow on CPU." )
@slow
def lowerCamelCase ( self : Tuple ):
lowerCAmelCase_ : str = {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
lowerCAmelCase_ : str = {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}
lowerCAmelCase_ : Dict = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B" , from_pt=a_ )
lowerCAmelCase_ : Tuple = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B" )
lowerCAmelCase_ : List[str] = ["Sam"]
lowerCAmelCase_ : int = tokenizer(a_ , return_tensors="jax" )
lowerCAmelCase_ : Optional[int] = model.generate(**a_ , **a_ )
lowerCAmelCase_ : str = "Sam is a great name. It means \"sun\" in Gaelic."
lowerCAmelCase_ : Tuple = tokenizer.batch_decode(a_ , **a_ )
assert generated_txt[0].strip() == tgt_text
| 241 | def a__ ( __UpperCamelCase ):
if not head:
return True
# split the list to two parts
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = head.next, head
while fast and fast.next:
SCREAMING_SNAKE_CASE_ = fast.next.next
SCREAMING_SNAKE_CASE_ = slow.next
SCREAMING_SNAKE_CASE_ = slow.next
SCREAMING_SNAKE_CASE_ = None # Don't forget here! But forget still works!
# reverse the second part
SCREAMING_SNAKE_CASE_ = None
while second:
SCREAMING_SNAKE_CASE_ = second.next
SCREAMING_SNAKE_CASE_ = node
SCREAMING_SNAKE_CASE_ = second
SCREAMING_SNAKE_CASE_ = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
SCREAMING_SNAKE_CASE_ = node.next
SCREAMING_SNAKE_CASE_ = head.next
return True
def a__ ( __UpperCamelCase ):
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
SCREAMING_SNAKE_CASE_ = SCREAMING_SNAKE_CASE_ = SCREAMING_SNAKE_CASE_ = head
while fast and fast.next:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = fast.next.next, slow.next
# 2. Push the second half into the stack
SCREAMING_SNAKE_CASE_ = [slow.val]
while slow.next:
SCREAMING_SNAKE_CASE_ = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
SCREAMING_SNAKE_CASE_ = cur.next
return True
def a__ ( __UpperCamelCase ):
if not head or not head.next:
return True
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = 0
while head:
if head.val in d:
d[head.val].append(__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = [pos]
SCREAMING_SNAKE_CASE_ = head.next
pos += 1
SCREAMING_SNAKE_CASE_ = pos - 1
SCREAMING_SNAKE_CASE_ = 0
for v in d.values():
if len(__UpperCamelCase ) % 2 != 0:
middle += 1
else:
SCREAMING_SNAKE_CASE_ = 0
for i in range(0 , len(__UpperCamelCase ) ):
if v[i] + v[len(__UpperCamelCase ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 118 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] = {
"""facebook/data2vec-vision-base-ft""": (
"""https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
),
}
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Tuple = 'data2vec-vision'
def __init__(self , __lowercase=7_68 , __lowercase=12 , __lowercase=12 , __lowercase=30_72 , __lowercase="gelu" , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.0_2 , __lowercase=1e-12 , __lowercase=2_24 , __lowercase=16 , __lowercase=3 , __lowercase=False , __lowercase=False , __lowercase=False , __lowercase=False , __lowercase=0.1 , __lowercase=0.1 , __lowercase=True , __lowercase=[3, 5, 7, 11] , __lowercase=[1, 2, 3, 6] , __lowercase=True , __lowercase=0.4 , __lowercase=2_56 , __lowercase=1 , __lowercase=False , __lowercase=2_55 , **__lowercase , ):
super().__init__(**__lowercase )
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = initializer_range
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = image_size
__lowerCAmelCase = patch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = use_mask_token
__lowerCAmelCase = use_absolute_position_embeddings
__lowerCAmelCase = use_relative_position_bias
__lowerCAmelCase = use_shared_relative_position_bias
__lowerCAmelCase = layer_scale_init_value
__lowerCAmelCase = drop_path_rate
__lowerCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
__lowerCAmelCase = out_indices
__lowerCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
__lowerCAmelCase = use_auxiliary_head
__lowerCAmelCase = auxiliary_loss_weight
__lowerCAmelCase = auxiliary_channels
__lowerCAmelCase = auxiliary_num_convs
__lowerCAmelCase = auxiliary_concat_input
__lowerCAmelCase = semantic_loss_ignore_index
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Any = version.parse('1.11' )
@property
def _snake_case (self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _snake_case (self ):
return 1e-4
| 363 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __magic_name__( ):
__lowerCAmelCase = [randint(-1_0_0_0, 1_0_0_0) for i in range(1_0)]
__lowerCAmelCase = randint(-5_0_0_0, 5_0_0_0)
return (arr, r)
_UpperCAmelCase : Dict = make_dataset()
def __magic_name__( lowerCamelCase, lowerCamelCase):
for triplet in permutations(lowerCamelCase, 3):
if sum(lowerCamelCase) == target:
return tuple(sorted(lowerCamelCase))
return (0, 0, 0)
def __magic_name__( lowerCamelCase, lowerCamelCase):
arr.sort()
__lowerCAmelCase = len(lowerCamelCase)
for i in range(n - 1):
__lowerCAmelCase , __lowerCAmelCase = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def __magic_name__( ):
__lowerCAmelCase = '''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
__lowerCAmelCase = '''
triplet_sum1(*dataset)
'''
__lowerCAmelCase = '''
triplet_sum2(*dataset)
'''
__lowerCAmelCase = repeat(setup=lowerCamelCase, stmt=lowerCamelCase, repeat=5, number=1_0_0_0_0)
__lowerCAmelCase = repeat(setup=lowerCamelCase, stmt=lowerCamelCase, repeat=5, number=1_0_0_0_0)
return (min(lowerCamelCase), min(lowerCamelCase))
if __name__ == "__main__":
from doctest import testmod
testmod()
_UpperCAmelCase : Union[str, Any] = solution_times()
print(f"""The time for naive implementation is {times[0]}.""")
print(f"""The time for optimized implementation is {times[1]}.""")
| 9 | 0 |
import cmath
import math
def a( A : float , A : float , A : float , A : float ) -> complex:
"""simple docstring"""
a = math.radians(A )
a = math.radians(A )
# Convert voltage and current to rectangular form
a = cmath.rect(A , A )
a = cmath.rect(A , A )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 227 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase: Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
"BridgeTowerTextConfig",
"BridgeTowerVisionConfig",
],
"processing_bridgetower": ["BridgeTowerProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase: Dict = ["BridgeTowerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase: int = [
"BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
"BridgeTowerForContrastiveLearning",
"BridgeTowerForImageAndTextRetrieval",
"BridgeTowerForMaskedLM",
"BridgeTowerModel",
"BridgeTowerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_bridgetower import (
BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
BridgeTowerConfig,
BridgeTowerTextConfig,
BridgeTowerVisionConfig,
)
from .processing_bridgetower import BridgeTowerProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_bridgetower import BridgeTowerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bridgetower import (
BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
BridgeTowerForContrastiveLearning,
BridgeTowerForImageAndTextRetrieval,
BridgeTowerForMaskedLM,
BridgeTowerModel,
BridgeTowerPreTrainedModel,
)
else:
import sys
_lowercase: Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 227 | 1 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class _A ( unittest.TestCase ):
def __init__( self : Tuple , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : int=13 , __SCREAMING_SNAKE_CASE : List[str]=7 , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : Optional[Any]=True , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : Optional[Any]=99 , __SCREAMING_SNAKE_CASE : Dict=32 , __SCREAMING_SNAKE_CASE : str=5 , __SCREAMING_SNAKE_CASE : str=4 , __SCREAMING_SNAKE_CASE : Union[str, Any]=37 , __SCREAMING_SNAKE_CASE : List[str]="gelu" , __SCREAMING_SNAKE_CASE : List[str]=0.1 , __SCREAMING_SNAKE_CASE : Optional[int]=0.1 , __SCREAMING_SNAKE_CASE : Optional[Any]=512 , __SCREAMING_SNAKE_CASE : Tuple=16 , __SCREAMING_SNAKE_CASE : Dict=2 , __SCREAMING_SNAKE_CASE : str=0.02 , __SCREAMING_SNAKE_CASE : str=4 , ):
'''simple docstring'''
__a = parent
__a = batch_size
__a = seq_length
__a = is_training
__a = use_attention_mask
__a = use_token_type_ids
__a = use_labels
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = type_vocab_size
__a = type_sequence_label_size
__a = initializer_range
__a = num_choices
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
__a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__a = None
if self.use_attention_mask:
__a = random_attention_mask([self.batch_size, self.seq_length])
__a = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , tie_weights_=__SCREAMING_SNAKE_CASE , )
return config, input_ids, attention_mask
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
__a = self.prepare_config_and_inputs()
__a , __a , __a = config_and_inputs
__a = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class _A ( __UpperCAmelCase ,unittest.TestCase ):
UpperCamelCase__ : Optional[Any] = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowerCamelCase ( self : str):
'''simple docstring'''
__a = FlaxDistilBertModelTester(self)
@slow
def _lowerCamelCase ( self : int):
'''simple docstring'''
for model_class_name in self.all_model_classes:
__a = model_class_name.from_pretrained('''distilbert-base-uncased''')
__a = model(np.ones((1, 1)))
self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
@require_flax
class _A ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__a = FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased''')
__a = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]])
__a = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
__a = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE)[0]
__a = (1, 11, 768)
self.assertEqual(output.shape , __SCREAMING_SNAKE_CASE)
__a = np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]])
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , __SCREAMING_SNAKE_CASE , atol=1E-4))
| 131 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
__snake_case :Dict = logging.get_logger(__name__)
@add_end_docstrings(__UpperCAmelCase )
class _A ( __UpperCAmelCase ):
def __init__( self : int , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : str):
'''simple docstring'''
super().__init__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
requires_backends(self , '''vision''')
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING)
def _lowerCamelCase ( self : int , __SCREAMING_SNAKE_CASE : int=None , __SCREAMING_SNAKE_CASE : int=None , __SCREAMING_SNAKE_CASE : Tuple=None):
'''simple docstring'''
__a = {}
__a = {}
if prompt is not None:
__a = prompt
if generate_kwargs is not None:
__a = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
__a = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
'''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
''' please use only one''')
__a = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : Any , __SCREAMING_SNAKE_CASE : Union[str, List[str], "Image.Image", List["Image.Image"]] , **__SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
return super().__call__(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Optional[Any]=None):
'''simple docstring'''
__a = load_image(__SCREAMING_SNAKE_CASE)
if prompt is not None:
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
raise ValueError(
F'Received an invalid text input, got - {type(__SCREAMING_SNAKE_CASE)} - but expected a single string. '
'''Note also that one single text can be provided for conditional image to text generation.''')
__a = self.model.config.model_type
if model_type == "git":
__a = self.image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors=self.framework)
__a = self.tokenizer(text=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE).input_ids
__a = [self.tokenizer.cls_token_id] + input_ids
__a = torch.tensor(__SCREAMING_SNAKE_CASE).unsqueeze(0)
model_inputs.update({'''input_ids''': input_ids})
elif model_type == "pix2struct":
__a = self.image_processor(images=__SCREAMING_SNAKE_CASE , header_text=__SCREAMING_SNAKE_CASE , return_tensors=self.framework)
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
__a = self.image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors=self.framework)
__a = self.tokenizer(__SCREAMING_SNAKE_CASE , return_tensors=self.framework)
model_inputs.update(__SCREAMING_SNAKE_CASE)
else:
raise ValueError(F'Model type {model_type} does not support conditional text generation')
else:
__a = self.image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors=self.framework)
if self.model.config.model_type == "git" and prompt is None:
__a = None
return model_inputs
def _lowerCamelCase ( self : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Tuple=None):
'''simple docstring'''
if (
"input_ids" in model_inputs
and isinstance(model_inputs['''input_ids'''] , __SCREAMING_SNAKE_CASE)
and all(x is None for x in model_inputs['''input_ids'''])
):
__a = None
if generate_kwargs is None:
__a = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
__a = model_inputs.pop(self.model.main_input_name)
__a = self.model.generate(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
return model_outputs
def _lowerCamelCase ( self : str , __SCREAMING_SNAKE_CASE : Union[str, Any]):
'''simple docstring'''
__a = []
for output_ids in model_outputs:
__a = {
'''generated_text''': self.tokenizer.decode(
__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE , )
}
records.append(__SCREAMING_SNAKE_CASE)
return records
| 131 | 1 |
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 _snake_case ( lowercase_ ):
'''simple docstring'''
A__ : List[str] = ["image_processor", "tokenizer"]
A__ : Tuple = "BlipImageProcessor"
A__ : List[Any] = ("BertTokenizer", "BertTokenizerFast")
def __init__( self: List[Any] ,lowerCamelCase_: Union[str, Any] ,lowerCamelCase_: List[str] ) -> Dict:
UpperCAmelCase_ : Optional[Any] = False
super().__init__(__UpperCamelCase ,__UpperCamelCase )
UpperCAmelCase_ : Tuple = self.image_processor
def __call__( self: Union[str, Any] ,lowerCamelCase_: Optional[int] = None ,lowerCamelCase_: str = None ,lowerCamelCase_: List[str] = True ,lowerCamelCase_: Union[str, Any] = False ,lowerCamelCase_: Optional[Any] = None ,lowerCamelCase_: List[str] = None ,lowerCamelCase_: List[str] = 0 ,lowerCamelCase_: Optional[int] = None ,lowerCamelCase_: int = None ,lowerCamelCase_: Optional[Any] = False ,lowerCamelCase_: List[Any] = False ,lowerCamelCase_: Optional[Any] = False ,lowerCamelCase_: Union[str, Any] = False ,lowerCamelCase_: Dict = False ,lowerCamelCase_: Union[str, Any] = True ,lowerCamelCase_: Optional[Any] = None ,**lowerCamelCase_: 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:
UpperCAmelCase_ : str = self.tokenizer
UpperCAmelCase_ : List[str] = self.tokenizer(
text=__UpperCamelCase ,add_special_tokens=__UpperCamelCase ,padding=__UpperCamelCase ,truncation=__UpperCamelCase ,max_length=__UpperCamelCase ,stride=__UpperCamelCase ,pad_to_multiple_of=__UpperCamelCase ,return_attention_mask=__UpperCamelCase ,return_overflowing_tokens=__UpperCamelCase ,return_special_tokens_mask=__UpperCamelCase ,return_offsets_mapping=__UpperCamelCase ,return_token_type_ids=__UpperCamelCase ,return_length=__UpperCamelCase ,verbose=__UpperCamelCase ,return_tensors=__UpperCamelCase ,**__UpperCamelCase ,)
return text_encoding
# add pixel_values
UpperCAmelCase_ : Union[str, Any] = self.image_processor(__UpperCamelCase ,return_tensors=__UpperCamelCase )
if text is not None:
UpperCAmelCase_ : Tuple = self.tokenizer(
text=__UpperCamelCase ,add_special_tokens=__UpperCamelCase ,padding=__UpperCamelCase ,truncation=__UpperCamelCase ,max_length=__UpperCamelCase ,stride=__UpperCamelCase ,pad_to_multiple_of=__UpperCamelCase ,return_attention_mask=__UpperCamelCase ,return_overflowing_tokens=__UpperCamelCase ,return_special_tokens_mask=__UpperCamelCase ,return_offsets_mapping=__UpperCamelCase ,return_token_type_ids=__UpperCamelCase ,return_length=__UpperCamelCase ,verbose=__UpperCamelCase ,return_tensors=__UpperCamelCase ,**__UpperCamelCase ,)
else:
UpperCAmelCase_ : Any = None
if text_encoding is not None:
encoding_image_processor.update(__UpperCamelCase )
return encoding_image_processor
def A__ ( self: int ,*lowerCamelCase_: Union[str, Any] ,**lowerCamelCase_: Optional[Any] ) -> str:
return self.tokenizer.batch_decode(*__UpperCamelCase ,**__UpperCamelCase )
def A__ ( self: Optional[Any] ,*lowerCamelCase_: List[str] ,**lowerCamelCase_: Optional[int] ) -> str:
return self.tokenizer.decode(*__UpperCamelCase ,**__UpperCamelCase )
@property
def A__ ( self: Union[str, Any] ) -> Optional[int]:
UpperCAmelCase_ : str = self.tokenizer.model_input_names
UpperCAmelCase_ : Optional[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 345 | """simple docstring"""
from __future__ import annotations
__SCREAMING_SNAKE_CASE =[]
def lowercase__( __SCREAMING_SNAKE_CASE : list[list[int]] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ):
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if board[row][i] == 1:
return False
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(__SCREAMING_SNAKE_CASE , -1 , -1 ) , range(__SCREAMING_SNAKE_CASE , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(__SCREAMING_SNAKE_CASE , -1 , -1 ) , range(__SCREAMING_SNAKE_CASE , len(__SCREAMING_SNAKE_CASE ) ) ):
if board[i][j] == 1:
return False
return True
def lowercase__( __SCREAMING_SNAKE_CASE : list[list[int]] , __SCREAMING_SNAKE_CASE : int ):
if row >= len(__SCREAMING_SNAKE_CASE ):
solution.append(__SCREAMING_SNAKE_CASE )
printboard(__SCREAMING_SNAKE_CASE )
print()
return True
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if is_safe(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase_ : int = 1
solve(__SCREAMING_SNAKE_CASE , row + 1 )
lowercase_ : Dict = 0
return False
def lowercase__( __SCREAMING_SNAKE_CASE : list[list[int]] ):
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
for j in range(len(__SCREAMING_SNAKE_CASE ) ):
if board[i][j] == 1:
print('Q' , end=' ' )
else:
print('.' , end=' ' )
print()
# n=int(input("The no. of queens"))
__SCREAMING_SNAKE_CASE =8
__SCREAMING_SNAKE_CASE =[[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))
| 213 | 0 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class _UpperCAmelCase :
'''simple docstring'''
def __init__(self , a_ , a_ , a_ , a_=None , a_=None ):
'''simple docstring'''
__snake_case : Optional[Any] = start
__snake_case : Optional[int] = end
__snake_case : Optional[Any] = val
__snake_case : List[Any] = (start + end) // 2
__snake_case : Union[str, Any] = left
__snake_case : str = right
def __repr__(self ):
'''simple docstring'''
return f"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""
class _UpperCAmelCase :
'''simple docstring'''
def __init__(self , a_ , a_ ):
'''simple docstring'''
__snake_case : Optional[Any] = collection
__snake_case : Optional[Any] = function
if self.collection:
__snake_case : Optional[Any] = self._build_tree(0 , len(a_ ) - 1 )
def SCREAMING_SNAKE_CASE (self , a_ , a_ ):
'''simple docstring'''
self._update_tree(self.root , a_ , a_ )
def SCREAMING_SNAKE_CASE (self , a_ , a_ ):
'''simple docstring'''
return self._query_range(self.root , a_ , a_ )
def SCREAMING_SNAKE_CASE (self , a_ , a_ ):
'''simple docstring'''
if start == end:
return SegmentTreeNode(a_ , a_ , self.collection[start] )
__snake_case : str = (start + end) // 2
__snake_case : Dict = self._build_tree(a_ , a_ )
__snake_case : Optional[int] = self._build_tree(mid + 1 , a_ )
return SegmentTreeNode(a_ , a_ , self.fn(left.val , right.val ) , a_ , a_ )
def SCREAMING_SNAKE_CASE (self , a_ , a_ , a_ ):
'''simple docstring'''
if node.start == i and node.end == i:
__snake_case : str = val
return
if i <= node.mid:
self._update_tree(node.left , a_ , a_ )
else:
self._update_tree(node.right , a_ , a_ )
__snake_case : Optional[Any] = self.fn(node.left.val , node.right.val )
def SCREAMING_SNAKE_CASE (self , a_ , a_ , a_ ):
'''simple docstring'''
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left , a_ , a_ )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left , a_ , node.mid ) , self._query_range(node.right , node.mid + 1 , a_ ) , )
else:
# range in right child tree
return self._query_range(node.right , a_ , a_ )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
if self.root is not None:
__snake_case : str = Queue()
queue.put(self.root )
while not queue.empty():
__snake_case : List[Any] = queue.get()
yield node
if node.left is not None:
queue.put(node.left )
if node.right is not None:
queue.put(node.right )
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print("""*""" * 50)
SCREAMING_SNAKE_CASE : Union[str, Any] = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 24 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"""
),
}
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ ='gptsan-japanese'
lowerCamelCase__ =[
'past_key_values',
]
lowerCamelCase__ ={
'hidden_size': 'd_model',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__(self , a_=3_60_00 , a_=12_80 , a_=10_24 , a_=81_92 , a_=40_96 , a_=1_28 , a_=10 , a_=0 , a_=16 , a_=16 , a_=1_28 , a_=0.0 , a_=1E-5 , a_=False , a_=0.0 , a_="float32" , a_=False , a_=False , a_=False , a_=0.002 , a_=False , a_=True , a_=3_59_98 , a_=3_59_95 , a_=3_59_99 , **a_ , ):
'''simple docstring'''
__snake_case : Any = vocab_size
__snake_case : str = max_position_embeddings
__snake_case : Any = d_model
__snake_case : List[str] = d_ff
__snake_case : Dict = d_ext
__snake_case : Optional[Any] = d_spout
__snake_case : int = num_switch_layers
__snake_case : List[Any] = num_ext_layers
__snake_case : Any = num_switch_layers + num_ext_layers
__snake_case : Optional[int] = num_heads
__snake_case : Tuple = num_experts
__snake_case : List[Any] = expert_capacity
__snake_case : Dict = dropout_rate
__snake_case : Optional[Any] = layer_norm_epsilon
__snake_case : Dict = router_bias
__snake_case : str = router_jitter_noise
__snake_case : List[str] = router_dtype
__snake_case : Union[str, Any] = router_ignore_padding_tokens
__snake_case : List[str] = output_hidden_states
__snake_case : Optional[Any] = output_attentions
__snake_case : Any = initializer_factor
__snake_case : int = output_router_logits
__snake_case : Union[str, Any] = use_cache
super().__init__(
separator_token_id=a_ , pad_token_id=a_ , eos_token_id=a_ , **a_ , )
| 24 | 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,
)
A ={
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
'tokenization_roformer': ['RoFormerTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =['RoFormerTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'RoFormerForCausalLM',
'RoFormerForMaskedLM',
'RoFormerForMultipleChoice',
'RoFormerForQuestionAnswering',
'RoFormerForSequenceClassification',
'RoFormerForTokenClassification',
'RoFormerLayer',
'RoFormerModel',
'RoFormerPreTrainedModel',
'load_tf_weights_in_roformer',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRoFormerForCausalLM',
'TFRoFormerForMaskedLM',
'TFRoFormerForMultipleChoice',
'TFRoFormerForQuestionAnswering',
'TFRoFormerForSequenceClassification',
'TFRoFormerForTokenClassification',
'TFRoFormerLayer',
'TFRoFormerModel',
'TFRoFormerPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'FlaxRoFormerForMaskedLM',
'FlaxRoFormerForMultipleChoice',
'FlaxRoFormerForQuestionAnswering',
'FlaxRoFormerForSequenceClassification',
'FlaxRoFormerForTokenClassification',
'FlaxRoFormerModel',
'FlaxRoFormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
A =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 34 | import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
__UpperCamelCase : Any = getLogger(__name__)
__UpperCamelCase : int = 'cuda' if torch.cuda.is_available() else 'cpu'
def A ( _lowercase , _lowercase , _lowercase , _lowercase = 8 , _lowercase = DEFAULT_DEVICE , _lowercase=False , _lowercase="summarization" , _lowercase=None , **_lowercase , ):
SCREAMING_SNAKE_CASE : List[str] = Path(_lowercase ).open('''w''' , encoding='''utf-8''' )
SCREAMING_SNAKE_CASE : int = str(_lowercase )
SCREAMING_SNAKE_CASE : Any = AutoModelForSeqaSeqLM.from_pretrained(_lowercase ).to(_lowercase )
if fpaa:
SCREAMING_SNAKE_CASE : Dict = model.half()
SCREAMING_SNAKE_CASE : Optional[Any] = AutoTokenizer.from_pretrained(_lowercase )
logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
SCREAMING_SNAKE_CASE : str = time.time()
# update config with task specific params
use_task_specific_params(_lowercase , _lowercase )
if prefix is None:
SCREAMING_SNAKE_CASE : Optional[int] = prefix or getattr(model.config , '''prefix''' , '''''' ) or ''''''
for examples_chunk in tqdm(list(chunks(_lowercase , _lowercase ) ) ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [prefix + text for text in examples_chunk]
SCREAMING_SNAKE_CASE : Dict = tokenizer(_lowercase , return_tensors='''pt''' , truncation=_lowercase , padding='''longest''' ).to(_lowercase )
SCREAMING_SNAKE_CASE : str = model.generate(
input_ids=batch.input_ids , attention_mask=batch.attention_mask , **_lowercase , )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.batch_decode(_lowercase , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase )
for hypothesis in dec:
fout.write(hypothesis + '''\n''' )
fout.flush()
fout.close()
SCREAMING_SNAKE_CASE : Tuple = int(time.time() - start_time ) # seconds
SCREAMING_SNAKE_CASE : str = len(_lowercase )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )}
def A ( ):
return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''' )
def A ( _lowercase=True ):
SCREAMING_SNAKE_CASE : int = argparse.ArgumentParser()
parser.add_argument('''model_name''' , type=_lowercase , help='''like facebook/bart-large-cnn,t5-base, etc.''' )
parser.add_argument('''input_path''' , type=_lowercase , help='''like cnn_dm/test.source''' )
parser.add_argument('''save_path''' , type=_lowercase , help='''where to save summaries''' )
parser.add_argument('''--reference_path''' , type=_lowercase , required=_lowercase , help='''like cnn_dm/test.target''' )
parser.add_argument('''--score_path''' , type=_lowercase , required=_lowercase , default='''metrics.json''' , help='''where to save metrics''' )
parser.add_argument('''--device''' , type=_lowercase , required=_lowercase , default=_lowercase , help='''cuda, cuda:1, cpu etc.''' )
parser.add_argument(
'''--prefix''' , type=_lowercase , required=_lowercase , default=_lowercase , help='''will be added to the begininng of src examples''' )
parser.add_argument('''--task''' , type=_lowercase , default='''summarization''' , help='''used for task_specific_params + metrics''' )
parser.add_argument('''--bs''' , type=_lowercase , default=8 , required=_lowercase , help='''batch size''' )
parser.add_argument(
'''--n_obs''' , type=_lowercase , default=-1 , required=_lowercase , help='''How many observations. Defaults to all.''' )
parser.add_argument('''--fp16''' , action='''store_true''' )
parser.add_argument('''--dump-args''' , action='''store_true''' , help='''print the custom hparams with the results''' )
parser.add_argument(
'''--info''' , nargs='''?''' , type=_lowercase , const=datetime_now() , help=(
'''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
) , )
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_known_args()
SCREAMING_SNAKE_CASE : Optional[Any] = parse_numeric_n_bool_cl_kwargs(_lowercase )
if parsed_args and verbose:
print(f"""parsed the following generate kwargs: {parsed_args}""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
SCREAMING_SNAKE_CASE : Any = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=_lowercase )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('''Can\'t mix --fp16 and --device cpu''' )
SCREAMING_SNAKE_CASE : List[str] = generate_summaries_or_translations(
_lowercase , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **_lowercase , )
if args.reference_path is None:
return {}
# Compute scores
SCREAMING_SNAKE_CASE : Dict = calculate_bleu if '''translation''' in args.task else calculate_rouge
SCREAMING_SNAKE_CASE : Union[str, Any] = [x.rstrip() for x in open(args.save_path ).readlines()]
SCREAMING_SNAKE_CASE : Optional[int] = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_lowercase )]
SCREAMING_SNAKE_CASE : dict = score_fn(_lowercase , _lowercase )
scores.update(_lowercase )
if args.dump_args:
scores.update(_lowercase )
if args.info:
SCREAMING_SNAKE_CASE : Tuple = args.info
if verbose:
print(_lowercase )
if args.score_path is not None:
json.dump(_lowercase , open(args.score_path , '''w''' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 182 | 0 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __UpperCamelCase :
def __init__( self :Optional[int] ,_UpperCamelCase :Any ,_UpperCamelCase :int=1_3 ,_UpperCamelCase :Any=3_2 ,_UpperCamelCase :Any=3 ,_UpperCamelCase :str=4 ,_UpperCamelCase :List[str]=[1_0, 2_0, 3_0, 4_0] ,_UpperCamelCase :Dict=[2, 2, 3, 2] ,_UpperCamelCase :Optional[Any]=True ,_UpperCamelCase :Tuple=True ,_UpperCamelCase :Dict=3_7 ,_UpperCamelCase :List[str]="gelu" ,_UpperCamelCase :Tuple=1_0 ,_UpperCamelCase :List[str]=0.02 ,_UpperCamelCase :Tuple=["stage2", "stage3", "stage4"] ,_UpperCamelCase :str=3 ,_UpperCamelCase :List[Any]=None ,):
snake_case_ : List[str] = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Tuple = image_size
snake_case_ : Optional[int] = num_channels
snake_case_ : List[str] = num_stages
snake_case_ : List[Any] = hidden_sizes
snake_case_ : Optional[Any] = depths
snake_case_ : List[str] = is_training
snake_case_ : str = use_labels
snake_case_ : Any = intermediate_size
snake_case_ : Dict = hidden_act
snake_case_ : List[Any] = type_sequence_label_size
snake_case_ : List[str] = initializer_range
snake_case_ : Tuple = out_features
snake_case_ : Dict = num_labels
snake_case_ : List[str] = scope
snake_case_ : Optional[Any] = num_stages
def a__ ( self :Any ):
snake_case_ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : Tuple = None
if self.use_labels:
snake_case_ : Any = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
snake_case_ : Optional[int] = self.get_config()
return config, pixel_values, labels
def a__ ( self :List[str] ):
return ConvNextConfig(
num_channels=self.num_channels ,num_stages=self.num_stages ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,is_training=self.is_training ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,out_features=self.out_features ,)
def a__ ( self :List[str] ):
return UperNetConfig(
backbone_config=self.get_backbone_config() ,hidden_size=5_1_2 ,pool_scales=[1, 2, 3, 6] ,use_auxiliary_head=_UpperCamelCase ,auxiliary_loss_weight=0.4 ,auxiliary_in_channels=4_0 ,auxiliary_channels=2_5_6 ,auxiliary_num_convs=1 ,auxiliary_concat_input=_UpperCamelCase ,loss_ignore_index=2_5_5 ,num_labels=self.num_labels ,)
def a__ ( self :Union[str, Any] ,_UpperCamelCase :Any ,_UpperCamelCase :Dict ,_UpperCamelCase :List[str] ):
snake_case_ : Union[str, Any] = UperNetForSemanticSegmentation(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
snake_case_ : List[Any] = model(_UpperCamelCase )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size, self.image_size) )
def a__ ( self :Dict ):
snake_case_ : List[str] = self.prepare_config_and_inputs()
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) : Union[str, Any] = config_and_inputs
snake_case_ : List[Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowercase__ , lowercase__ , unittest.TestCase ):
lowercase : str = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
lowercase : List[Any] = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
lowercase : Optional[int] = False
lowercase : Union[str, Any] = False
lowercase : int = False
lowercase : int = False
lowercase : Dict = False
lowercase : Any = False
def a__ ( self :List[Any] ):
snake_case_ : Tuple = UperNetModelTester(self )
snake_case_ : List[Any] = ConfigTester(self ,config_class=_UpperCamelCase ,has_text_modality=_UpperCamelCase ,hidden_size=3_7 )
def a__ ( 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 a__ ( self :List[Any] ):
return
def a__ ( self :Optional[Any] ):
snake_case_ , snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : List[Any] = model_class(_UpperCamelCase )
snake_case_ : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[Any] = [*signature.parameters.keys()]
snake_case_ : Optional[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,_UpperCamelCase )
def a__ ( self :Optional[Any] ):
snake_case_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCamelCase )
@unittest.skip(reason="""UperNet does not use inputs_embeds""" )
def a__ ( self :List[str] ):
pass
@unittest.skip(reason="""UperNet does not support input and output embeddings""" )
def a__ ( self :Dict ):
pass
@unittest.skip(reason="""UperNet does not have a base model""" )
def a__ ( self :Union[str, Any] ):
pass
@unittest.skip(reason="""UperNet does not have a base model""" )
def a__ ( self :Union[str, Any] ):
pass
@require_torch_multi_gpu
@unittest.skip(reason="""UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def a__ ( self :str ):
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def a__ ( self :Union[str, Any] ):
pass
def a__ ( self :Optional[int] ):
def check_hidden_states_output(_UpperCamelCase :Dict ,_UpperCamelCase :Union[str, Any] ,_UpperCamelCase :str ):
snake_case_ : Any = model_class(_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ : List[Any] = model(**self._prepare_for_class(_UpperCamelCase ,_UpperCamelCase ) )
snake_case_ : Optional[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
snake_case_ : Any = self.model_tester.num_stages
self.assertEqual(len(_UpperCamelCase ) ,expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,)
snake_case_ , snake_case_ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : List[Any] = True
check_hidden_states_output(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ : List[str] = True
check_hidden_states_output(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
def a__ ( self :int ):
snake_case_ , snake_case_ : int = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : str = _config_zero_init(_UpperCamelCase )
snake_case_ : str = _config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
snake_case_ : str = model_class(config=_UpperCamelCase )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() ,[0.0, 1.0] ,msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,)
@unittest.skip(reason="""UperNet does not have tied weights""" )
def a__ ( self :Union[str, Any] ):
pass
@slow
def a__ ( self :Tuple ):
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : List[Any] = UperNetForSemanticSegmentation.from_pretrained(_UpperCamelCase )
self.assertIsNotNone(_UpperCamelCase )
def UpperCAmelCase ( ):
'''simple docstring'''
snake_case_ : Tuple = hf_hub_download(
repo_id="""hf-internal-testing/fixtures_ade20k""" , repo_type="""dataset""" , filename="""ADE_val_00000001.jpg""" )
snake_case_ : Optional[Any] = Image.open(lowerCamelCase_ ).convert("""RGB""" )
return image
@require_torch
@require_vision
@slow
class __UpperCamelCase ( unittest.TestCase ):
def a__ ( self :Optional[int] ):
snake_case_ : Union[str, Any] = AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny""" )
snake_case_ : Optional[Any] = UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny""" ).to(_UpperCamelCase )
snake_case_ : Optional[int] = prepare_img()
snake_case_ : int = processor(images=_UpperCamelCase ,return_tensors="""pt""" ).to(_UpperCamelCase )
with torch.no_grad():
snake_case_ : str = model(**_UpperCamelCase )
snake_case_ : List[str] = torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2) )
self.assertEqual(outputs.logits.shape ,_UpperCamelCase )
snake_case_ : Dict = torch.tensor(
[[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]] ).to(_UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] ,_UpperCamelCase ,atol=1E-4 ) )
def a__ ( self :Optional[int] ):
snake_case_ : int = AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny""" )
snake_case_ : List[Any] = UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny""" ).to(_UpperCamelCase )
snake_case_ : Optional[int] = prepare_img()
snake_case_ : Tuple = processor(images=_UpperCamelCase ,return_tensors="""pt""" ).to(_UpperCamelCase )
with torch.no_grad():
snake_case_ : Optional[Any] = model(**_UpperCamelCase )
snake_case_ : Tuple = torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2) )
self.assertEqual(outputs.logits.shape ,_UpperCamelCase )
snake_case_ : Optional[int] = torch.tensor(
[[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]] ).to(_UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] ,_UpperCamelCase ,atol=1E-4 ) ) | 8 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def UpperCAmelCase ( lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :int , lowerCamelCase_ :List[Any] , lowerCamelCase_ :List[Any] , lowerCamelCase_ :str=True , lowerCamelCase_ :str="pt" ):
'''simple docstring'''
snake_case_ : Tuple = {"""add_prefix_space""": True} if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and not line.startswith(""" """ ) else {}
snake_case_ : Union[str, Any] = padding_side
return tokenizer(
[line] , max_length=lowerCamelCase_ , padding="""max_length""" if pad_to_max_length else None , truncation=lowerCamelCase_ , return_tensors=lowerCamelCase_ , add_special_tokens=lowerCamelCase_ , **lowerCamelCase_ , )
def UpperCAmelCase ( lowerCamelCase_ :Optional[int] , lowerCamelCase_ :str , lowerCamelCase_ :Any=None , ):
'''simple docstring'''
snake_case_ : Dict = input_ids.ne(lowerCamelCase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __UpperCamelCase ( lowercase__ ):
def __init__( self :List[Any] ,_UpperCamelCase :List[Any] ,_UpperCamelCase :Any ,_UpperCamelCase :int ,_UpperCamelCase :Optional[Any] ,_UpperCamelCase :Any="train" ,_UpperCamelCase :Optional[int]=None ,_UpperCamelCase :int=None ,_UpperCamelCase :List[Any]=None ,_UpperCamelCase :Optional[int]="" ,):
super().__init__()
snake_case_ : List[str] = Path(_UpperCamelCase ).joinpath(type_path + """.source""" )
snake_case_ : int = Path(_UpperCamelCase ).joinpath(type_path + """.target""" )
snake_case_ : Optional[int] = self.get_char_lens(self.src_file )
snake_case_ : List[str] = max_source_length
snake_case_ : str = max_target_length
assert min(self.src_lens ) > 0, F'''found empty line in {self.src_file}'''
snake_case_ : str = tokenizer
snake_case_ : str = prefix
if n_obs is not None:
snake_case_ : int = self.src_lens[:n_obs]
snake_case_ : Tuple = src_lang
snake_case_ : str = tgt_lang
def __len__( self :Any ):
return len(self.src_lens )
def __getitem__( self :List[str] ,_UpperCamelCase :Union[str, Any] ):
snake_case_ : Optional[int] = index + 1 # linecache starts at 1
snake_case_ : Dict = self.prefix + linecache.getline(str(self.src_file ) ,_UpperCamelCase ).rstrip("""\n""" )
snake_case_ : List[Any] = linecache.getline(str(self.tgt_file ) ,_UpperCamelCase ).rstrip("""\n""" )
assert source_line, F'''empty source line for index {index}'''
assert tgt_line, F'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,_UpperCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
snake_case_ : int = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,_UpperCamelCase ) else self.tokenizer
)
snake_case_ : Optional[int] = self.tokenizer.generator if isinstance(self.tokenizer ,_UpperCamelCase ) else self.tokenizer
snake_case_ : Optional[Any] = encode_line(_UpperCamelCase ,_UpperCamelCase ,self.max_source_length ,"""right""" )
snake_case_ : Tuple = encode_line(_UpperCamelCase ,_UpperCamelCase ,self.max_target_length ,"""right""" )
snake_case_ : int = source_inputs["""input_ids"""].squeeze()
snake_case_ : str = target_inputs["""input_ids"""].squeeze()
snake_case_ : Union[str, Any] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def a__ ( _UpperCamelCase :str ):
return [len(_UpperCamelCase ) for x in Path(_UpperCamelCase ).open().readlines()]
def a__ ( self :Optional[int] ,_UpperCamelCase :List[str] ):
snake_case_ : Optional[Any] = torch.stack([x["""input_ids"""] for x in batch] )
snake_case_ : List[Any] = torch.stack([x["""attention_mask"""] for x in batch] )
snake_case_ : Union[str, Any] = torch.stack([x["""decoder_input_ids"""] for x in batch] )
snake_case_ : Optional[Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,_UpperCamelCase )
else self.tokenizer.pad_token_id
)
snake_case_ : Tuple = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,_UpperCamelCase )
else self.tokenizer.pad_token_id
)
snake_case_ : Optional[int] = trim_batch(_UpperCamelCase ,_UpperCamelCase )
snake_case_ , snake_case_ : Dict = trim_batch(_UpperCamelCase ,_UpperCamelCase ,attention_mask=_UpperCamelCase )
snake_case_ : Optional[int] = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__A : List[Any] = getLogger(__name__)
def UpperCAmelCase ( lowerCamelCase_ :List[List] ):
'''simple docstring'''
return list(itertools.chain.from_iterable(lowerCamelCase_ ) )
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : int = get_git_info()
save_json(lowerCamelCase_ , os.path.join(lowerCamelCase_ , """git_log.json""" ) )
def UpperCAmelCase ( lowerCamelCase_ :Optional[int] , lowerCamelCase_ :int , lowerCamelCase_ :Optional[int]=4 , **lowerCamelCase_ :Union[str, Any] ):
'''simple docstring'''
with open(lowerCamelCase_ , """w""" ) as f:
json.dump(lowerCamelCase_ , lowerCamelCase_ , indent=lowerCamelCase_ , **lowerCamelCase_ )
def UpperCAmelCase ( lowerCamelCase_ :List[Any] ):
'''simple docstring'''
with open(lowerCamelCase_ ) as f:
return json.load(lowerCamelCase_ )
def UpperCAmelCase ( ):
'''simple docstring'''
snake_case_ : Optional[Any] = git.Repo(search_parent_directories=lowerCamelCase_ )
snake_case_ : List[str] = {
"""repo_id""": str(lowerCamelCase_ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def UpperCAmelCase ( lowerCamelCase_ :Callable , lowerCamelCase_ :Iterable ):
'''simple docstring'''
return list(map(lowerCamelCase_ , lowerCamelCase_ ) )
def UpperCAmelCase ( lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :int ):
'''simple docstring'''
with open(lowerCamelCase_ , """wb""" ) as f:
return pickle.dump(lowerCamelCase_ , lowerCamelCase_ )
def UpperCAmelCase ( lowerCamelCase_ :Dict ):
'''simple docstring'''
def remove_articles(lowerCamelCase_ :str ):
return re.sub(R"""\b(a|an|the)\b""" , """ """ , lowerCamelCase_ )
def white_space_fix(lowerCamelCase_ :Optional[Any] ):
return " ".join(text.split() )
def remove_punc(lowerCamelCase_ :Tuple ):
snake_case_ : Union[str, Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowerCamelCase_ :Optional[Any] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase_ ) ) ) )
def UpperCAmelCase ( lowerCamelCase_ :List[Any] , lowerCamelCase_ :Optional[int] ):
'''simple docstring'''
snake_case_ : List[Any] = normalize_answer(lowerCamelCase_ ).split()
snake_case_ : Optional[int] = normalize_answer(lowerCamelCase_ ).split()
snake_case_ : List[Any] = Counter(lowerCamelCase_ ) & Counter(lowerCamelCase_ )
snake_case_ : Optional[Any] = sum(common.values() )
if num_same == 0:
return 0
snake_case_ : Optional[Any] = 1.0 * num_same / len(lowerCamelCase_ )
snake_case_ : Union[str, Any] = 1.0 * num_same / len(lowerCamelCase_ )
snake_case_ : Optional[Any] = (2 * precision * recall) / (precision + recall)
return fa
def UpperCAmelCase ( lowerCamelCase_ :Optional[Any] , lowerCamelCase_ :Union[str, Any] ):
'''simple docstring'''
return normalize_answer(lowerCamelCase_ ) == normalize_answer(lowerCamelCase_ )
def UpperCAmelCase ( lowerCamelCase_ :List[str] , lowerCamelCase_ :List[str] ):
'''simple docstring'''
assert len(lowerCamelCase_ ) == len(lowerCamelCase_ )
snake_case_ : Optional[int] = 0
for hypo, pred in zip(lowerCamelCase_ , lowerCamelCase_ ):
em += exact_match_score(lowerCamelCase_ , lowerCamelCase_ )
if len(lowerCamelCase_ ) > 0:
em /= len(lowerCamelCase_ )
return {"em": em}
def UpperCAmelCase ( lowerCamelCase_ :Any ):
'''simple docstring'''
return model_prefix.startswith("""rag""" )
def UpperCAmelCase ( lowerCamelCase_ :Optional[int] , lowerCamelCase_ :Any , lowerCamelCase_ :Union[str, Any] ):
'''simple docstring'''
snake_case_ : List[str] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
snake_case_ : Optional[int] = """dropout_rate"""
for p in extra_params:
if getattr(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
if not hasattr(lowerCamelCase_ , lowerCamelCase_ ) and not hasattr(lowerCamelCase_ , equivalent_param[p] ):
logger.info("""config doesn't have a `{}` attribute""".format(lowerCamelCase_ ) )
delattr(lowerCamelCase_ , lowerCamelCase_ )
continue
snake_case_ : str = p if hasattr(lowerCamelCase_ , lowerCamelCase_ ) else equivalent_param[p]
setattr(lowerCamelCase_ , lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
delattr(lowerCamelCase_ , lowerCamelCase_ )
return hparams, config | 8 | 1 |
from math import pi
def __A ( __lowerCAmelCase , __lowerCAmelCase )-> float:
"""simple docstring"""
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 39 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_A = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
"processing_speech_to_text": ["Speech2TextProcessor"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ["Speech2TextTokenizer"]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ["Speech2TextFeatureExtractor"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFSpeech2TextForConditionalGeneration",
"TFSpeech2TextModel",
"TFSpeech2TextPreTrainedModel",
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"Speech2TextForConditionalGeneration",
"Speech2TextModel",
"Speech2TextPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 231 | 0 |
'''simple docstring'''
from __future__ import annotations
__snake_case : Optional[int] = list[tuple[int, int]]
__snake_case : Optional[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],
]
__snake_case : Tuple = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class __UpperCAmelCase :
'''simple docstring'''
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> List[str]:
A_ = pos_x
A_ = pos_y
A_ = (pos_y, pos_x)
A_ = goal_x
A_ = goal_y
A_ = g_cost
A_ = parent
A_ = self.calculate_heuristic()
def __A ( self ) -> float:
A_ = abs(self.pos_x - self.goal_x )
A_ = abs(self.pos_y - self.goal_y )
return dx + dy
def __lt__( self , _SCREAMING_SNAKE_CASE ) -> bool:
return self.f_cost < other.f_cost
class __UpperCAmelCase :
'''simple docstring'''
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
A_ = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , _SCREAMING_SNAKE_CASE )
A_ = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9999 , _SCREAMING_SNAKE_CASE )
A_ = [self.start]
A_ = []
A_ = False
def __A ( self ) -> Path | None:
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
A_ = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
A_ = True
return self.retrace_path(_SCREAMING_SNAKE_CASE )
self.closed_nodes.append(_SCREAMING_SNAKE_CASE )
A_ = self.get_successors(_SCREAMING_SNAKE_CASE )
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(_SCREAMING_SNAKE_CASE )
else:
# retrieve the best current path
A_ = self.open_nodes.pop(self.open_nodes.index(_SCREAMING_SNAKE_CASE ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(_SCREAMING_SNAKE_CASE )
else:
self.open_nodes.append(_SCREAMING_SNAKE_CASE )
if not self.reached:
return [self.start.pos]
return None
def __A ( self , _SCREAMING_SNAKE_CASE ) -> list[Node]:
A_ = []
for action in delta:
A_ = parent.pos_x + action[1]
A_ = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_SCREAMING_SNAKE_CASE ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , _SCREAMING_SNAKE_CASE , ) )
return successors
def __A ( self , _SCREAMING_SNAKE_CASE ) -> Path:
A_ = node
A_ = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
A_ = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
__snake_case : Tuple = (0, 0)
__snake_case : Any = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print('------')
__snake_case : Optional[Any] = GreedyBestFirst(init, goal)
__snake_case : str = greedy_bf.search()
if path:
for pos_x, pos_y in path:
__snake_case : Optional[Any] = 2
for elem in grid:
print(elem)
| 18 | '''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( _UpperCamelCase : int | str ) -> bool:
A_ = str(_UpperCamelCase )
return n == n[::-1]
def _UpperCAmelCase ( _UpperCamelCase : int = 1_00_00_00 ) -> Any:
A_ = 0
for i in range(1, _UpperCamelCase ):
if is_palindrome(_UpperCamelCase ) and is_palindrome(bin(_UpperCamelCase ).split('''b''' )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 18 | 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
a_ = logging.get_logger(__name__)
a_ = {
'''sail/poolformer_s12''': '''https://huggingface.co/sail/poolformer_s12/resolve/main/config.json''',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class lowercase__ ( _UpperCAmelCase ):
a_ ="""poolformer"""
def __init__( self , __UpperCAmelCase=3 , __UpperCAmelCase=16 , __UpperCAmelCase=16 , __UpperCAmelCase=3 , __UpperCAmelCase=4.0 , __UpperCAmelCase=[2, 2, 6, 2] , __UpperCAmelCase=[64, 128, 320, 512] , __UpperCAmelCase=[7, 3, 3, 3] , __UpperCAmelCase=[4, 2, 2, 2] , __UpperCAmelCase=[2, 1, 1, 1] , __UpperCAmelCase=4 , __UpperCAmelCase=0.0 , __UpperCAmelCase="gelu" , __UpperCAmelCase=True , __UpperCAmelCase=1E-5 , __UpperCAmelCase=0.02 , **__UpperCAmelCase , )-> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = patch_size
lowerCAmelCase__ = stride
lowerCAmelCase__ = padding
lowerCAmelCase__ = pool_size
lowerCAmelCase__ = hidden_sizes
lowerCAmelCase__ = mlp_ratio
lowerCAmelCase__ = depths
lowerCAmelCase__ = patch_sizes
lowerCAmelCase__ = strides
lowerCAmelCase__ = num_encoder_blocks
lowerCAmelCase__ = drop_path_rate
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = use_layer_scale
lowerCAmelCase__ = layer_scale_init_value
lowerCAmelCase__ = initializer_range
super().__init__(**__UpperCAmelCase )
class lowercase__ ( _UpperCAmelCase ):
a_ =version.parse("""1.11""" )
@property
def UpperCAmelCase ( self )-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def UpperCAmelCase ( self )-> float:
'''simple docstring'''
return 2E-3
| 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 |
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[Any] = name
snake_case_ : str = value
snake_case_ : Tuple = weight
def __repr__(self ) -> Tuple:
'''simple docstring'''
return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
return self.value
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return self.name
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return self.weight
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
return self.value / self.weight
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Optional[int] = []
for i in range(len(_UpperCamelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Union[str, Any] = sorted(_UpperCamelCase , key=_UpperCamelCase , reverse=_UpperCamelCase )
snake_case_ : Optional[int] = []
snake_case_ : Optional[int] = 0.0, 0.0
for i in range(len(_UpperCamelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 351 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCamelCase_ ( _UpperCamelCase ) -> tuple:
"""simple docstring"""
return (data["data"], data["target"])
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> XGBClassifier:
"""simple docstring"""
snake_case_ : Optional[Any] = XGBClassifier()
classifier.fit(_UpperCamelCase , _UpperCamelCase )
return classifier
def lowerCamelCase_ ( ) -> None:
"""simple docstring"""
snake_case_ : Optional[Any] = load_iris()
snake_case_ , snake_case_ : str = data_handling(_UpperCamelCase )
snake_case_ , snake_case_ , snake_case_ , snake_case_ : Dict = train_test_split(
_UpperCamelCase , _UpperCamelCase , test_size=0.25 )
snake_case_ : List[str] = iris['''target_names''']
# Create an XGBoost Classifier from the training data
snake_case_ : int = xgboost(_UpperCamelCase , _UpperCamelCase )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , display_labels=_UpperCamelCase , cmap='''Blues''' , normalize='''true''' , )
plt.title('''Normalized Confusion Matrix - IRIS Dataset''' )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 279 | 0 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class lowercase ( unittest.TestCase ):
def a__ ( self ) -> Optional[int]:
debug_launcher(test_script.main )
def a__ ( self ) -> int:
debug_launcher(test_ops.main )
| 26 |
'''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
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = """▁"""
UpperCamelCase_ = {
"""vocab_file""": """vocab.json""",
"""spm_file""": """sentencepiece.bpe.model""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
UpperCamelCase_ = {
"""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""",
},
}
UpperCamelCase_ = {
"""facebook/m2m100_418M""": 10_24,
}
# fmt: off
UpperCamelCase_ = {
"""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 ):
__lowerCAmelCase : Optional[Any] = VOCAB_FILES_NAMES
__lowerCAmelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase : Dict = ["""input_ids""", """attention_mask"""]
__lowerCAmelCase : List[int] = []
__lowerCAmelCase : List[int] = []
def __init__( self , snake_case_ , snake_case_ , snake_case_=None , snake_case_=None , snake_case_="<s>" , snake_case_="</s>" , snake_case_="</s>" , snake_case_="<pad>" , snake_case_="<unk>" , snake_case_="m2m100" , snake_case_ = None , snake_case_=8 , **snake_case_ , ):
_lowerCAmelCase : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
_lowerCAmelCase : Optional[Any] = language_codes
_lowerCAmelCase : Tuple = FAIRSEQ_LANGUAGE_CODES[language_codes]
_lowerCAmelCase : str = {lang_code: f'__{lang_code}__' for lang_code in fairseq_language_code}
_lowerCAmelCase : int = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(snake_case_ )
for lang_code in fairseq_language_code
if self.get_lang_token(snake_case_ ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=snake_case_ , tgt_lang=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , sep_token=snake_case_ , unk_token=snake_case_ , pad_token=snake_case_ , language_codes=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=snake_case_ , **snake_case_ , )
_lowerCAmelCase : Optional[int] = vocab_file
_lowerCAmelCase : Any = load_json(snake_case_ )
_lowerCAmelCase : str = {v: k for k, v in self.encoder.items()}
_lowerCAmelCase : Union[str, Any] = spm_file
_lowerCAmelCase : Tuple = load_spm(snake_case_ , self.sp_model_kwargs )
_lowerCAmelCase : int = len(self.encoder )
_lowerCAmelCase : Union[str, Any] = {
self.get_lang_token(snake_case_ ): self.encoder_size + i for i, lang_code in enumerate(snake_case_ )
}
_lowerCAmelCase : List[str] = {lang_code: self.encoder_size + i for i, lang_code in enumerate(snake_case_ )}
_lowerCAmelCase : Optional[Any] = {v: k for k, v in self.lang_token_to_id.items()}
_lowerCAmelCase : Any = src_lang if src_lang is not None else """en"""
_lowerCAmelCase : Optional[int] = tgt_lang
_lowerCAmelCase : Tuple = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
_lowerCAmelCase : List[Any] = num_madeup_words
@property
def __UpperCamelCase ( self ):
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def __UpperCamelCase ( self ):
return self._src_lang
@src_lang.setter
def __UpperCamelCase ( self , snake_case_ ):
_lowerCAmelCase : Any = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __UpperCamelCase ( self , snake_case_ ):
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def __UpperCamelCase ( self , snake_case_ ):
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(snake_case_ , self.encoder[self.unk_token] )
def __UpperCamelCase ( self , snake_case_ ):
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(snake_case_ , self.unk_token )
def __UpperCamelCase ( self , snake_case_ ):
_lowerCAmelCase : Any = []
_lowerCAmelCase : Optional[int] = """"""
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(snake_case_ ) + token
_lowerCAmelCase : Optional[Any] = []
else:
current_sub_tokens.append(snake_case_ )
out_string += self.sp_model.decode(snake_case_ )
return out_string.strip()
def __UpperCamelCase ( self , snake_case_ , snake_case_ = None , snake_case_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case_ , token_ids_a=snake_case_ , already_has_special_tokens=snake_case_ )
_lowerCAmelCase : List[Any] = [1] * len(self.prefix_tokens )
_lowerCAmelCase : Dict = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(snake_case_ )) + suffix_ones
return prefix_ones + ([0] * len(snake_case_ )) + ([0] * len(snake_case_ )) + suffix_ones
def __UpperCamelCase ( self , snake_case_ , snake_case_ = None ):
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 __UpperCamelCase ( self ):
_lowerCAmelCase : Union[str, Any] = {self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
_lowerCAmelCase : int = self.__dict__.copy()
_lowerCAmelCase : str = None
return state
def __setstate__( self , snake_case_ ):
_lowerCAmelCase : List[str] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_lowerCAmelCase : str = {}
_lowerCAmelCase : str = load_spm(self.spm_file , self.sp_model_kwargs )
def __UpperCamelCase ( self , snake_case_ , snake_case_ = None ):
_lowerCAmelCase : Dict = Path(snake_case_ )
if not save_dir.is_dir():
raise OSError(f'{save_directory} should be a directory' )
_lowerCAmelCase : Any = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
)
_lowerCAmelCase : Any = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
)
save_json(self.encoder , snake_case_ )
if os.path.abspath(self.spm_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , snake_case_ )
elif not os.path.isfile(self.spm_file ):
with open(snake_case_ , """wb""" ) as fi:
_lowerCAmelCase : List[str] = self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (str(snake_case_ ), str(snake_case_ ))
def __UpperCamelCase ( self , snake_case_ , snake_case_ = "en" , snake_case_ = None , snake_case_ = "ro" , **snake_case_ , ):
_lowerCAmelCase : Union[str, Any] = src_lang
_lowerCAmelCase : Optional[Any] = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(snake_case_ , snake_case_ , **snake_case_ )
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , **snake_case_ ):
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
_lowerCAmelCase : Dict = src_lang
_lowerCAmelCase : str = self(snake_case_ , add_special_tokens=snake_case_ , **snake_case_ )
_lowerCAmelCase : Union[str, Any] = self.get_lang_id(snake_case_ )
_lowerCAmelCase : Tuple = tgt_lang_id
return inputs
def __UpperCamelCase ( self ):
self.set_src_lang_special_tokens(self.src_lang )
def __UpperCamelCase ( self ):
self.set_tgt_lang_special_tokens(self.tgt_lang )
def __UpperCamelCase ( self , snake_case_ ):
_lowerCAmelCase : Optional[Any] = self.get_lang_token(snake_case_ )
_lowerCAmelCase : List[Any] = self.lang_token_to_id[lang_token]
_lowerCAmelCase : Any = [self.cur_lang_id]
_lowerCAmelCase : Any = [self.eos_token_id]
def __UpperCamelCase ( self , snake_case_ ):
_lowerCAmelCase : Any = self.get_lang_token(snake_case_ )
_lowerCAmelCase : int = self.lang_token_to_id[lang_token]
_lowerCAmelCase : str = [self.cur_lang_id]
_lowerCAmelCase : str = [self.eos_token_id]
def __UpperCamelCase ( self , snake_case_ ):
return self.lang_code_to_token[lang]
def __UpperCamelCase ( self , snake_case_ ):
_lowerCAmelCase : List[str] = self.get_lang_token(snake_case_ )
return self.lang_token_to_id[lang_token]
def _UpperCAmelCase ( _lowerCamelCase : str , _lowerCamelCase : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor:
_lowerCAmelCase : Optional[Any] = sentencepiece.SentencePieceProcessor(**_lowerCamelCase )
spm.Load(str(_lowerCamelCase ) )
return spm
def _UpperCAmelCase ( _lowerCamelCase : str ) -> Union[Dict, List]:
with open(_lowerCamelCase , """r""" ) as f:
return json.load(_lowerCamelCase )
def _UpperCAmelCase ( _lowerCamelCase : Tuple , _lowerCamelCase : str ) -> None:
with open(_lowerCamelCase , """w""" ) as f:
json.dump(_lowerCamelCase , _lowerCamelCase , indent=2 )
| 309 | 0 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def lowerCamelCase_ ( _a : str , _a : Optional[int]="shi-labs/oneformer_demo" ):
'''simple docstring'''
with open(hf_hub_download(_a , _a , repo_type="""dataset""" ) , """r""" ) as f:
UpperCAmelCase_ : int = json.load(_a )
UpperCAmelCase_ : Optional[int] = {}
UpperCAmelCase_ : List[Any] = []
UpperCAmelCase_ : Union[str, Any] = []
for key, info in class_info.items():
UpperCAmelCase_ : List[Any] = info["""name"""]
class_names.append(info["""name"""] )
if info["isthing"]:
thing_ids.append(int(_a ) )
UpperCAmelCase_ : Optional[Any] = thing_ids
UpperCAmelCase_ : Tuple = class_names
return metadata
class _snake_case ( unittest.TestCase ):
'''simple docstring'''
def __init__( self: Union[str, Any] ,lowerCamelCase_: str ,lowerCamelCase_: Tuple=7 ,lowerCamelCase_: Any=3 ,lowerCamelCase_: Optional[int]=30 ,lowerCamelCase_: Optional[int]=400 ,lowerCamelCase_: List[str]=None ,lowerCamelCase_: Any=True ,lowerCamelCase_: List[str]=True ,lowerCamelCase_: Optional[Any]=[0.5, 0.5, 0.5] ,lowerCamelCase_: Optional[Any]=[0.5, 0.5, 0.5] ,lowerCamelCase_: List[Any]=10 ,lowerCamelCase_: List[Any]=False ,lowerCamelCase_: Any=255 ,lowerCamelCase_: Tuple="shi-labs/oneformer_demo" ,lowerCamelCase_: int="ade20k_panoptic.json" ,lowerCamelCase_: Union[str, Any]=10 ,) -> Optional[Any]:
UpperCAmelCase_ : Dict = parent
UpperCAmelCase_ : Optional[Any] = batch_size
UpperCAmelCase_ : Optional[Any] = num_channels
UpperCAmelCase_ : str = min_resolution
UpperCAmelCase_ : List[str] = max_resolution
UpperCAmelCase_ : Union[str, Any] = do_resize
UpperCAmelCase_ : int = {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
UpperCAmelCase_ : List[str] = do_normalize
UpperCAmelCase_ : Dict = image_mean
UpperCAmelCase_ : List[Any] = image_std
UpperCAmelCase_ : Optional[int] = class_info_file
UpperCAmelCase_ : Optional[Any] = prepare_metadata(lowerCamelCase_ ,lowerCamelCase_ )
UpperCAmelCase_ : int = num_text
UpperCAmelCase_ : str = repo_path
# for the post_process_functions
UpperCAmelCase_ : List[str] = 2
UpperCAmelCase_ : Optional[Any] = 10
UpperCAmelCase_ : str = 10
UpperCAmelCase_ : int = 3
UpperCAmelCase_ : Union[str, Any] = 4
UpperCAmelCase_ : List[Any] = num_labels
UpperCAmelCase_ : List[Any] = do_reduce_labels
UpperCAmelCase_ : int = ignore_index
def A__ ( self: str ) -> List[str]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def A__ ( self: Dict ,lowerCamelCase_: Union[str, Any] ,lowerCamelCase_: Dict=False ) -> Optional[int]:
if not batched:
UpperCAmelCase_ : Optional[Any] = image_inputs[0]
if isinstance(lowerCamelCase_ ,Image.Image ):
UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = image.size
else:
UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = image.shape[1], image.shape[2]
if w < h:
UpperCAmelCase_ : Union[str, Any] = int(self.size["""shortest_edge"""] * h / w )
UpperCAmelCase_ : Any = self.size["""shortest_edge"""]
elif w > h:
UpperCAmelCase_ : int = self.size["""shortest_edge"""]
UpperCAmelCase_ : List[str] = int(self.size["""shortest_edge"""] * w / h )
else:
UpperCAmelCase_ : Any = self.size["""shortest_edge"""]
UpperCAmelCase_ : Tuple = self.size["""shortest_edge"""]
else:
UpperCAmelCase_ : Dict = []
for image in image_inputs:
UpperCAmelCase_ , UpperCAmelCase_ : List[str] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCAmelCase_ : str = max(lowerCamelCase_ ,key=lambda lowerCamelCase_ : item[0] )[0]
UpperCAmelCase_ : Optional[int] = max(lowerCamelCase_ ,key=lambda lowerCamelCase_ : item[1] )[1]
return expected_height, expected_width
def A__ ( self: Dict ) -> List[str]:
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) ,masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) ,)
@require_torch
@require_vision
class _snake_case ( __snake_case , unittest.TestCase ):
'''simple docstring'''
A__ : Tuple = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
A__ : Optional[int] = image_processing_class
def A__ ( self: Any ) -> Dict:
UpperCAmelCase_ : Dict = OneFormerImageProcessorTester(self )
@property
def A__ ( self: str ) -> Dict:
return self.image_processing_tester.prepare_image_processor_dict()
def A__ ( self: str ) -> Tuple:
UpperCAmelCase_ : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase_ ,"""image_mean""" ) )
self.assertTrue(hasattr(lowerCamelCase_ ,"""image_std""" ) )
self.assertTrue(hasattr(lowerCamelCase_ ,"""do_normalize""" ) )
self.assertTrue(hasattr(lowerCamelCase_ ,"""do_resize""" ) )
self.assertTrue(hasattr(lowerCamelCase_ ,"""size""" ) )
self.assertTrue(hasattr(lowerCamelCase_ ,"""ignore_index""" ) )
self.assertTrue(hasattr(lowerCamelCase_ ,"""class_info_file""" ) )
self.assertTrue(hasattr(lowerCamelCase_ ,"""num_text""" ) )
self.assertTrue(hasattr(lowerCamelCase_ ,"""repo_path""" ) )
self.assertTrue(hasattr(lowerCamelCase_ ,"""metadata""" ) )
self.assertTrue(hasattr(lowerCamelCase_ ,"""do_reduce_labels""" ) )
def A__ ( self: List[Any] ) -> Optional[int]:
pass
def A__ ( self: int ) -> str:
# Initialize image_processor
UpperCAmelCase_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase_ : Optional[int] = prepare_image_inputs(self.image_processing_tester ,equal_resolution=lowerCamelCase_ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase_ ,Image.Image )
# Test not batched input
UpperCAmelCase_ : int = image_processor(image_inputs[0] ,["""semantic"""] ,return_tensors="""pt""" ).pixel_values
UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = self.image_processing_tester.get_expected_values(lowerCamelCase_ )
self.assertEqual(
encoded_images.shape ,(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,)
# Test batched
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = self.image_processing_tester.get_expected_values(lowerCamelCase_ ,batched=lowerCamelCase_ )
UpperCAmelCase_ : int = image_processor(
lowerCamelCase_ ,["""semantic"""] * len(lowerCamelCase_ ) ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) ,)
def A__ ( self: List[Any] ) -> Optional[int]:
# Initialize image_processor
UpperCAmelCase_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase_ : str = prepare_image_inputs(self.image_processing_tester ,equal_resolution=lowerCamelCase_ ,numpify=lowerCamelCase_ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase_ ,np.ndarray )
# Test not batched input
UpperCAmelCase_ : Optional[int] = image_processor(image_inputs[0] ,["""semantic"""] ,return_tensors="""pt""" ).pixel_values
UpperCAmelCase_ , UpperCAmelCase_ : str = self.image_processing_tester.get_expected_values(lowerCamelCase_ )
self.assertEqual(
encoded_images.shape ,(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,)
# Test batched
UpperCAmelCase_ , UpperCAmelCase_ : int = self.image_processing_tester.get_expected_values(lowerCamelCase_ ,batched=lowerCamelCase_ )
UpperCAmelCase_ : Union[str, Any] = image_processor(
lowerCamelCase_ ,["""semantic"""] * len(lowerCamelCase_ ) ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) ,)
def A__ ( self: List[str] ) -> str:
# Initialize image_processor
UpperCAmelCase_ : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase_ : Any = prepare_image_inputs(self.image_processing_tester ,equal_resolution=lowerCamelCase_ ,torchify=lowerCamelCase_ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase_ ,torch.Tensor )
# Test not batched input
UpperCAmelCase_ : int = image_processor(image_inputs[0] ,["""semantic"""] ,return_tensors="""pt""" ).pixel_values
UpperCAmelCase_ , UpperCAmelCase_ : Dict = self.image_processing_tester.get_expected_values(lowerCamelCase_ )
self.assertEqual(
encoded_images.shape ,(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,)
# Test batched
UpperCAmelCase_ , UpperCAmelCase_ : Any = self.image_processing_tester.get_expected_values(lowerCamelCase_ ,batched=lowerCamelCase_ )
UpperCAmelCase_ : Union[str, Any] = image_processor(
lowerCamelCase_ ,["""semantic"""] * len(lowerCamelCase_ ) ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) ,)
def A__ ( self: Tuple ,lowerCamelCase_: Union[str, Any]=False ,lowerCamelCase_: List[Any]=False ,lowerCamelCase_: int="np" ) -> int:
UpperCAmelCase_ : str = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
UpperCAmelCase_ : List[str] = self.image_processing_tester.num_labels
UpperCAmelCase_ : int = None
UpperCAmelCase_ : Tuple = None
UpperCAmelCase_ : Any = prepare_image_inputs(self.image_processing_tester ,equal_resolution=lowerCamelCase_ )
if with_segmentation_maps:
UpperCAmelCase_ : Optional[Any] = num_labels
if is_instance_map:
UpperCAmelCase_ : Tuple = list(range(lowerCamelCase_ ) ) * 2
UpperCAmelCase_ : Tuple = dict(enumerate(lowerCamelCase_ ) )
UpperCAmelCase_ : Any = [
np.random.randint(0 ,high * 2 ,(img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
UpperCAmelCase_ : int = [Image.fromarray(lowerCamelCase_ ) for annotation in annotations]
UpperCAmelCase_ : Tuple = image_processor(
lowerCamelCase_ ,["""semantic"""] * len(lowerCamelCase_ ) ,lowerCamelCase_ ,return_tensors="""pt""" ,instance_id_to_semantic_id=lowerCamelCase_ ,pad_and_return_pixel_mask=lowerCamelCase_ ,)
return inputs
def A__ ( self: str ) -> str:
pass
def A__ ( self: str ) -> Tuple:
def common(lowerCamelCase_: List[Any]=False ,lowerCamelCase_: Optional[int]=None ):
UpperCAmelCase_ : Any = self.comm_get_image_processor_inputs(
with_segmentation_maps=lowerCamelCase_ ,is_instance_map=lowerCamelCase_ ,segmentation_type=lowerCamelCase_ )
UpperCAmelCase_ : str = inputs["""mask_labels"""]
UpperCAmelCase_ : Any = inputs["""class_labels"""]
UpperCAmelCase_ : Optional[Any] = inputs["""pixel_values"""]
UpperCAmelCase_ : str = inputs["""text_inputs"""]
# check the batch_size
for mask_label, class_label, text_input in zip(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ):
self.assertEqual(mask_label.shape[0] ,class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] ,pixel_values.shape[2:] )
self.assertEqual(len(lowerCamelCase_ ) ,self.image_processing_tester.num_text )
common()
common(is_instance_map=lowerCamelCase_ )
common(is_instance_map=lowerCamelCase_ ,segmentation_type="""pil""" )
common(is_instance_map=lowerCamelCase_ ,segmentation_type="""pil""" )
def A__ ( self: List[Any] ) -> Tuple:
UpperCAmelCase_ : str = np.zeros((20, 50) )
UpperCAmelCase_ : Tuple = 1
UpperCAmelCase_ : str = 1
UpperCAmelCase_ : Optional[Any] = 1
UpperCAmelCase_ : Union[str, Any] = binary_mask_to_rle(lowerCamelCase_ )
self.assertEqual(len(lowerCamelCase_ ) ,4 )
self.assertEqual(rle[0] ,21 )
self.assertEqual(rle[1] ,45 )
def A__ ( self: List[Any] ) -> str:
UpperCAmelCase_ : Dict = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes ,max_seq_length=77 ,task_seq_length=77 ,class_info_file="""ade20k_panoptic.json""" ,num_text=self.image_processing_tester.num_text ,repo_path="""shi-labs/oneformer_demo""" ,)
UpperCAmelCase_ : Any = self.image_processing_tester.get_fake_oneformer_outputs()
UpperCAmelCase_ : Dict = fature_extractor.post_process_semantic_segmentation(lowerCamelCase_ )
self.assertEqual(len(lowerCamelCase_ ) ,self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape ,(
self.image_processing_tester.height,
self.image_processing_tester.width,
) ,)
UpperCAmelCase_ : str = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
UpperCAmelCase_ : Dict = fature_extractor.post_process_semantic_segmentation(lowerCamelCase_ ,target_sizes=lowerCamelCase_ )
self.assertEqual(segmentation[0].shape ,target_sizes[0] )
def A__ ( self: int ) -> Optional[int]:
UpperCAmelCase_ : Optional[int] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes ,max_seq_length=77 ,task_seq_length=77 ,class_info_file="""ade20k_panoptic.json""" ,num_text=self.image_processing_tester.num_text ,repo_path="""shi-labs/oneformer_demo""" ,)
UpperCAmelCase_ : Any = self.image_processing_tester.get_fake_oneformer_outputs()
UpperCAmelCase_ : Dict = image_processor.post_process_instance_segmentation(lowerCamelCase_ ,threshold=0 )
self.assertTrue(len(lowerCamelCase_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) ,lowerCamelCase_ )
self.assertEqual(
el["""segmentation"""].shape ,(self.image_processing_tester.height, self.image_processing_tester.width) )
def A__ ( self: int ) -> List[Any]:
UpperCAmelCase_ : Optional[int] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes ,max_seq_length=77 ,task_seq_length=77 ,class_info_file="""ade20k_panoptic.json""" ,num_text=self.image_processing_tester.num_text ,repo_path="""shi-labs/oneformer_demo""" ,)
UpperCAmelCase_ : List[str] = self.image_processing_tester.get_fake_oneformer_outputs()
UpperCAmelCase_ : Dict = image_processor.post_process_panoptic_segmentation(lowerCamelCase_ ,threshold=0 )
self.assertTrue(len(lowerCamelCase_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) ,lowerCamelCase_ )
self.assertEqual(
el["""segmentation"""].shape ,(self.image_processing_tester.height, self.image_processing_tester.width) )
| 59 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase_ = ['''small''', '''medium''', '''large''']
UpperCamelCase_ = '''lm_head.decoder.weight'''
UpperCamelCase_ = '''lm_head.weight'''
def lowerCamelCase_ ( _a : str , _a : str ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = torch.load(_a )
UpperCAmelCase_ : Tuple = d.pop(_a )
os.makedirs(_a , exist_ok=_a )
torch.save(_a , os.path.join(_a , _a ) )
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser()
parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
UpperCamelCase_ = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
UpperCamelCase_ = os.path.join(args.dialogpt_path, F"{MODEL}_ft.pkl")
UpperCamelCase_ = F"./DialoGPT-{MODEL}"
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 59 | 1 |
"""simple docstring"""
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_ ( _a):
def __init__( self , a , a ) -> List[Any]:
super().__init__()
self.register_modules(unet=a , scheduler=a )
@torch.no_grad()
def __call__( self , a = 1 , a = None , a = 5_0 , a = "pil" , a = True , **a , ) -> Union[ImagePipelineOutput, Tuple]:
lowercase__ : Union[str, Any] = torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=a , )
lowercase__ : Tuple = image.to(self.device )
# set step values
self.scheduler.set_timesteps(a )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowercase__ : Dict = self.unet(a , a ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
lowercase__ : Optional[int] = self.scheduler.step(a , a , a ).prev_sample
lowercase__ : Dict = (image / 2 + 0.5).clamp(0 , 1 )
lowercase__ : Dict = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowercase__ : Union[str, Any] = self.numpy_to_pil(a )
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=a ), "This is a local test"
| 77 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self : Tuple , __SCREAMING_SNAKE_CASE : Any ) -> List[Any]:
lowerCamelCase_ = data
lowerCamelCase_ = None
class a :
def __init__( self : Union[str, Any] ) -> List[Any]:
lowerCamelCase_ = None
def UpperCamelCase ( self : Dict ) -> Optional[int]:
lowerCamelCase_ = self.head
while temp is not None:
print(temp.data , end=' ' )
lowerCamelCase_ = temp.next
print()
def UpperCamelCase ( self : List[Any] , __SCREAMING_SNAKE_CASE : Any ) -> Union[str, Any]:
lowerCamelCase_ = Node(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = self.head
lowerCamelCase_ = new_node
def UpperCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : int ) -> Union[str, Any]:
if node_data_a == node_data_a:
return
else:
lowerCamelCase_ = self.head
while node_a is not None and node_a.data != node_data_a:
lowerCamelCase_ = node_a.next
lowerCamelCase_ = self.head
while node_a is not None and node_a.data != node_data_a:
lowerCamelCase_ = node_a.next
if node_a is None or node_a is None:
return
lowerCamelCase_ , lowerCamelCase_ = node_a.data, node_a.data
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Optional[int] = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print('''After swapping''')
ll.print_list()
| 183 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
metadata={"""help""": """The output directory where the model will be written."""} , )
_SCREAMING_SNAKE_CASE :str = field(
metadata={
"""help""": (
"""The encoder model checkpoint for weights initialization."""
"""Don't set if you want to train an encoder model from scratch."""
)
} , )
_SCREAMING_SNAKE_CASE :str = field(
metadata={
"""help""": (
"""The decoder model checkpoint for weights initialization."""
"""Don't set if you want to train a decoder model from scratch."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""})
def _lowercase ( ) -> int:
SCREAMING_SNAKE_CASE__ : Any = HfArgumentParser((ModelArguments,) )
((SCREAMING_SNAKE_CASE__) , ) : Tuple = parser.parse_args_into_dataclasses()
# Load pretrained model and tokenizer
# Use explicit specified encoder config
if model_args.encoder_config_name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoConfig.from_pretrained(model_args.encoder_config_name )
# Use pretrained encoder model's config
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )
# Use explicit specified decoder config
if model_args.decoder_config_name:
SCREAMING_SNAKE_CASE__ : int = AutoConfig.from_pretrained(model_args.decoder_config_name )
# Use pretrained decoder model's config
else:
SCREAMING_SNAKE_CASE__ : Tuple = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )
# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
SCREAMING_SNAKE_CASE__ : Union[str, Any] = True
SCREAMING_SNAKE_CASE__ : Optional[int] = True
SCREAMING_SNAKE_CASE__ : Tuple = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path , decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path , encoder_config=__lowerCAmelCase , decoder_config=__lowerCAmelCase , )
# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
SCREAMING_SNAKE_CASE__ : Tuple = decoder_config.decoder_start_token_id
SCREAMING_SNAKE_CASE__ : List[Any] = decoder_config.pad_token_id
if decoder_start_token_id is None:
SCREAMING_SNAKE_CASE__ : Dict = decoder_config.bos_token_id
if pad_token_id is None:
SCREAMING_SNAKE_CASE__ : List[Any] = decoder_config.eos_token_id
# This is necessary to make Flax's generate() work
SCREAMING_SNAKE_CASE__ : Optional[Any] = decoder_config.eos_token_id
SCREAMING_SNAKE_CASE__ : List[Any] = decoder_start_token_id
SCREAMING_SNAKE_CASE__ : Tuple = pad_token_id
SCREAMING_SNAKE_CASE__ : Tuple = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
SCREAMING_SNAKE_CASE__ : str = tokenizer.convert_ids_to_tokens(model.config.pad_token_id )
model.save_pretrained(model_args.output_dir )
image_processor.save_pretrained(model_args.output_dir )
tokenizer.save_pretrained(model_args.output_dir )
if __name__ == "__main__":
main()
| 56 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a :Optional[int] = logging.get_logger(__name__)
def _lowercase ( __lowerCAmelCase ) -> List[int]:
if isinstance(__lowerCAmelCase , np.ndarray ):
return list(tensor.shape )
SCREAMING_SNAKE_CASE__ : int = tf.shape(__lowerCAmelCase )
if tensor.shape == tf.TensorShape(__lowerCAmelCase ):
return dynamic
SCREAMING_SNAKE_CASE__ : List[Any] = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCAmelCase )]
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1E-9 , axis=__lowerCAmelCase , name=__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=1E-5 , __lowerCAmelCase=-1 ) -> List[Any]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )
# Get mean and variance on the axis to be normalized
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = tf.nn.moments(__lowerCAmelCase , axes=[axis] , keepdims=__lowerCAmelCase )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
SCREAMING_SNAKE_CASE__ : str = [1] * inputs.shape.rank
SCREAMING_SNAKE_CASE__ : Optional[int] = shape_list(__lowerCAmelCase )[axis]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tf.reshape(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = tf.reshape(__lowerCAmelCase , __lowerCAmelCase )
# Compute layer normalization using the batch_normalization
# function.
SCREAMING_SNAKE_CASE__ : Any = tf.nn.batch_normalization(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , offset=__lowerCAmelCase , scale=__lowerCAmelCase , variance_epsilon=__lowerCAmelCase , )
return outputs
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=0 , __lowerCAmelCase=-1 ) -> Optional[Any]:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tf.shape(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
SCREAMING_SNAKE_CASE__ : int = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(__lowerCAmelCase , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> tf.Tensor:
if not isinstance(__lowerCAmelCase , tf.Tensor ):
SCREAMING_SNAKE_CASE__ : Dict = tf.convert_to_tensor(__lowerCAmelCase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
SCREAMING_SNAKE_CASE__ : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
SCREAMING_SNAKE_CASE__ : Any = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = "input_ids" ) -> None:
tf.debugging.assert_less(
__lowerCAmelCase , tf.cast(__lowerCAmelCase , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCAmelCase )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = 6_4512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
SCREAMING_SNAKE_CASE__ : List[str] = [x for x in data if len(__lowerCAmelCase ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"""The following attributes cannot be saved to HDF5 file because """
F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
F'''bytes: {bad_attributes}''' )
SCREAMING_SNAKE_CASE__ : Any = np.asarray(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = 1
SCREAMING_SNAKE_CASE__ : Optional[int] = np.array_split(__lowerCAmelCase , __lowerCAmelCase )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
SCREAMING_SNAKE_CASE__ : List[str] = np.array_split(__lowerCAmelCase , __lowerCAmelCase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : List[str] = chunk_data
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = data
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
if name in group.attrs:
SCREAMING_SNAKE_CASE__ : Optional[Any] = [n.decode("""utf8""" ) if hasattr(__lowerCAmelCase , """decode""" ) else n for n in group.attrs[name]]
else:
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : List[str] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(__lowerCAmelCase , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def _lowercase ( __lowerCAmelCase ) -> List[Any]:
def _expand_single_ad_tensor(__lowerCAmelCase ):
if isinstance(__lowerCAmelCase , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(__lowerCAmelCase , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , __lowerCAmelCase )
| 56 | 1 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :List[str] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__SCREAMING_SNAKE_CASE :Tuple = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__SCREAMING_SNAKE_CASE :Optional[int] = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 22 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowercase__ : Tuple = re.compile(R"\b(a|an|the)\b", re.UNICODE)
lowercase__ : Optional[int] = None
def lowerCamelCase__ ( ):
'''simple docstring'''
snake_case_ = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0." )
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file." )
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions." )
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout)." )
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer." )
parser.add_argument(
"--na-prob-thresh" , "-t" , type=_A , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=_A , help="Save precision-recall curves to directory." )
parser.add_argument("--verbose" , "-v" , action="store_true" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case_ = bool(qa["answers"]["text"] )
return qid_to_has_ans
def lowerCamelCase__ ( _A ):
'''simple docstring'''
def remove_articles(_A ):
return ARTICLES_REGEX.sub(" " , _A )
def white_space_fix(_A ):
return " ".join(text.split() )
def remove_punc(_A ):
snake_case_ = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_A ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_A ) ) ) )
def lowerCamelCase__ ( _A ):
'''simple docstring'''
if not s:
return []
return normalize_answer(_A ).split()
def lowerCamelCase__ ( _A , _A ):
'''simple docstring'''
return int(normalize_answer(_A ) == normalize_answer(_A ) )
def lowerCamelCase__ ( _A , _A ):
'''simple docstring'''
snake_case_ = get_tokens(_A )
snake_case_ = get_tokens(_A )
snake_case_ = collections.Counter(_A ) & collections.Counter(_A )
snake_case_ = sum(common.values() )
if len(_A ) == 0 or len(_A ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
snake_case_ = 1.0 * num_same / len(_A )
snake_case_ = 1.0 * num_same / len(_A )
snake_case_ = (2 * precision * recall) / (precision + recall)
return fa
def lowerCamelCase__ ( _A , _A ):
'''simple docstring'''
snake_case_ = {}
snake_case_ = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case_ = qa["id"]
snake_case_ = [t for t in qa["answers"]["text"] if normalize_answer(_A )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
snake_case_ = [""]
if qid not in preds:
print(f"Missing prediction for {qid}" )
continue
snake_case_ = preds[qid]
# Take max over all gold answers
snake_case_ = max(compute_exact(_A , _A ) for a in gold_answers )
snake_case_ = max(compute_fa(_A , _A ) for a in gold_answers )
return exact_scores, fa_scores
def lowerCamelCase__ ( _A , _A , _A , _A ):
'''simple docstring'''
snake_case_ = {}
for qid, s in scores.items():
snake_case_ = na_probs[qid] > na_prob_thresh
if pred_na:
snake_case_ = float(not qid_to_has_ans[qid] )
else:
snake_case_ = s
return new_scores
def lowerCamelCase__ ( _A , _A , _A=None ):
'''simple docstring'''
if not qid_list:
snake_case_ = len(_A )
return collections.OrderedDict(
[
("exact", 1_00.0 * sum(exact_scores.values() ) / total),
("f1", 1_00.0 * sum(fa_scores.values() ) / total),
("total", total),
] )
else:
snake_case_ = len(_A )
return collections.OrderedDict(
[
("exact", 1_00.0 * sum(exact_scores[k] for k in qid_list ) / total),
("f1", 1_00.0 * sum(fa_scores[k] for k in qid_list ) / total),
("total", total),
] )
def lowerCamelCase__ ( _A , _A , _A ):
'''simple docstring'''
for k in new_eval:
snake_case_ = new_eval[k]
def lowerCamelCase__ ( _A , _A , _A , _A ):
'''simple docstring'''
plt.step(_A , _A , color="b" , alpha=0.2 , where="post" )
plt.fill_between(_A , _A , step="post" , alpha=0.2 , color="b" )
plt.xlabel("Recall" )
plt.ylabel("Precision" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(_A )
plt.savefig(_A )
plt.clf()
def lowerCamelCase__ ( _A , _A , _A , _A , _A=None , _A=None ):
'''simple docstring'''
snake_case_ = sorted(_A , key=lambda _A : na_probs[k] )
snake_case_ = 0.0
snake_case_ = 1.0
snake_case_ = 0.0
snake_case_ = [1.0]
snake_case_ = [0.0]
snake_case_ = 0.0
for i, qid in enumerate(_A ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
snake_case_ = true_pos / float(i + 1 )
snake_case_ = true_pos / float(_A )
if i == len(_A ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(_A )
recalls.append(_A )
if out_image:
plot_pr_curve(_A , _A , _A , _A )
return {"ap": 1_00.0 * avg_prec}
def lowerCamelCase__ ( _A , _A , _A , _A , _A , _A ):
'''simple docstring'''
if out_image_dir and not os.path.exists(_A ):
os.makedirs(_A )
snake_case_ = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
snake_case_ = make_precision_recall_eval(
_A , _A , _A , _A , out_image=os.path.join(_A , "pr_exact.png" ) , title="Precision-Recall curve for Exact Match score" , )
snake_case_ = make_precision_recall_eval(
_A , _A , _A , _A , out_image=os.path.join(_A , "pr_f1.png" ) , title="Precision-Recall curve for F1 score" , )
snake_case_ = {k: float(_A ) for k, v in qid_to_has_ans.items()}
snake_case_ = make_precision_recall_eval(
_A , _A , _A , _A , out_image=os.path.join(_A , "pr_oracle.png" ) , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(_A , _A , "pr_exact" )
merge_eval(_A , _A , "pr_f1" )
merge_eval(_A , _A , "pr_oracle" )
def lowerCamelCase__ ( _A , _A , _A , _A ):
'''simple docstring'''
if not qid_list:
return
snake_case_ = [na_probs[k] for k in qid_list]
snake_case_ = np.ones_like(_A ) / float(len(_A ) )
plt.hist(_A , weights=_A , bins=20 , range=(0.0, 1.0) )
plt.xlabel("Model probability of no-answer" )
plt.ylabel("Proportion of dataset" )
plt.title(f"Histogram of no-answer probability: {name}" )
plt.savefig(os.path.join(_A , f"na_prob_hist_{name}.png" ) )
plt.clf()
def lowerCamelCase__ ( _A , _A , _A , _A ):
'''simple docstring'''
snake_case_ = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
snake_case_ = num_no_ans
snake_case_ = cur_score
snake_case_ = 0.0
snake_case_ = sorted(_A , key=lambda _A : na_probs[k] )
for i, qid in enumerate(_A ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
snake_case_ = scores[qid]
else:
if preds[qid]:
snake_case_ = -1
else:
snake_case_ = 0
cur_score += diff
if cur_score > best_score:
snake_case_ = cur_score
snake_case_ = na_probs[qid]
return 1_00.0 * best_score / len(_A ), best_thresh
def lowerCamelCase__ ( _A , _A , _A , _A , _A , _A ):
'''simple docstring'''
snake_case_ , snake_case_ = find_best_thresh(_A , _A , _A , _A )
snake_case_ , snake_case_ = find_best_thresh(_A , _A , _A , _A )
snake_case_ = best_exact
snake_case_ = exact_thresh
snake_case_ = best_fa
snake_case_ = fa_thresh
def lowerCamelCase__ ( ):
'''simple docstring'''
with open(OPTS.data_file ) as f:
snake_case_ = json.load(_A )
snake_case_ = dataset_json["data"]
with open(OPTS.pred_file ) as f:
snake_case_ = json.load(_A )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
snake_case_ = json.load(_A )
else:
snake_case_ = {k: 0.0 for k in preds}
snake_case_ = make_qid_to_has_ans(_A ) # maps qid to True/False
snake_case_ = [k for k, v in qid_to_has_ans.items() if v]
snake_case_ = [k for k, v in qid_to_has_ans.items() if not v]
snake_case_ , snake_case_ = get_raw_scores(_A , _A )
snake_case_ = apply_no_ans_threshold(_A , _A , _A , OPTS.na_prob_thresh )
snake_case_ = apply_no_ans_threshold(_A , _A , _A , OPTS.na_prob_thresh )
snake_case_ = make_eval_dict(_A , _A )
if has_ans_qids:
snake_case_ = make_eval_dict(_A , _A , qid_list=_A )
merge_eval(_A , _A , "HasAns" )
if no_ans_qids:
snake_case_ = make_eval_dict(_A , _A , qid_list=_A )
merge_eval(_A , _A , "NoAns" )
if OPTS.na_prob_file:
find_all_best_thresh(_A , _A , _A , _A , _A , _A )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(_A , _A , _A , _A , _A , OPTS.out_image_dir )
histogram_na_prob(_A , _A , OPTS.out_image_dir , "hasAns" )
histogram_na_prob(_A , _A , OPTS.out_image_dir , "noAns" )
if OPTS.out_file:
with open(OPTS.out_file , "w" ) as f:
json.dump(_A , _A )
else:
print(json.dumps(_A , indent=2 ) )
if __name__ == "__main__":
lowercase__ : Union[str, Any] = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main()
| 187 | 0 |
import os
import sys
import unittest
__magic_name__ = 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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__magic_name__ = os.path.join(git_repo_path, "src", "transformers")
__magic_name__ = "\n{0} = None\n"
__magic_name__ = "\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n"
__magic_name__ = "\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n"
class lowercase ( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = find_backend(''' _import_structure["models.albert"].append("AlbertTokenizerFast")''' )
self.assertIsNone(_snake_case )
UpperCAmelCase = find_backend(''' if not is_tokenizers_available():''' )
self.assertEqual(_snake_case , '''tokenizers''' )
UpperCAmelCase = find_backend(''' if not is_tensorflow_text_available():''' )
self.assertEqual(_snake_case , '''tensorflow_text''' )
UpperCAmelCase = find_backend(''' if not (is_sentencepiece_available() and is_tokenizers_available()):''' )
self.assertEqual(_snake_case , '''sentencepiece_and_tokenizers''' )
UpperCAmelCase = find_backend(
''' if not (is_sentencepiece_available() and is_tensorflow_text_available()):''' )
self.assertEqual(_snake_case , '''sentencepiece_and_tensorflow_text''' )
UpperCAmelCase = find_backend(
''' if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):''' )
self.assertEqual(_snake_case , '''sentencepiece_and_tokenizers_and_vision''' )
def snake_case_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('''torch''' , _snake_case )
self.assertIn('''tensorflow_text''' , _snake_case )
self.assertIn('''sentencepiece_and_tokenizers''' , _snake_case )
# Likewise, we can't assert on the exact content of a key
self.assertIn('''BertModel''' , objects['''torch'''] )
self.assertIn('''TFBertModel''' , objects['''tf'''] )
self.assertIn('''FlaxBertModel''' , objects['''flax'''] )
self.assertIn('''BertModel''' , objects['''torch'''] )
self.assertIn('''TFBertTokenizer''' , objects['''tensorflow_text'''] )
self.assertIn('''convert_slow_tokenizer''' , objects['''sentencepiece_and_tokenizers'''] )
def snake_case_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = create_dummy_object('''CONSTANT''' , '''\'torch\'''' )
self.assertEqual(_snake_case , '''\nCONSTANT = None\n''' )
UpperCAmelCase = create_dummy_object('''function''' , '''\'torch\'''' )
self.assertEqual(
_snake_case , '''\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n''' )
UpperCAmelCase = '''
class FakeClass(metaclass=DummyObject):
_backends = \'torch\'
def __init__(self, *args, **kwargs):
requires_backends(self, \'torch\')
'''
UpperCAmelCase = create_dummy_object('''FakeClass''' , '''\'torch\'''' )
self.assertEqual(_snake_case , _snake_case )
def snake_case_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase = '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, ["torch"])
class FakeClass(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
'''
UpperCAmelCase = create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']} )
self.assertEqual(dummy_files['''torch'''] , _snake_case )
| 152 |
def _lowerCAmelCase ( A__: list[int] , A__: list[int] ):
'''simple docstring'''
UpperCAmelCase = len(A__ )
print('''The following activities are selected:''' )
# The first activity is always selected
UpperCAmelCase = 0
print(A__ , end=''',''' )
# Consider rest of the activities
for j in range(A__ ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(A__ , end=''',''' )
UpperCAmelCase = j
if __name__ == "__main__":
import doctest
doctest.testmod()
__magic_name__ = [1, 3, 0, 5, 8, 5]
__magic_name__ = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 152 | 1 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def _snake_case ( _snake_case : int , _snake_case : int ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def _snake_case ( _snake_case : int ) -> list[str]:
'''simple docstring'''
_A = []
_A = 11
_A = int('1' + '0' * digit_len )
for num in range(__a , __a ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(__a , __a ):
solutions.append(F'''{num}/{den}''' )
den += 1
num += 1
_A = 10
return solutions
def _snake_case ( _snake_case : int = 2 ) -> int:
'''simple docstring'''
_A = 1.0
for fraction in fraction_list(__a ):
_A = Fraction(__a )
result *= frac.denominator / frac.numerator
return int(__a )
if __name__ == "__main__":
print(solution())
| 315 |
import requests
a__ = '''YOUR API KEY'''
def __UpperCAmelCase ( __a : str ,__a : str = giphy_api_key ) -> list:
"""simple docstring"""
_a : Optional[Any] = '''+'''.join(query.split() )
_a : Union[str, Any] = F"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
_a : List[Any] = requests.get(__a ).json()['''data''']
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print('''\n'''.join(get_gifs('''space ship''')))
| 235 | 0 |
import argparse
import copy
def UpperCAmelCase__ (UpperCamelCase_ ):
"""simple docstring"""
snake_case = {}
with open(UpperCamelCase_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case = []
_list.append([line.split()[1], line.split()[2]] )
snake_case = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
snake_case = []
_list.append([line.split()[0], line.split()[2]] )
snake_case = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
with open(UpperCamelCase_ ) as f:
snake_case = f.read(1 )
snake_case = start_node
snake_case = []
snake_case = start_node
snake_case = 0
while visiting not in first_solution:
snake_case = 1_00_00
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(UpperCamelCase_ ) and k[0] not in first_solution:
snake_case = k[1]
snake_case = k[0]
first_solution.append(UpperCamelCase_ )
snake_case = distance_of_first_solution + int(UpperCamelCase_ )
snake_case = best_node
first_solution.append(UpperCamelCase_ )
snake_case = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
snake_case = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_00_00
)
return first_solution, distance_of_first_solution
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
snake_case = []
for n in solution[1:-1]:
snake_case = solution.index(UpperCamelCase_ )
for kn in solution[1:-1]:
snake_case = solution.index(UpperCamelCase_ )
if n == kn:
continue
snake_case = copy.deepcopy(UpperCamelCase_ )
snake_case = kn
snake_case = n
snake_case = 0
for k in _tmp[:-1]:
snake_case = _tmp[_tmp.index(UpperCamelCase_ ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
snake_case = distance + int(i[1] )
_tmp.append(UpperCamelCase_ )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
snake_case = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda UpperCamelCase_ : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
snake_case = 1
snake_case = first_solution
snake_case = []
snake_case = distance_of_first_solution
snake_case = solution
while count <= iters:
snake_case = find_neighborhood(UpperCamelCase_ ,UpperCamelCase_ )
snake_case = 0
snake_case = neighborhood[index_of_best_solution]
snake_case = len(UpperCamelCase_ ) - 1
snake_case = False
while not found:
snake_case = 0
while i < len(UpperCamelCase_ ):
if best_solution[i] != solution[i]:
snake_case = best_solution[i]
snake_case = solution[i]
break
snake_case = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
snake_case = True
snake_case = best_solution[:-1]
snake_case = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
snake_case = cost
snake_case = solution
else:
snake_case = index_of_best_solution + 1
snake_case = neighborhood[index_of_best_solution]
if len(UpperCamelCase_ ) >= size:
tabu_list.pop(0 )
snake_case = count + 1
return best_solution_ever, best_cost
def UpperCAmelCase__ (UpperCamelCase_=None ):
"""simple docstring"""
snake_case = generate_neighbours(args.File )
snake_case = generate_first_solution(
args.File ,UpperCamelCase_ )
snake_case = tabu_search(
UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,args.Iterations ,args.Size ,)
print(F'''Best solution: {best_sol}, with total distance: {best_cost}.''' )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Union[str, Any] = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 361 |
from __future__ import annotations
import time
_SCREAMING_SNAKE_CASE : List[Any] = list[tuple[int, int]]
_SCREAMING_SNAKE_CASE : Any = [
[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],
]
_SCREAMING_SNAKE_CASE : Any = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class A__ :
"""simple docstring"""
def __init__( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ):
snake_case = pos_x
snake_case = pos_y
snake_case = (pos_y, pos_x)
snake_case = goal_x
snake_case = goal_y
snake_case = parent
class A__ :
"""simple docstring"""
def __init__( self , __snake_case , __snake_case ):
snake_case = Node(start[1] , start[0] , goal[1] , goal[0] , __snake_case )
snake_case = Node(goal[1] , goal[0] , goal[1] , goal[0] , __snake_case )
snake_case = [self.start]
snake_case = False
def a_ ( self ):
while self.node_queue:
snake_case = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
snake_case = True
return self.retrace_path(__snake_case )
snake_case = self.get_successors(__snake_case )
for node in successors:
self.node_queue.append(__snake_case )
if not self.reached:
return [self.start.pos]
return None
def a_ ( self , __snake_case ):
snake_case = []
for action in delta:
snake_case = parent.pos_x + action[1]
snake_case = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__snake_case ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(__snake_case , __snake_case , self.target.pos_y , self.target.pos_x , __snake_case ) )
return successors
def a_ ( self , __snake_case ):
snake_case = node
snake_case = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
snake_case = current_node.parent
path.reverse()
return path
class A__ :
"""simple docstring"""
def __init__( self , __snake_case , __snake_case ):
snake_case = BreadthFirstSearch(__snake_case , __snake_case )
snake_case = BreadthFirstSearch(__snake_case , __snake_case )
snake_case = False
def a_ ( self ):
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
snake_case = self.fwd_bfs.node_queue.pop(0 )
snake_case = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
snake_case = True
return self.retrace_bidirectional_path(
__snake_case , __snake_case )
snake_case = current_bwd_node
snake_case = current_fwd_node
snake_case = {
self.fwd_bfs: self.fwd_bfs.get_successors(__snake_case ),
self.bwd_bfs: self.bwd_bfs.get_successors(__snake_case ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(__snake_case )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def a_ ( self , __snake_case , __snake_case ):
snake_case = self.fwd_bfs.retrace_path(__snake_case )
snake_case = self.bwd_bfs.retrace_path(__snake_case )
bwd_path.pop()
bwd_path.reverse()
snake_case = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
_SCREAMING_SNAKE_CASE : Optional[Any] = (0, 0)
_SCREAMING_SNAKE_CASE : List[Any] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
_SCREAMING_SNAKE_CASE : List[Any] = time.time()
_SCREAMING_SNAKE_CASE : List[Any] = BreadthFirstSearch(init, goal)
_SCREAMING_SNAKE_CASE : List[str] = bfs.search()
_SCREAMING_SNAKE_CASE : int = time.time() - start_bfs_time
print("Unidirectional BFS computation time : ", bfs_time)
_SCREAMING_SNAKE_CASE : Any = time.time()
_SCREAMING_SNAKE_CASE : Union[str, Any] = BidirectionalBreadthFirstSearch(init, goal)
_SCREAMING_SNAKE_CASE : Union[str, Any] = bd_bfs.search()
_SCREAMING_SNAKE_CASE : Tuple = time.time() - start_bd_bfs_time
print("Bidirectional BFS computation time : ", bd_bfs_time)
| 213 | 0 |
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